Atom search optimization and its application to solve a hydrogeologic parameter estimation problem
In recent years, various metaheuristic optimization methods have been proposed in scientific and engineering fields. In this study, a novel physics-inspired metaheuristic optimization algorithm, atom search optimization (ASO), inspired by basic molecular dynamics, is developed to address a diverse s...
Uložené v:
| Vydané v: | Knowledge-based systems Ročník 163; s. 283 - 304 |
|---|---|
| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Amsterdam
Elsevier B.V
01.01.2019
Elsevier Science Ltd |
| Predmet: | |
| ISSN: | 0950-7051, 1872-7409 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | In recent years, various metaheuristic optimization methods have been proposed in scientific and engineering fields. In this study, a novel physics-inspired metaheuristic optimization algorithm, atom search optimization (ASO), inspired by basic molecular dynamics, is developed to address a diverse set of optimization problems. ASO mathematically models and mimics the atomic motion model in nature, where atoms interact through interaction forces resulting from the Lennard-Jones potential and constraint forces resulting from the bond-length potential. The proposed algorithm is simple and easy to implement. ASO is tested on a range of benchmark functions to verify its validity, qualitatively and quantitatively, and then applied to a hydrogeologic parameter estimation problem with success. The results demonstrate that ASO is superior to some classic and newly emerging algorithms in the literature and is a promising solution to real-world engineering problems.
•A novel optimization algorithm called Atom Search Optimization (ASO) is proposed.•ASO is benchmarked on 37 well-known test functions.•The results on test functions show the competitiveness of ASO.•The results on hydrogeologic parameter estimation confirm the performance of ASO. |
|---|---|
| AbstractList | In recent years, various metaheuristic optimization methods have been proposed in scientific and engineering fields. In this study, a novel physics-inspired metaheuristic optimization algorithm, atom search optimization (ASO), inspired by basic molecular dynamics, is developed to address a diverse set of optimization problems. ASO mathematically models and mimics the atomic motion model in nature, where atoms interact through interaction forces resulting from the Lennard-Jones potential and constraint forces resulting from the bond-length potential. The proposed algorithm is simple and easy to implement. ASO is tested on a range of benchmark functions to verify its validity, qualitatively and quantitatively, and then applied to a hydrogeologic parameter estimation problem with success. The results demonstrate that ASO is superior to some classic and newly emerging algorithms in the literature and is a promising solution to real-world engineering problems.
•A novel optimization algorithm called Atom Search Optimization (ASO) is proposed.•ASO is benchmarked on 37 well-known test functions.•The results on test functions show the competitiveness of ASO.•The results on hydrogeologic parameter estimation confirm the performance of ASO. In recent years, various metaheuristic optimization methods have been proposed in scientific and engineering fields. In this study, a novel physics-inspired metaheuristic optimization algorithm, atom search optimization (ASO), inspired by basic molecular dynamics, is developed to address a diverse set of optimization problems. ASO mathematically models and mimics the atomic motion model in nature, where atoms interact through interaction forces resulting from the Lennard-Jones potential and constraint forces resulting from the bond-length potential. The proposed algorithm is simple and easy to implement. ASO is tested on a range of benchmark functions to verify its validity, qualitatively and quantitatively, and then applied to a hydrogeologic parameter estimation problem with success. The results demonstrate that ASO is superior to some classic and newly emerging algorithms in the literature and is a promising solution to real-world engineering problems. |
| Author | Wang, Liying Zhao, Weiguo Zhang, Zhenxing |
| Author_xml | – sequence: 1 givenname: Weiguo surname: Zhao fullname: Zhao, Weiguo email: wgzhao@illinois.edu organization: School of Water Conservancy and Hydropower, Hebei University of Engineering, Handan, Hebei, 056021, China – sequence: 2 givenname: Liying surname: Wang fullname: Wang, Liying email: wangliying@hebeu.edu.cn organization: School of Water Conservancy and Hydropower, Hebei University of Engineering, Handan, Hebei, 056021, China – sequence: 3 givenname: Zhenxing surname: Zhang fullname: Zhang, Zhenxing email: zhang538@illinois.edu organization: Illinois State Water Survey, Prairie Research Institute, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA |
| BookMark | eNqFUEtLAzEQDlLBWv0HHgKet042u8muB6EUXyB40XNIs9M2dbtZk1Sov97U9eRBYWCY4XvMfKdk1LkOCblgMGXAxNVm-ta5sA_THFg1hVQcjsiYVTLPZAH1iIyhLiGTULITchrCBgDynFVjsphFt6UBtTdr6vpot_ZTR-s6qruG2hio7vvWmmEXHQ2u_UCq6XrfeLdC17qVNbTXXm8xoqcYksaA7r1btLg9I8dL3QY8_-kT8np3-zJ_yJ6e7x_ns6fMFAAxk00toMwX3DDOha4By-WCS16lyUAlSlFUhWikYAVKU0udl0teCmmkLmQjcj4hl4Nu8n3fpTvUxu18lyxVzkQJopAMEqoYUMa7EDwuVe_TwX6vGKhDmmqjhjTVIU0FqfiBdv2LZmz8fjN6bdv_yDcDGdP7Hxa9CsZiZ7CxHk1UjbN_C3wB8kmV8Q |
| CitedBy_id | crossref_primary_10_1016_j_cma_2021_114194 crossref_primary_10_1016_j_ijmecsci_2025_110860 crossref_primary_10_1038_s41598_024_67197_1 crossref_primary_10_1007_s00521_022_07980_1 crossref_primary_10_1080_15567036_2022_2055231 crossref_primary_10_1007_s00217_022_04168_8 crossref_primary_10_32604_cmc_2023_034025 crossref_primary_10_1007_s42235_024_00493_8 crossref_primary_10_1016_j_bspc_2022_104031 crossref_primary_10_1016_j_engstruct_2023_116718 crossref_primary_10_1016_j_pce_2023_103415 crossref_primary_10_1371_journal_pone_0267041 crossref_primary_10_3390_app15020910 crossref_primary_10_1109_ACCESS_2023_3336595 crossref_primary_10_1109_ACCESS_2020_2979921 crossref_primary_10_3390_sym17060841 crossref_primary_10_1016_j_icheatmasstransfer_2020_104822 crossref_primary_10_1016_j_engappai_2023_107101 crossref_primary_10_1016_j_measen_2022_100519 crossref_primary_10_3389_fenrg_2023_1173739 crossref_primary_10_1016_j_egyr_2021_11_010 crossref_primary_10_1080_09540091_2020_1741515 crossref_primary_10_3390_en12101884 crossref_primary_10_1016_j_knosys_2022_108320 crossref_primary_10_1109_ACCESS_2022_3143802 crossref_primary_10_1002_er_8399 crossref_primary_10_3390_automation6020013 crossref_primary_10_32604_cmes_2025_058473 crossref_primary_10_1007_s13042_024_02462_3 crossref_primary_10_1016_j_asoc_2021_107282 crossref_primary_10_1007_s00521_022_07203_7 crossref_primary_10_1016_j_renene_2023_119528 crossref_primary_10_1016_j_advengsoft_2023_103517 crossref_primary_10_1016_j_mtcomm_2024_109394 crossref_primary_10_1007_s42417_022_00538_4 crossref_primary_10_1038_s41598_024_84632_5 crossref_primary_10_1016_j_cma_2022_115223 crossref_primary_10_1109_ACCESS_2025_3562367 crossref_primary_10_1088_1361_6501_acd713 crossref_primary_10_1088_1757_899X_981_4_042067 crossref_primary_10_1038_s41598_024_73991_8 crossref_primary_10_1016_j_eswa_2022_119211 crossref_primary_10_1016_j_engfailanal_2021_105714 crossref_primary_10_1109_ACCESS_2023_3311271 crossref_primary_10_3390_math10111929 crossref_primary_10_1016_j_eswa_2023_120602 crossref_primary_10_1080_19648189_2024_2319868 crossref_primary_10_3390_math11204224 crossref_primary_10_1038_s41598_023_38252_0 crossref_primary_10_1016_j_knosys_2022_110206 crossref_primary_10_1016_j_procs_2021_08_072 crossref_primary_10_1016_j_advengsoft_2022_103399 crossref_primary_10_1049_rpg2_13004 crossref_primary_10_3390_biomimetics10050282 crossref_primary_10_1016_j_conbuildmat_2025_141883 crossref_primary_10_3390_electronics12041058 crossref_primary_10_1109_ACCESS_2021_3133286 crossref_primary_10_1007_s13042_023_01788_8 crossref_primary_10_1007_s42235_023_00387_1 crossref_primary_10_1016_j_asoc_2021_107140 crossref_primary_10_1109_ACCESS_2025_3552312 crossref_primary_10_1016_j_apm_2024_04_057 crossref_primary_10_1007_s42235_024_00505_7 crossref_primary_10_1016_j_engappai_2023_106121 crossref_primary_10_3390_biomimetics8020191 crossref_primary_10_3390_diagnostics11020315 crossref_primary_10_1007_s00202_024_02822_w crossref_primary_10_1007_s00500_021_05654_z crossref_primary_10_1007_s11227_024_06279_3 crossref_primary_10_14710_ijred_0_37482 crossref_primary_10_1016_j_eswa_2025_127733 crossref_primary_10_4218_etrij_2022_0324 crossref_primary_10_1007_s13369_020_04486_7 crossref_primary_10_3390_biomimetics9040205 crossref_primary_10_3390_w14193035 crossref_primary_10_1016_j_cie_2021_107904 crossref_primary_10_1016_j_est_2023_109183 crossref_primary_10_1007_s00521_020_05073_5 crossref_primary_10_3390_math10132329 crossref_primary_10_7717_peerj_cs_2722 crossref_primary_10_1016_j_cma_2023_115878 crossref_primary_10_1109_JSYST_2023_3248658 crossref_primary_10_1007_s11047_020_09826_y crossref_primary_10_1038_s41598_023_35863_5 crossref_primary_10_1016_j_csi_2025_103977 crossref_primary_10_1016_j_iswa_2025_200521 crossref_primary_10_1007_s11831_023_09990_1 crossref_primary_10_1007_s42835_020_00644_x crossref_primary_10_1016_j_engappai_2022_105082 crossref_primary_10_3390_en15196966 crossref_primary_10_1002_ese3_1182 crossref_primary_10_1007_s10586_024_04927_0 crossref_primary_10_1007_s11831_025_10281_0 crossref_primary_10_32604_cmes_2022_019198 crossref_primary_10_1007_s00366_021_01451_2 crossref_primary_10_1007_s12597_023_00682_9 crossref_primary_10_1016_j_bspc_2022_104359 crossref_primary_10_1007_s00500_021_06138_w crossref_primary_10_1007_s11831_023_09902_3 crossref_primary_10_1016_j_asoc_2021_108053 crossref_primary_10_3390_su14063267 crossref_primary_10_1007_s12046_022_01962_8 crossref_primary_10_1016_j_eswa_2025_126532 crossref_primary_10_1007_s12652_021_03008_z crossref_primary_10_1007_s12065_023_00877_5 crossref_primary_10_1007_s13198_024_02605_3 crossref_primary_10_1038_s41598_025_99207_1 crossref_primary_10_1007_s11227_025_07052_w crossref_primary_10_1016_j_engappai_2019_06_017 crossref_primary_10_1155_2022_4629178 crossref_primary_10_3390_math9131532 crossref_primary_10_1016_j_tsep_2024_102862 crossref_primary_10_1109_ACCESS_2021_3068223 crossref_primary_10_1002_cpe_6918 crossref_primary_10_1016_j_est_2020_102219 crossref_primary_10_1088_1402_4896_add8c7 crossref_primary_10_1007_s00366_020_01248_9 crossref_primary_10_3389_fphy_2024_1301035 crossref_primary_10_1007_s13369_025_10482_6 crossref_primary_10_1109_ACCESS_2020_3030874 crossref_primary_10_1007_s00521_024_10905_9 crossref_primary_10_1007_s13369_025_10138_5 crossref_primary_10_1155_2023_8685976 crossref_primary_10_1016_j_knosys_2023_110940 crossref_primary_10_3390_en16114362 crossref_primary_10_1140_epjs_s11734_025_01703_y crossref_primary_10_1155_2020_6657181 crossref_primary_10_1155_2024_5806437 crossref_primary_10_3389_fnhum_2022_973959 crossref_primary_10_1007_s12652_021_02905_7 crossref_primary_10_1007_s13369_021_05677_6 crossref_primary_10_1007_s00521_020_04701_4 crossref_primary_10_1016_j_energy_2022_123587 crossref_primary_10_1016_j_aeue_2019_152854 crossref_primary_10_1016_j_asoc_2024_111539 crossref_primary_10_1007_s10115_022_01825_y crossref_primary_10_3390_sym17091410 crossref_primary_10_1007_s11277_022_09647_5 crossref_primary_10_1016_j_asoc_2022_108574 crossref_primary_10_1016_j_heliyon_2023_e14467 crossref_primary_10_1007_s00521_019_04452_x crossref_primary_10_28948_ngumuh_1570577 crossref_primary_10_3390_math10101626 crossref_primary_10_1007_s00500_023_08925_z crossref_primary_10_1080_10168664_2022_2129121 crossref_primary_10_1007_s00521_025_11228_z crossref_primary_10_1007_s10973_022_11516_z crossref_primary_10_1093_jcde_qwac113 crossref_primary_10_1007_s10489_020_01893_z crossref_primary_10_3390_math9192383 crossref_primary_10_1016_j_asoc_2023_110837 crossref_primary_10_1016_j_swevo_2020_100821 crossref_primary_10_3389_fenrg_2022_1030034 crossref_primary_10_1007_s11831_025_10249_0 crossref_primary_10_1109_ACCESS_2023_3343619 crossref_primary_10_1186_s43067_024_00144_2 crossref_primary_10_1007_s00500_020_05227_6 crossref_primary_10_1016_j_jhydrol_2021_126211 crossref_primary_10_32604_EE_2022_015910 crossref_primary_10_1007_s12530_022_09425_5 crossref_primary_10_1016_j_egyr_2022_10_342 crossref_primary_10_1371_journal_pone_0290117 crossref_primary_10_1016_j_heliyon_2023_e23434 crossref_primary_10_1016_j_jclepro_2019_119301 crossref_primary_10_3390_math9182230 crossref_primary_10_1007_s10586_025_05273_5 crossref_primary_10_1016_j_knosys_2022_108281 crossref_primary_10_1016_j_eswa_2025_127660 crossref_primary_10_3846_jcem_2023_20399 crossref_primary_10_1080_0952813X_2022_2084566 crossref_primary_10_1016_j_cma_2022_114616 crossref_primary_10_3390_math13152499 crossref_primary_10_3390_bioengineering10040475 crossref_primary_10_1007_s12652_021_02892_9 crossref_primary_10_3390_app13020906 crossref_primary_10_1007_s13369_020_05228_5 crossref_primary_10_1016_j_dajour_2023_100182 crossref_primary_10_1038_s41598_024_54910_3 crossref_primary_10_3390_biomimetics8020243 crossref_primary_10_3390_math11092018 crossref_primary_10_1080_15567036_2023_2244909 crossref_primary_10_3390_w17081221 crossref_primary_10_1007_s11227_023_05790_3 crossref_primary_10_1080_0305215X_2022_2127698 crossref_primary_10_1007_s13721_024_00489_8 crossref_primary_10_3390_rs14051136 crossref_primary_10_1002_suco_202400163 crossref_primary_10_1007_s10462_024_10981_2 crossref_primary_10_1016_j_engappai_2024_107906 crossref_primary_10_1007_s11831_020_09456_8 crossref_primary_10_1088_1402_4896_adb706 crossref_primary_10_3390_en13195097 crossref_primary_10_1016_j_jocs_2022_101636 crossref_primary_10_1016_j_enconman_2022_116346 crossref_primary_10_1371_journal_pone_0291788 crossref_primary_10_1108_EC_07_2024_0675 crossref_primary_10_1016_j_compbiomed_2022_106404 crossref_primary_10_1007_s12530_025_09715_8 crossref_primary_10_1038_s41598_025_91203_9 crossref_primary_10_1016_j_procs_2025_04_124 crossref_primary_10_1007_s10586_024_05027_9 crossref_primary_10_1007_s11760_020_01814_0 crossref_primary_10_1038_s41598_023_37824_4 crossref_primary_10_1108_EC_10_2021_0612 crossref_primary_10_3390_math12152364 crossref_primary_10_1016_j_apm_2023_05_023 crossref_primary_10_3390_biomimetics8030310 crossref_primary_10_1007_s11831_023_09923_y crossref_primary_10_1371_journal_pone_0287136 crossref_primary_10_1002_jnm_2845 crossref_primary_10_1109_ACCESS_2022_3151641 crossref_primary_10_1186_s40537_024_00902_z crossref_primary_10_1371_journal_pone_0233110 crossref_primary_10_3390_app10072490 crossref_primary_10_1016_j_measen_2023_101007 crossref_primary_10_1007_s40435_022_01094_1 crossref_primary_10_1016_j_energy_2025_137696 crossref_primary_10_1016_j_eswa_2023_122200 crossref_primary_10_3233_JIFS_221348 crossref_primary_10_1007_s11227_023_05618_0 crossref_primary_10_1007_s10489_022_03265_1 crossref_primary_10_1038_s41598_024_61434_3 crossref_primary_10_1007_s13369_019_04285_9 crossref_primary_10_1016_j_conbuildmat_2025_141383 crossref_primary_10_1109_ACCESS_2022_3197745 crossref_primary_10_1007_s10462_025_11360_1 crossref_primary_10_1007_s00521_022_07391_2 crossref_primary_10_1038_s41598_024_63908_w crossref_primary_10_1016_j_compag_2024_109167 crossref_primary_10_1016_j_jestch_2023_101408 crossref_primary_10_1007_s00366_020_00994_0 crossref_primary_10_1016_j_renene_2023_118994 crossref_primary_10_1049_ipr2_12019 crossref_primary_10_1007_s13198_021_01575_0 crossref_primary_10_1371_journal_pone_0286060 crossref_primary_10_1007_s40747_021_00402_0 crossref_primary_10_1016_j_solener_2024_113189 crossref_primary_10_1038_s41598_025_88080_7 crossref_primary_10_1016_j_envsoft_2021_105237 crossref_primary_10_1007_s10462_024_11023_7 crossref_primary_10_3390_axioms13060361 crossref_primary_10_3390_su15042884 crossref_primary_10_1016_j_matcom_2023_04_020 crossref_primary_10_1016_j_eswa_2023_121218 crossref_primary_10_1016_j_isatra_2025_07_023 crossref_primary_10_1016_j_asoc_2020_106761 crossref_primary_10_1049_rpg2_12359 crossref_primary_10_1016_j_engappai_2022_105202 crossref_primary_10_1038_s41598_024_60821_0 crossref_primary_10_1007_s42235_022_00316_8 crossref_primary_10_1080_02286203_2023_2287968 crossref_primary_10_1186_s40537_025_01129_2 crossref_primary_10_3390_math13050717 crossref_primary_10_1016_j_bspc_2025_108207 crossref_primary_10_1016_j_engappai_2023_106959 crossref_primary_10_1007_s11831_023_10030_1 crossref_primary_10_1007_s10586_024_04950_1 crossref_primary_10_32604_cmc_2022_031909 crossref_primary_10_1016_j_amc_2020_125535 crossref_primary_10_1016_j_bspc_2022_104534 crossref_primary_10_1007_s00034_025_02997_y crossref_primary_10_1007_s11831_024_10217_0 crossref_primary_10_1007_s42235_022_00207_y crossref_primary_10_3390_fractalfract7020119 crossref_primary_10_1007_s00500_023_07928_0 crossref_primary_10_1007_s40815_024_01841_w crossref_primary_10_1002_dac_4341 crossref_primary_10_1016_j_knosys_2025_114273 crossref_primary_10_1007_s00521_024_10694_1 crossref_primary_10_1061__ASCE_IR_1943_4774_0001690 crossref_primary_10_1038_s41598_025_04705_x crossref_primary_10_32604_cmc_2023_035911 crossref_primary_10_1016_j_ress_2025_111610 crossref_primary_10_1109_ACCESS_2019_2958689 crossref_primary_10_1007_s11831_022_09800_0 crossref_primary_10_1038_s41598_025_11861_7 crossref_primary_10_3390_rs15245653 crossref_primary_10_1016_j_compbiomed_2022_106239 crossref_primary_10_1007_s10462_025_11269_9 crossref_primary_10_1155_2022_2721490 crossref_primary_10_1007_s12065_019_00212_x crossref_primary_10_1080_15435075_2024_2449155 crossref_primary_10_1016_j_eswa_2020_113917 crossref_primary_10_1016_j_eswa_2023_120186 crossref_primary_10_1016_j_cma_2022_114901 crossref_primary_10_3390_sym13122388 crossref_primary_10_3390_biomimetics10010003 crossref_primary_10_1016_j_jastp_2024_106360 crossref_primary_10_1038_s41598_024_70731_w crossref_primary_10_3390_a17080342 crossref_primary_10_1016_j_jhydrol_2024_132596 crossref_primary_10_1038_s41598_022_16498_4 crossref_primary_10_1007_s12065_019_00269_8 crossref_primary_10_1007_s11042_021_11839_3 crossref_primary_10_1007_s00521_025_11379_z crossref_primary_10_1016_j_jclepro_2020_121817 crossref_primary_10_1002_2050_7038_12712 crossref_primary_10_1016_j_tust_2023_105235 crossref_primary_10_1016_j_knosys_2024_111725 crossref_primary_10_1007_s00521_020_05409_1 crossref_primary_10_1007_s11042_024_19039_5 crossref_primary_10_3390_biomimetics10080482 crossref_primary_10_1007_s42235_022_00330_w crossref_primary_10_1038_s41598_024_78761_0 crossref_primary_10_1007_s10115_025_02422_5 crossref_primary_10_3390_su142316205 crossref_primary_10_1007_s11069_021_05160_3 crossref_primary_10_1016_j_asoc_2020_106320 crossref_primary_10_1063_5_0108340 crossref_primary_10_1016_j_knosys_2021_107796 crossref_primary_10_1016_j_prime_2024_100536 crossref_primary_10_1007_s13042_024_02185_5 crossref_primary_10_1007_s13369_021_06513_7 crossref_primary_10_1007_s10586_024_05011_3 crossref_primary_10_1007_s13042_025_02588_y crossref_primary_10_1109_ACCESS_2025_3547537 crossref_primary_10_1016_j_jhydrol_2025_132998 crossref_primary_10_3390_su15119017 crossref_primary_10_1016_j_enconman_2020_113279 crossref_primary_10_1007_s11042_022_13171_w crossref_primary_10_1038_s41598_023_41024_5 crossref_primary_10_1007_s10825_021_01796_3 crossref_primary_10_1016_j_oceaneng_2024_117806 crossref_primary_10_3233_JIFS_230459 crossref_primary_10_1016_j_jocs_2022_101766 crossref_primary_10_1016_j_engappai_2020_103731 crossref_primary_10_1109_ACCESS_2020_3021212 crossref_primary_10_1016_j_apm_2022_11_016 crossref_primary_10_1007_s10489_024_05930_z crossref_primary_10_1155_2020_4568906 crossref_primary_10_1007_s11831_024_10202_7 crossref_primary_10_1016_j_asoc_2023_110113 crossref_primary_10_1016_j_apenergy_2024_123437 crossref_primary_10_1016_j_engappai_2024_109202 crossref_primary_10_1007_s11042_022_13093_7 crossref_primary_10_1016_j_jenvman_2023_119807 crossref_primary_10_1109_ACCESS_2025_3567303 crossref_primary_10_1016_j_isatra_2023_02_025 crossref_primary_10_1007_s10586_025_05328_7 crossref_primary_10_1016_j_egyr_2022_10_386 crossref_primary_10_1016_j_cma_2023_116062 crossref_primary_10_3390_fuels6020030 crossref_primary_10_1016_j_matcom_2022_04_031 crossref_primary_10_1007_s12065_022_00762_7 crossref_primary_10_33889_IJMEMS_2023_8_2_016 crossref_primary_10_1007_s10462_024_10747_w crossref_primary_10_14710_ijred_2021_37482 crossref_primary_10_1109_ACCESS_2020_2996611 crossref_primary_10_1016_j_engappai_2022_104763 crossref_primary_10_1007_s40031_022_00731_9 crossref_primary_10_1049_rpg2_12746 crossref_primary_10_1007_s10489_020_01947_2 crossref_primary_10_1016_j_eswa_2024_124765 crossref_primary_10_1002_ima_23001 crossref_primary_10_1016_j_eswa_2019_113134 crossref_primary_10_1038_s41598_022_18993_0 crossref_primary_10_1016_j_apm_2020_07_052 crossref_primary_10_1007_s11276_020_02263_w crossref_primary_10_1155_2019_6759106 crossref_primary_10_1007_s13369_023_08335_1 crossref_primary_10_7717_peerj_cs_2263 crossref_primary_10_1016_j_asoc_2019_105974 crossref_primary_10_1016_j_conengprac_2024_106061 crossref_primary_10_1109_JIOT_2022_3228736 crossref_primary_10_1007_s12530_023_09552_7 crossref_primary_10_3390_biomimetics10070471 crossref_primary_10_1007_s00500_019_04631_x crossref_primary_10_1016_j_renene_2022_12_067 crossref_primary_10_1109_ACCESS_2021_3120749 crossref_primary_10_1109_ACCESS_2023_3311626 crossref_primary_10_1007_s10462_025_11279_7 crossref_primary_10_1038_s41598_025_06380_4 crossref_primary_10_3390_math12142250 crossref_primary_10_1186_s40537_024_00895_9 crossref_primary_10_1007_s11831_022_09801_z crossref_primary_10_1038_s41598_025_93370_1 crossref_primary_10_1007_s00202_024_02653_9 crossref_primary_10_1016_j_cmpb_2021_106579 crossref_primary_10_3390_pr11051346 crossref_primary_10_1016_j_engappai_2022_104860 crossref_primary_10_1155_2022_3343505 crossref_primary_10_1002_oca_2767 crossref_primary_10_1007_s00521_020_04799_6 crossref_primary_10_3390_machines9120341 crossref_primary_10_1007_s11831_023_09928_7 crossref_primary_10_1016_j_engappai_2024_109879 crossref_primary_10_1155_2024_4590764 crossref_primary_10_1109_ACCESS_2025_3574730 crossref_primary_10_1007_s11227_023_05579_4 crossref_primary_10_1016_j_sysarc_2023_102871 crossref_primary_10_1109_ACCESS_2022_3228782 crossref_primary_10_1007_s10462_025_11118_9 crossref_primary_10_1016_j_advengsoft_2022_103405 crossref_primary_10_1007_s10462_022_10333_y crossref_primary_10_1016_j_chaos_2024_115636 crossref_primary_10_1016_j_jestch_2020_08_011 crossref_primary_10_1108_EC_03_2020_0137 crossref_primary_10_1016_j_compbiomed_2025_110500 crossref_primary_10_1016_j_bbe_2020_10_001 crossref_primary_10_3390_drones7070427 crossref_primary_10_1016_j_cma_2023_116664 crossref_primary_10_1093_jcde_qwae080 crossref_primary_10_1016_j_asoc_2024_112295 crossref_primary_10_32604_cmes_2024_055860 crossref_primary_10_1007_s11227_024_06916_x crossref_primary_10_1186_s40537_023_00864_8 crossref_primary_10_1109_TEVC_2023_3346672 crossref_primary_10_1016_j_jestch_2020_08_001 crossref_primary_10_1186_s40537_025_01274_8 crossref_primary_10_1016_j_dt_2024_04_006 crossref_primary_10_1515_mt_2024_0097 crossref_primary_10_1002_eng2_12773 crossref_primary_10_1038_s41598_024_75387_0 crossref_primary_10_1007_s10115_025_02498_z crossref_primary_10_3390_batteries11070272 crossref_primary_10_3390_math13040668 crossref_primary_10_54287_gujsa_1667182 crossref_primary_10_1016_j_ins_2020_06_037 crossref_primary_10_3390_math11051231 crossref_primary_10_1371_journal_pone_0295579 crossref_primary_10_1111_exsy_13715 crossref_primary_10_26599_Jic_2025_9180087 crossref_primary_10_1007_s10115_025_02389_3 crossref_primary_10_1016_j_asoc_2025_113527 crossref_primary_10_1109_ACCESS_2020_3027654 crossref_primary_10_3390_en16041782 crossref_primary_10_1016_j_jwpe_2023_103957 crossref_primary_10_3390_math13040675 crossref_primary_10_1007_s12065_020_00506_5 crossref_primary_10_3390_buildings13051167 crossref_primary_10_1016_j_cma_2023_116200 crossref_primary_10_1002_cpe_6425 crossref_primary_10_1016_j_cma_2023_116446 crossref_primary_10_1007_s12008_024_02136_y crossref_primary_10_3390_buildings14123753 crossref_primary_10_4018_IJSKD_330150 crossref_primary_10_1007_s00521_022_07705_4 crossref_primary_10_1007_s13198_025_02721_8 crossref_primary_10_1016_j_eswa_2021_115079 crossref_primary_10_1016_j_jobe_2025_113515 crossref_primary_10_1007_s10489_021_02629_3 crossref_primary_10_1016_j_aei_2024_102464 crossref_primary_10_3389_fenrg_2022_902486 crossref_primary_10_1016_j_asoc_2021_107421 crossref_primary_10_1038_s41598_022_12030_w crossref_primary_10_1007_s00521_021_06260_8 crossref_primary_10_1007_s10586_025_05170_x crossref_primary_10_1002_cpe_6732 crossref_primary_10_3390_bioengineering10070825 crossref_primary_10_1016_j_seta_2021_101824 crossref_primary_10_1007_s11831_023_09897_x crossref_primary_10_1007_s11063_022_10797_7 crossref_primary_10_1016_j_rineng_2025_104215 crossref_primary_10_1007_s11277_021_08946_7 crossref_primary_10_1002_oca_2823 crossref_primary_10_3389_fgene_2021_793629 crossref_primary_10_1016_j_enconman_2020_113692 crossref_primary_10_1080_15325008_2022_2049648 crossref_primary_10_1007_s00521_020_05333_4 crossref_primary_10_1016_j_eswa_2024_123362 crossref_primary_10_1016_j_epsr_2023_109513 crossref_primary_10_3390_math12182870 crossref_primary_10_1007_s00521_024_09879_5 crossref_primary_10_1109_ACCESS_2022_3143035 crossref_primary_10_3390_math11051273 crossref_primary_10_1007_s12065_020_00450_4 crossref_primary_10_1109_ACCESS_2022_3157400 crossref_primary_10_1007_s13042_022_01524_8 crossref_primary_10_1680_jtran_24_00050 crossref_primary_10_3389_fmars_2023_1126556 crossref_primary_10_1007_s12065_021_00615_9 crossref_primary_10_3390_sym14112282 crossref_primary_10_1155_2021_6622655 crossref_primary_10_1109_ACCESS_2019_2923557 crossref_primary_10_1007_s11831_022_09876_8 crossref_primary_10_3390_electronics12194113 crossref_primary_10_1007_s13369_023_07984_6 crossref_primary_10_1016_j_eswa_2023_119941 crossref_primary_10_1007_s41939_025_00800_8 crossref_primary_10_1038_s41598_023_48462_1 crossref_primary_10_1016_j_knosys_2023_111081 crossref_primary_10_3390_e23121637 crossref_primary_10_1016_j_cma_2023_116238 crossref_primary_10_1109_ACCESS_2022_3208700 crossref_primary_10_46904_eea_24_72_1_1108007 |
| Cites_doi | 10.1002/tee.20628 10.1016/j.neucom.2013.04.052 10.1016/j.physleta.2016.08.027 10.1016/j.jclepro.2018.02.004 10.7763/IJMLC.2012.V2.114 10.1108/02644401211235834 10.1016/j.ins.2010.07.015 10.1109/4235.585893 10.1016/j.knosys.2016.07.005 10.1016/j.compstruc.2012.07.010 10.1504/IJBIC.2010.032124 10.1016/j.asoc.2015.03.035 10.1016/j.ins.2012.06.032 10.1016/j.engappai.2016.04.004 10.1016/j.eswa.2015.04.055 10.1103/PhysRev.136.A405 10.1016/j.ins.2012.04.027 10.1109/TEVC.2016.2634625 10.1016/0021-9991(77)90098-5 10.1061/(ASCE)0733-9437(2003)129:5(348) 10.1016/j.swevo.2015.05.002 10.1029/TR036i001p00095 10.1109/MCS.2002.1004010 10.1109/TCYB.2016.2638902 10.1109/TEVC.2005.843751 10.1063/1.465188 10.1016/j.compstruc.2016.01.008 10.1063/1.4822471 10.1177/003754970107600201 10.1016/j.ecoinf.2006.07.003 10.1016/j.epsr.2016.09.025 10.1016/j.advengsoft.2013.03.004 10.1126/science.220.4598.671 10.1016/j.ins.2014.08.053 10.1016/j.knosys.2011.07.001 10.1016/j.advengsoft.2013.03.001 10.1016/j.ins.2013.01.020 10.1016/j.ins.2015.10.001 10.1016/j.cogsys.2018.07.022 10.1002/hyp.6274 10.1016/j.swevo.2018.02.013 10.1016/j.biosystems.2006.04.005 10.1109/3477.484436 10.1109/NABIC.2009.5393690 10.1109/TAP.2013.2238654 10.1002/oca.2334 10.1016/j.advengsoft.2017.07.002 10.1016/j.cor.2014.10.008 10.1016/j.advengsoft.2017.01.004 10.1504/IJBIC.2009.022775 10.1063/1.1730376 10.1103/PhysRevLett.89.150201 10.1023/A:1022452626305 10.1016/j.ins.2015.06.044 10.1016/j.camwa.2010.07.049 10.1111/j.1745-6584.1998.tb02203.x 10.1109/59.801925 10.1016/j.asoc.2015.03.003 10.1016/j.ins.2016.03.025 10.1016/j.ins.2011.08.006 10.1016/j.cnsns.2012.05.010 10.1007/s00707-009-0270-4 10.1023/A:1015059928466 10.1016/j.fluid.2016.07.008 10.1016/j.eswa.2013.05.041 10.1016/j.ins.2009.03.004 10.1007/s00500-012-0921-6 10.1007/s13201-015-0374-z 10.1016/j.advengsoft.2017.03.014 10.1016/j.advengsoft.2016.01.008 10.1186/1748-7188-5-32 10.1016/j.amc.2013.02.017 10.1109/TEVC.2008.919004 10.1109/MAP.2011.5773566 10.1016/0167-8191(88)90098-1 10.1016/j.cnsns.2013.08.027 10.1016/j.knosys.2016.01.009 10.1016/j.jnoncrysol.2015.10.031 10.1109/ICNN.1995.488968 10.1016/j.knosys.2015.12.022 |
| ContentType | Journal Article |
| Copyright | 2018 Elsevier B.V. Copyright Elsevier Science Ltd. Jan 1, 2019 |
| Copyright_xml | – notice: 2018 Elsevier B.V. – notice: Copyright Elsevier Science Ltd. Jan 1, 2019 |
| DBID | AAYXX CITATION 7SC 8FD E3H F2A JQ2 L7M L~C L~D |
| DOI | 10.1016/j.knosys.2018.08.030 |
| DatabaseName | CrossRef Computer and Information Systems Abstracts Technology Research Database Library & Information Sciences Abstracts (LISA) Library & Information Science Abstracts (LISA) ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional |
| DatabaseTitle | CrossRef Technology Research Database Computer and Information Systems Abstracts – Academic Library and Information Science Abstracts (LISA) ProQuest Computer Science Collection Computer and Information Systems Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | Technology Research Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science Engineering Physics |
| EISSN | 1872-7409 |
| EndPage | 304 |
| ExternalDocumentID | 10_1016_j_knosys_2018_08_030 S0950705118304271 |
| GroupedDBID | --K --M .DC .~1 0R~ 1B1 1~. 1~5 4.4 457 4G. 5VS 7-5 71M 77K 8P~ 9JN AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAXUO AAYFN ABAOU ABBOA ABIVO ABJNI ABMAC ABYKQ ACAZW ACDAQ ACGFS ACRLP ACZNC ADBBV ADEZE ADGUI ADTZH AEBSH AECPX AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD ARUGR AXJTR BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 FDB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ IHE J1W JJJVA KOM LG9 LY7 M41 MHUIS MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. PQQKQ Q38 RIG ROL RPZ SDF SDG SDP SES SPC SPCBC SST SSV SSW SSZ T5K WH7 XPP ZMT ~02 ~G- 29L 77I 9DU AAQXK AATTM AAXKI AAYWO AAYXX ABDPE ABWVN ABXDB ACLOT ACNNM ACRPL ACVFH ADCNI ADJOM ADMUD ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP ASPBG AVWKF AZFZN CITATION EFKBS FEDTE FGOYB G-2 HLZ HVGLF HZ~ R2- SBC SET SEW UHS WUQ ~HD 7SC 8FD E3H F2A JQ2 L7M L~C L~D |
| ID | FETCH-LOGICAL-c400t-7d96052b3c1336a90e5fb3738336c086564846d7614e7c97a25f3567c7a47d623 |
| ISICitedReferencesCount | 540 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000454468200023&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0950-7051 |
| IngestDate | Fri Nov 14 19:18:17 EST 2025 Sat Nov 29 06:41:33 EST 2025 Tue Nov 18 22:38:35 EST 2025 Fri Feb 23 02:18:39 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Benchmark functions Optimization algorithm Global optimization Metaheuristic Parameter estimation Heuristic algorithm Atom search optimization |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c400t-7d96052b3c1336a90e5fb3738336c086564846d7614e7c97a25f3567c7a47d623 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| PQID | 2165064710 |
| PQPubID | 2035257 |
| PageCount | 22 |
| ParticipantIDs | proquest_journals_2165064710 crossref_primary_10_1016_j_knosys_2018_08_030 crossref_citationtrail_10_1016_j_knosys_2018_08_030 elsevier_sciencedirect_doi_10_1016_j_knosys_2018_08_030 |
| PublicationCentury | 2000 |
| PublicationDate | 2019-01-01 2019-01-00 20190101 |
| PublicationDateYYYYMMDD | 2019-01-01 |
| PublicationDate_xml | – month: 01 year: 2019 text: 2019-01-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationPlace | Amsterdam |
| PublicationPlace_xml | – name: Amsterdam |
| PublicationTitle | Knowledge-based systems |
| PublicationYear | 2019 |
| Publisher | Elsevier B.V Elsevier Science Ltd |
| Publisher_xml | – name: Elsevier B.V – name: Elsevier Science Ltd |
| References | Li, Sang, Han, Wang, Gao (b3) 2018; 181 Rapaport, Blumberg, McKay, Christian (b82) 1996; 10 F.F. Moghaddam, R.F. Moghaddam, M. Cheriet, Curved space optimization: a random search based on general relativity theory, 2012. arXiv preprint Kaveh, Talatahari (b39) 2010; 213 Wang, Zhao, Tian, Pan (b93) 2018; 52 Beni, Wang (b53) 1993 Kaveh, Dadras (b47) 2017; 110 Pishkenari, Mohagheghian, Rasouli (b89) 2016; 380 Askarzadeh (b60) 2014; 19 Tuckerman, Berne, Martyna, Klein (b88) 1993; 99 Goldstein, Poole, Safko (b81) 2001 Kaveh, Bakhshpoori (b32) 2016; 167 Oftadeh, Mahjoob, Shariatpanahi (b66) 2010; 60 Moscato, Mendes, Berretta (b19) 2007; 88 Mirjalili, Lewis (b95) 2016; 95 Shah-Hosseini (b38) 2009; 1 Kaveh, Farhoudi (b65) 2013; 59 Hsiao, Chuang, Jiang, Chien (b33) 2005 Rashedi, Nezamabadi-Pour, Saryazdi (b28) 2009; 179 Rahman (b80) 1964; 136 Birbil, Fang (b29) 2003; 25 Lennard-Jones (b86) 1924; 106 Mohamed, Mohamed, El-Gaafary, Hemeida (b64) 2017; 142 Yang, Hossein Gandomi (b20) 2012; 29 Patel, Savsani (b49) 2015; 324 Passino (b12) 2002; 22 W.F. Sacco, C.R.E. De Oliveira, A new stochastic optimization algorithm based on a particle collision metaheuristic, in: Proceedings of 6th WCSMO, 2005. . Alba, Dorronsoro (b71) 2005; 9 Kirkpatrick, Gelatt, Vecchi (b1) 1983; 220 Samuel, Jha (b102) 2003; 129 Mirjalili, Hashim (b40) 2012; 2 Gandomi, Alavi (b57) 2012; 17 Lynn, Suganthan (b72) 2015; 24 Chuang, Jiang (b37) 2007 Pan (b21) 2012; 26 Wolpert, Macready (b75) 1997; 1 Holland (b14) 1975 J.J. Liang, B.Y. Qu, P.N. Suganthan, Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore, 2013. Ryckaert, Ciccotti, Berendsen (b87) 1977; 23 Stone (b85) 1996 Mirjalili, Gandomi, Mirjalili, Saremi, Faris, Mirjalili (b56) 2017; 114 Jain, Singh, Rani (b70) 2018 Zheng, Liu, Zhou, Liang, Wang (b41) 2010; 5 Geem, Kim, Loganathan (b10) 2001; 76 Barker (b76) 1870 Alder, Wainwright (b79) 1959; 31 Gong, Sun, Miao (b15) 2018; 22 Uymaz, Tezel, Yel (b23) 2015; 31 Duman, Uysal, Alkaya (b67) 2012; 217 Bayraktar, Komurcu, Bossard, Werner (b96) 2013; 61 Tamura, Yasuda (b50) 2011; 6 Kenkel, Kelter, Hage (b78) 2000 Srivastava, Guzman-Guzman (b100) 1998; 36 Doğan, Ölmez (b31) 2015; 293 J. Kennedy, R. Eberhart, Particle swarm optimization, in: Proceedings of the 1995 IEEE International Conference on Neural Networks, 1995, pp. 1942–1948. Gong, Ji, Sun, Sun (b9) 2014; 137 Flores, López, Barrera (b44) 2011 Shen, Li (b48) 2009 Cuevas, González (b97) 2013; 17 Gong, Sun, Ji (b8) 2013; 233 De Falco, Della Cioppa, Maisto, Scafuri, Tarantino (b13) 2012; 207 Beyer, Schwefel (b16) 2002; 1 Akay, Karaboga (b55) 2012; 192 Genç, Eksin, Erol (b34) 2010 Cuevas, Cienfuegos, Zaldívar, Pérez-Cisneros (b59) 2013; 40 Gray, Gubbins (b84) 1984 Zhang, Xin (b74) 2017; 7 Krause, Cordeiro, Parpinelli, Lopes (b11) 2013 Hare, Nutini, Tesfamariam (b6) 2013; 59 X.S. Yang, S. Deb, Cuckoo search via Lévy flights, nature & biologically inspired computing, in: NaBIC 2009, World Congress on IEEE, 2009, pp. 210–214. Kilymis, Delaye, Ispas (b91) 2016; 432 Rao, Savsani, Vakharia (b45) 2012; 183 Zheng (b43) 2015; 55 Dorigo, Maniezzo, Colorni (b54) 1996; 26 Walker, King (b77) 2005 Mucherino, Seref (b69) 2007 Rocca, Oliveri, Massa (b17) 2011; 53 Simon (b24) 2009; 12 Hantush, Jacob (b101) 1955; 36 Kiran (b58) 2015; 42 Mirjalili (b63) 2016; 96 Eskandar, Sadollah, Bahreininejad, Hamdi (b51) 2012; 110 Mehrabian, Lucas (b26) 2006; 1 Javidy, Hatamlou, Mirjalili (b42) 2015; 32 Yang (b68) 2010; 2 M. Kripka, R.M.L. Kripka, Big crunch optimization method, in: International Conference on Engineering Optimization, Brazil, 2008, pp. 1–5. Topal, Altun (b27) 2016; 354 Zarand, Pazmandi, Pál, Zimányi (b46) 2002; 89 Mühlenbein, Gorges-Schleuter, Krämer (b7) 1988; 7 Punnathanam, Kotecha (b25) 2016; 54 Zhao, Wang (b92) 2016; 329 Civicioglu (b98) 2013; 219 Duan, Li, Wang, Sang, Jia (b5) 2018; 39 Juste, Kita, Tanaka, Hasegawa (b18) 1999; 14 Bertsimas, Jaillet, Martin (b73) 2018 Saremi, Mirjalili, Lewis (b62) 2017; 105 Zhang, Li, Li, Lai, Zhang (b99) 2016; 109 Yeh, Lin, Huang (b103) 2007; 21 Meng, Pan (b22) 2016; 97 Maitland, Rigby, Smith, Wakeham (b83) 1981 Hui, Bo, Hongyu (b104) 2009 Hasheminasab, Mehdipour (b90) 2016; 427 Liu, Gong, Sun, Jin (b4) 2017; 47 Shah-Hosseini (b35) 2011; 6 Patel (10.1016/j.knosys.2018.08.030_b49) 2015; 324 Pishkenari (10.1016/j.knosys.2018.08.030_b89) 2016; 380 Yeh (10.1016/j.knosys.2018.08.030_b103) 2007; 21 Kaveh (10.1016/j.knosys.2018.08.030_b65) 2013; 59 10.1016/j.knosys.2018.08.030_b94 Shen (10.1016/j.knosys.2018.08.030_b48) 2009 Genç (10.1016/j.knosys.2018.08.030_b34) 2010 Kaveh (10.1016/j.knosys.2018.08.030_b47) 2017; 110 Wang (10.1016/j.knosys.2018.08.030_b93) 2018; 52 Hasheminasab (10.1016/j.knosys.2018.08.030_b90) 2016; 427 Walker (10.1016/j.knosys.2018.08.030_b77) 2005 Holland (10.1016/j.knosys.2018.08.030_b14) 1975 Hui (10.1016/j.knosys.2018.08.030_b104) 2009 Uymaz (10.1016/j.knosys.2018.08.030_b23) 2015; 31 Cuevas (10.1016/j.knosys.2018.08.030_b97) 2013; 17 Lynn (10.1016/j.knosys.2018.08.030_b72) 2015; 24 Kirkpatrick (10.1016/j.knosys.2018.08.030_b1) 1983; 220 Zarand (10.1016/j.knosys.2018.08.030_b46) 2002; 89 Oftadeh (10.1016/j.knosys.2018.08.030_b66) 2010; 60 Rashedi (10.1016/j.knosys.2018.08.030_b28) 2009; 179 Mirjalili (10.1016/j.knosys.2018.08.030_b63) 2016; 96 Duan (10.1016/j.knosys.2018.08.030_b5) 2018; 39 Mucherino (10.1016/j.knosys.2018.08.030_b69) 2007 Passino (10.1016/j.knosys.2018.08.030_b12) 2002; 22 Kaveh (10.1016/j.knosys.2018.08.030_b39) 2010; 213 Geem (10.1016/j.knosys.2018.08.030_b10) 2001; 76 Shah-Hosseini (10.1016/j.knosys.2018.08.030_b35) 2011; 6 Mirjalili (10.1016/j.knosys.2018.08.030_b40) 2012; 2 Shah-Hosseini (10.1016/j.knosys.2018.08.030_b38) 2009; 1 10.1016/j.knosys.2018.08.030_b36 10.1016/j.knosys.2018.08.030_b30 Kenkel (10.1016/j.knosys.2018.08.030_b78) 2000 10.1016/j.knosys.2018.08.030_b2 Zhang (10.1016/j.knosys.2018.08.030_b99) 2016; 109 Maitland (10.1016/j.knosys.2018.08.030_b83) 1981 De Falco (10.1016/j.knosys.2018.08.030_b13) 2012; 207 Beni (10.1016/j.knosys.2018.08.030_b53) 1993 Mehrabian (10.1016/j.knosys.2018.08.030_b26) 2006; 1 Mühlenbein (10.1016/j.knosys.2018.08.030_b7) 1988; 7 Hantush (10.1016/j.knosys.2018.08.030_b101) 1955; 36 Barker (10.1016/j.knosys.2018.08.030_b76) 1870 Topal (10.1016/j.knosys.2018.08.030_b27) 2016; 354 Chuang (10.1016/j.knosys.2018.08.030_b37) 2007 Askarzadeh (10.1016/j.knosys.2018.08.030_b60) 2014; 19 Zhao (10.1016/j.knosys.2018.08.030_b92) 2016; 329 Gong (10.1016/j.knosys.2018.08.030_b8) 2013; 233 Javidy (10.1016/j.knosys.2018.08.030_b42) 2015; 32 Stone (10.1016/j.knosys.2018.08.030_b85) 1996 Alba (10.1016/j.knosys.2018.08.030_b71) 2005; 9 Moscato (10.1016/j.knosys.2018.08.030_b19) 2007; 88 Cuevas (10.1016/j.knosys.2018.08.030_b59) 2013; 40 Tuckerman (10.1016/j.knosys.2018.08.030_b88) 1993; 99 Zhang (10.1016/j.knosys.2018.08.030_b74) 2017; 7 Meng (10.1016/j.knosys.2018.08.030_b22) 2016; 97 Simon (10.1016/j.knosys.2018.08.030_b24) 2009; 12 Rao (10.1016/j.knosys.2018.08.030_b45) 2012; 183 Tamura (10.1016/j.knosys.2018.08.030_b50) 2011; 6 Krause (10.1016/j.knosys.2018.08.030_b11) 2013 Rahman (10.1016/j.knosys.2018.08.030_b80) 1964; 136 10.1016/j.knosys.2018.08.030_b52 Gong (10.1016/j.knosys.2018.08.030_b15) 2018; 22 Kilymis (10.1016/j.knosys.2018.08.030_b91) 2016; 432 Duman (10.1016/j.knosys.2018.08.030_b67) 2012; 217 Rocca (10.1016/j.knosys.2018.08.030_b17) 2011; 53 Eskandar (10.1016/j.knosys.2018.08.030_b51) 2012; 110 Jain (10.1016/j.knosys.2018.08.030_b70) 2018 Pan (10.1016/j.knosys.2018.08.030_b21) 2012; 26 Dorigo (10.1016/j.knosys.2018.08.030_b54) 1996; 26 Rapaport (10.1016/j.knosys.2018.08.030_b82) 1996; 10 Hsiao (10.1016/j.knosys.2018.08.030_b33) 2005 Kaveh (10.1016/j.knosys.2018.08.030_b32) 2016; 167 Srivastava (10.1016/j.knosys.2018.08.030_b100) 1998; 36 Zheng (10.1016/j.knosys.2018.08.030_b43) 2015; 55 Juste (10.1016/j.knosys.2018.08.030_b18) 1999; 14 Mirjalili (10.1016/j.knosys.2018.08.030_b95) 2016; 95 Li (10.1016/j.knosys.2018.08.030_b3) 2018; 181 Bertsimas (10.1016/j.knosys.2018.08.030_b73) 2018 Birbil (10.1016/j.knosys.2018.08.030_b29) 2003; 25 Lennard-Jones (10.1016/j.knosys.2018.08.030_b86) 1924; 106 Yang (10.1016/j.knosys.2018.08.030_b68) 2010; 2 Goldstein (10.1016/j.knosys.2018.08.030_b81) 2001 Mirjalili (10.1016/j.knosys.2018.08.030_b56) 2017; 114 Gong (10.1016/j.knosys.2018.08.030_b9) 2014; 137 Samuel (10.1016/j.knosys.2018.08.030_b102) 2003; 129 Ryckaert (10.1016/j.knosys.2018.08.030_b87) 1977; 23 Zheng (10.1016/j.knosys.2018.08.030_b41) 2010; 5 Kiran (10.1016/j.knosys.2018.08.030_b58) 2015; 42 Punnathanam (10.1016/j.knosys.2018.08.030_b25) 2016; 54 Hare (10.1016/j.knosys.2018.08.030_b6) 2013; 59 Wolpert (10.1016/j.knosys.2018.08.030_b75) 1997; 1 Gray (10.1016/j.knosys.2018.08.030_b84) 1984 Yang (10.1016/j.knosys.2018.08.030_b20) 2012; 29 Gandomi (10.1016/j.knosys.2018.08.030_b57) 2012; 17 Alder (10.1016/j.knosys.2018.08.030_b79) 1959; 31 Bayraktar (10.1016/j.knosys.2018.08.030_b96) 2013; 61 Civicioglu (10.1016/j.knosys.2018.08.030_b98) 2013; 219 Flores (10.1016/j.knosys.2018.08.030_b44) 2011 10.1016/j.knosys.2018.08.030_b61 Akay (10.1016/j.knosys.2018.08.030_b55) 2012; 192 Saremi (10.1016/j.knosys.2018.08.030_b62) 2017; 105 Beyer (10.1016/j.knosys.2018.08.030_b16) 2002; 1 Liu (10.1016/j.knosys.2018.08.030_b4) 2017; 47 Mohamed (10.1016/j.knosys.2018.08.030_b64) 2017; 142 Doğan (10.1016/j.knosys.2018.08.030_b31) 2015; 293 |
| References_xml | – volume: 52 start-page: 301 year: 2018 end-page: 311 ident: b93 article-title: A bare bones bacterial foraging optimization algorithm publication-title: Cognit. Syst. Res. – volume: 76 start-page: 60 year: 2001 end-page: 68 ident: b10 article-title: A new heuristic optimization algorithm: harmony search publication-title: Trans. Simul. – volume: 88 start-page: 56 year: 2007 end-page: 75 ident: b19 article-title: Benchmarking a memetic algorithm for ordering microarray data publication-title: Biosystems – volume: 96 start-page: 120 year: 2016 end-page: 133 ident: b63 article-title: SCA: a sine cosine algorithm for solving optimization problems publication-title: Knowl.-Based Syst. – reference: J.J. Liang, B.Y. Qu, P.N. Suganthan, Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore, 2013. – year: 1981 ident: b83 article-title: Intermolecular Forces: Their Origin and Determination – volume: 167 start-page: 69 year: 2016 end-page: 85 ident: b32 article-title: Water evaporation optimization: a novel physically inspired optimization algorithm publication-title: Comput. Struct. – year: 2000 ident: b78 article-title: Chemistry: An Industry-Based Introduction With CD-ROM – volume: 183 start-page: 1 year: 2012 end-page: 15 ident: b45 article-title: Teaching-learning-based optimization: an optimization method for continuous non-linear large scale problems publication-title: Inf. Sci. – volume: 6 start-page: 132 year: 2011 end-page: 140 ident: b35 article-title: Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimisation publication-title: Int. J. Comput. Sci. Eng. – volume: 59 start-page: 19 year: 2013 end-page: 28 ident: b6 article-title: A survey of non-gradient optimization methods in structural engineering publication-title: Adv. Eng. Softw. – start-page: 107 year: 2009 end-page: 115 ident: b104 article-title: Groundwater Dynamics – volume: 54 start-page: 62 year: 2016 end-page: 79 ident: b25 article-title: Yin-Yang-pair Optimization: a novel lightweight optimization algorithm publication-title: Eng. Appl. Artif. Intell. – volume: 61 start-page: 2745 year: 2013 end-page: 2757 ident: b96 article-title: The wind driven optimization technique and its application in electromagnetics publication-title: IEEE Trans. Antennas Propag. – volume: 5 start-page: 32 year: 2010 ident: b41 article-title: Gravitation field algorithm and its application in gene cluster publication-title: Algorithms Mol. Biol. – volume: 42 start-page: 6686 year: 2015 end-page: 6698 ident: b58 article-title: TSA: Tree-seed algorithm for continuous optimization publication-title: Expert Syst. Appl. – volume: 114 start-page: 163 year: 2017 end-page: 191 ident: b56 article-title: Salp Swarm Algorithm: a bio-inspired optimizer for engineering design problems publication-title: Adv. Eng. Softw. – volume: 36 start-page: 844 year: 1998 end-page: 848 ident: b100 article-title: Practical approximations of the well function publication-title: Groundwater – volume: 1 start-page: 355 year: 2006 end-page: 366 ident: b26 article-title: A novel numerical optimization algorithm inspired from weed colonization publication-title: Ecol. Inf. – volume: 219 start-page: 8121 year: 2013 end-page: 8144 ident: b98 article-title: Backtracking search optimization algorithm for numerical optimization problems publication-title: Appl. Math. Comput. – start-page: 881 year: 2010 end-page: 887 ident: b34 article-title: Big Bang-Big Crunch optimization algorithm hybridized with local directional moves and application to target motion analysis problem publication-title: 2010 IEEE International Conference on Systems Man and Cybernetics, SMC – volume: 192 start-page: 120 year: 2012 end-page: 142 ident: b55 article-title: A modified artificial bee colony algorithm for real-parameter optimization publication-title: Inf. Sci. – volume: 136 start-page: A405 year: 1964 end-page: A411 ident: b80 article-title: Correlations in the motion of atoms in liquid argon publication-title: Phys. Rev. – volume: 10 year: 1996 ident: b82 article-title: The art of molecular dynamics simulation publication-title: Comput. Phys. – start-page: 162 year: 2007 end-page: 173 ident: b69 article-title: Monkey search: a novel metaheuristic search for global optimization publication-title: AIP conference proceedings, Vol. 935 – year: 2001 ident: b81 article-title: Classical Mechanics – volume: 99 start-page: 2796 year: 1993 end-page: 2808 ident: b88 article-title: Efficient molecular dynamics and hybrid Monte Carlo algorithms for path integrals publication-title: J. Chem. Phys. – reference: M. Kripka, R.M.L. Kripka, Big crunch optimization method, in: International Conference on Engineering Optimization, Brazil, 2008, pp. 1–5. – start-page: 226 year: 2011 end-page: 237 ident: b44 article-title: Gravitational interactions optimization publication-title: International Conference on Learning and Intelligent Optimization – volume: 7 start-page: 65 year: 1988 end-page: 85 ident: b7 article-title: Evolution algorithms in combinatorial optimization publication-title: Parallel Comput. – year: 1870 ident: b76 article-title: Divisions of matter publication-title: A Text-Book of Elementary Chemistry: Theoretical and Inorganic – volume: 25 start-page: 263 year: 2003 end-page: 282 ident: b29 article-title: An electromagnetism-like mechanism for global optimization publication-title: J. Global Optim. – start-page: 2323 year: 2005 end-page: 2328 ident: b33 article-title: A novel optimization algorithm: space gravitational optimization publication-title: 2005 IEEE International Conference on Systems, Man and Cybernetics, Vol. 3 – volume: 324 start-page: 217 year: 2015 end-page: 246 ident: b49 article-title: Heat transfer search (HTS): a novel optimization algorithm publication-title: Inf. Sci. – volume: 354 start-page: 222 year: 2016 end-page: 235 ident: b27 article-title: A novel meta-heuristic algorithm: Dynamic Virtual Bats Algorithm publication-title: Inf. Sci. – volume: 1 start-page: 3 year: 2002 end-page: 52 ident: b16 article-title: Evolution strategies-a comprehensive introduction publication-title: Nat. Comput. – reference: X.S. Yang, S. Deb, Cuckoo search via Lévy flights, nature & biologically inspired computing, in: NaBIC 2009, World Congress on IEEE, 2009, pp. 210–214. – volume: 9 start-page: 126 year: 2005 end-page: 142 ident: b71 article-title: The exploration/exploitation tradeoff in dynamic cellular genetic algorithms publication-title: IEEE Trans. Evol. Comput. – volume: 1 start-page: 71 year: 2009 end-page: 79 ident: b38 article-title: The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm publication-title: Int. J. Bio-Inspired Comput. – volume: 12 start-page: 702 year: 2009 end-page: 713 ident: b24 article-title: Biogeography-based optimization publication-title: IEEE Trans. Evol. Comput. – volume: 29 start-page: 464 year: 2012 end-page: 483 ident: b20 article-title: Bat algorithm: a novel approach for global engineering optimization publication-title: Eng. Comput. – volume: 36 start-page: 95 year: 1955 end-page: 100 ident: b101 article-title: Non-steady radial flow in an infinite leaky aquifer publication-title: Trans. Amer. Geophys. Union – volume: 23 start-page: 327 year: 1977 end-page: 341 ident: b87 article-title: Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes publication-title: J. Comput. Phys. – volume: 293 start-page: 125 year: 2015 end-page: 145 ident: b31 article-title: A new metaheuristic for numerical function optimization: Vortex Search algorithm publication-title: Inf. Sci. – volume: 22 start-page: 52 year: 2002 end-page: 67 ident: b12 article-title: Biomimicry of bacterial foraging for distributed optimization and control publication-title: IEEE Control Syst. – volume: 179 start-page: 2232 year: 2009 end-page: 2248 ident: b28 article-title: GSA: a gravitational search algorithm publication-title: Inf. Sci. – start-page: 918 year: 2009 end-page: 922 ident: b48 article-title: Light ray optimization and its parameter analysis publication-title: International Joint Conference on Computational Sciences and Optimization, Vol. 2, CSO 2009 – volume: 32 start-page: 72 year: 2015 end-page: 79 ident: b42 article-title: Ions motion algorithm for solving optimization problems publication-title: Appl. Soft Comput. – volume: 31 start-page: 459 year: 1959 end-page: 466 ident: b79 article-title: Studies in molecular dynamics, I. general method publication-title: J. Chem. Phys. – volume: 6 start-page: S98 year: 2011 end-page: S100 ident: b50 article-title: Primary study of spiral dynamics inspired optimization publication-title: IEEE Trans. Electr. Electron. Eng. – volume: 26 start-page: 29 year: 1996 end-page: 41 ident: b54 article-title: Ant system: optimization by a colony of cooperating agents publication-title: IEEE Trans. Syst. Man Cybern. Part B – volume: 142 start-page: 190 year: 2017 end-page: 206 ident: b64 article-title: Optimal power flow using moth swarm algorithm publication-title: Electr. Power Syst. Res. – volume: 17 start-page: 489 year: 2013 end-page: 502 ident: b97 article-title: An optimization algorithm for multimodal functions inspired by collective animal behavior publication-title: Soft Comput. – volume: 2 start-page: 78 year: 2010 end-page: 84 ident: b68 article-title: Firefly algorithm, stochastic test functions and design optimization publication-title: Int. J. Bio-Inspired Comput. – volume: 22 start-page: 47 year: 2018 end-page: 60 ident: b15 article-title: A set-based genetic algorithm for interval many-objective optimization problems publication-title: IEEE Trans. Evol. Comput. – volume: 59 start-page: 53 year: 2013 end-page: 70 ident: b65 article-title: A new optimization method: dolphin echolocation publication-title: Adv. Eng. Softw. – volume: 181 start-page: 584 year: 2018 end-page: 598 ident: b3 article-title: Efficient multi-objective optimization algorithm for hybrid flow shop scheduling problems with setup energy consumptions publication-title: J. Cleaner Prod. – volume: 95 start-page: 51 year: 2016 end-page: 67 ident: b95 article-title: The whale optimization algorithm publication-title: Adv. Eng. Softw. – volume: 89 start-page: 150 year: 2002 end-page: 201 ident: b46 article-title: Using hysteresis for optimization publication-title: Phys. Rev. Lett. – start-page: 703 year: 1993 end-page: 712 ident: b53 article-title: Swarm intelligence in cellular robotic systems publication-title: Robots and Biological Systems: Towards a New Bionics? – reference: J. Kennedy, R. Eberhart, Particle swarm optimization, in: Proceedings of the 1995 IEEE International Conference on Neural Networks, 1995, pp. 1942–1948. – volume: 329 start-page: 719 year: 2016 end-page: 735 ident: b92 article-title: An effective bacterial foraging optimizer for global optimization publication-title: Inf. Sci. – year: 2018 ident: b73 article-title: Online Vehicle routing: The edge of optimization in large-scale applications publication-title: Oper. Res. – year: 1996 ident: b85 article-title: The Theory of Intermolecular Forces – volume: 207 start-page: 50 year: 2012 end-page: 65 ident: b13 article-title: Biological invasion-inspired migration in distributed evolutionary algorithms publication-title: Inf. Sci. – volume: 53 start-page: 38 year: 2011 end-page: 49 ident: b17 article-title: Differential evolution as applied to electromagnetics publication-title: IEEE Antennas Propag. Mag. – volume: 26 start-page: 69 year: 2012 end-page: 74 ident: b21 article-title: A new fruit fly optimization algorithm: taking the financial distress model as an example publication-title: Knowl.-Based Syst. – volume: 110 start-page: 69 year: 2017 end-page: 84 ident: b47 article-title: A novel meta-heuristic optimization algorithm: thermal exchange optimization publication-title: Adv. Eng. Softw. – year: 1975 ident: b14 article-title: Adaptation in Natural and Artificial Systems – volume: 7 start-page: 1955 year: 2017 end-page: 1963 ident: b74 article-title: Pollutant source identification model for water pollution incidents in small straight rivers based on genetic algorithm publication-title: Appl. Water Sci. – volume: 427 start-page: 161 year: 2016 end-page: 165 ident: b90 article-title: Molecular dynamics simulation of fluid sodium publication-title: Fluid Phase Equilib. – volume: 17 start-page: 4831 year: 2012 end-page: 4845 ident: b57 article-title: Krill herd: a new bio-inspired optimization algorithm publication-title: Commun. Nonlinear Sci. Numer. Simul. – year: 2005 ident: b77 article-title: What is Matter? – volume: 432 start-page: 354 year: 2016 end-page: 360 ident: b91 article-title: Density effects on the structure of irradiated sodium borosilicate glass: a molecular dynamics study publication-title: J. Non-cryst. Solids – volume: 217 start-page: 65 year: 2012 end-page: 77 ident: b67 article-title: Migrating Birds Optimization: A new metaheuristic approach and its performance on quadratic assignment problem publication-title: Inf. Sci. – volume: 55 start-page: 1 year: 2015 end-page: 11 ident: b43 article-title: Water wave optimization: a new nature-inspired metaheuristic publication-title: Comput. Oper. Res. – volume: 14 start-page: 1452 year: 1999 end-page: 1459 ident: b18 article-title: An evolutionary programming solution to the unit commitment problem publication-title: IEEE Trans. Power Syst. – volume: 24 start-page: 11 year: 2015 end-page: 24 ident: b72 article-title: Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation publication-title: Swarm Evol. Comput. – start-page: 169 year: 2013 end-page: 191 ident: b11 article-title: A survey of swarm algorithms applied to discrete optimization problems publication-title: Swarm Intelligence and Bio-inspired Computation: Theory and Applications – volume: 97 start-page: 144 year: 2016 end-page: 157 ident: b22 article-title: Monkey King Evolution: a new memetic evolutionary algorithm and its application in vehicle fuel consumption optimization publication-title: Knowl.-Based Syst. – volume: 2 start-page: 204 year: 2012 ident: b40 article-title: BMOA: binary magnetic optimization algorithm publication-title: Int. J. Mach. Learn. Comput. – reference: F.F. Moghaddam, R.F. Moghaddam, M. Cheriet, Curved space optimization: a random search based on general relativity theory, 2012. arXiv preprint – volume: 31 start-page: 153 year: 2015 end-page: 171 ident: b23 article-title: Artificial algae algorithm (AAA) for nonlinear global optimization publication-title: Appl. Soft Comput. – volume: 1 start-page: 67 year: 1997 end-page: 82 ident: b75 article-title: No free lunch theorems for optimization publication-title: IEEE Trans. Evol. Comput. – volume: 109 start-page: 218 year: 2016 end-page: 237 ident: b99 article-title: A mixed-strategy based gravitational search algorithm for parameter identification of hydraulic turbine governing system publication-title: Knowl.-Based Syst. – volume: 137 start-page: 241 year: 2014 end-page: 251 ident: b9 article-title: Interactive evolutionary algorithms with decision-maker’s preferences for solving interval multi-objective optimization problems publication-title: Neurocomputing – volume: 106 start-page: 463 year: 1924 end-page: 477 ident: b86 article-title: On the determination of molecular fields publication-title: Proc. R. Soc. Lond. A: Math. Phys. Eng. Sci. R. Soc. – start-page: 3157 year: 2007 end-page: 3164 ident: b37 article-title: Integrated radiation optimization: inspired by the gravitational radiation in the curvature of space–time publication-title: IEEE Congress on Evolutionary Computation, CEC 2007 – year: 2018 ident: b70 article-title: A novel nature-inspired algorithm for optimization: Squirrel search algorithm publication-title: Swarm Evol. Comput. – volume: 47 start-page: 2689 year: 2017 end-page: 2702 ident: b4 article-title: A many-objective evolutionary algorithm using a one-by-one selection strategy publication-title: IEEE Trans. Cybern. – volume: 110 start-page: 151 year: 2012 end-page: 166 ident: b51 article-title: Water cycle algorithm-a novel metaheuristic optimization method for solving constrained engineering optimization problems publication-title: Comput. Struct. – reference: W.F. Sacco, C.R.E. De Oliveira, A new stochastic optimization algorithm based on a particle collision metaheuristic, in: Proceedings of 6th WCSMO, 2005. – volume: 233 start-page: 141 year: 2013 end-page: 161 ident: b8 article-title: Evolutionary algorithms with preference polyhedron for interval multi-objective optimization problems publication-title: Inf. Sci. – volume: 40 start-page: 6374 year: 2013 end-page: 6384 ident: b59 article-title: A swarm optimization algorithm inspired in the behavior of the social-spider publication-title: Expert Syst. Appl. – volume: 19 start-page: 1213 year: 2014 end-page: 1228 ident: b60 article-title: Bird mating optimizer: an optimization algorithm inspired by bird mating strategies publication-title: Commun. Nonlinear Sci. Numer. Simul. – volume: 220 start-page: 671 year: 1983 end-page: 680 ident: b1 article-title: Optimization by simulated annealing publication-title: Science – volume: 39 start-page: 65 year: 2018 end-page: 77 ident: b5 article-title: Solving chiller loading optimization problems using an improved teaching-learning-based optimization algorithm publication-title: Optim. Control Appl. Methods – volume: 60 start-page: 2087 year: 2010 end-page: 2098 ident: b66 article-title: A novel meta-heuristic optimization algorithm inspired by group hunting of animals: hunting search publication-title: Comput. Math. Appl. – volume: 380 start-page: 4039 year: 2016 end-page: 4043 ident: b89 article-title: Molecular dynamics study of the thermal expansion coefficient of silicon publication-title: Phys. Lett. A – reference: . – volume: 213 start-page: 267 year: 2010 end-page: 289 ident: b39 article-title: A novel heuristic optimization method: charged system search publication-title: Acta Mech. – volume: 105 start-page: 30 year: 2017 end-page: 47 ident: b62 article-title: Grasshopper optimisation algorithm: theory and application publication-title: Adv. Eng. Softw. – volume: 21 start-page: 862 year: 2007 end-page: 872 ident: b103 article-title: Parameter identification for leaky aquifers using global optimization methods publication-title: Hydrol. Process. – year: 1984 ident: b84 article-title: Theory of Molecular Fluids, Volume 1: Fundamentals – volume: 129 start-page: 348 year: 2003 end-page: 359 ident: b102 article-title: Estimation of aquifer parameters from pumping test data by genetic algorithm optimization technique publication-title: J. Irrig. Drain. Div. – volume: 6 start-page: S98 issue: S1 year: 2011 ident: 10.1016/j.knosys.2018.08.030_b50 article-title: Primary study of spiral dynamics inspired optimization publication-title: IEEE Trans. Electr. Electron. Eng. doi: 10.1002/tee.20628 – volume: 137 start-page: 241 year: 2014 ident: 10.1016/j.knosys.2018.08.030_b9 article-title: Interactive evolutionary algorithms with decision-maker’s preferences for solving interval multi-objective optimization problems publication-title: Neurocomputing doi: 10.1016/j.neucom.2013.04.052 – year: 2001 ident: 10.1016/j.knosys.2018.08.030_b81 – volume: 380 start-page: 4039 issue: 48 year: 2016 ident: 10.1016/j.knosys.2018.08.030_b89 article-title: Molecular dynamics study of the thermal expansion coefficient of silicon publication-title: Phys. Lett. A doi: 10.1016/j.physleta.2016.08.027 – volume: 181 start-page: 584 year: 2018 ident: 10.1016/j.knosys.2018.08.030_b3 article-title: Efficient multi-objective optimization algorithm for hybrid flow shop scheduling problems with setup energy consumptions publication-title: J. Cleaner Prod. doi: 10.1016/j.jclepro.2018.02.004 – start-page: 107 year: 2009 ident: 10.1016/j.knosys.2018.08.030_b104 – volume: 2 start-page: 204 issue: 3 year: 2012 ident: 10.1016/j.knosys.2018.08.030_b40 article-title: BMOA: binary magnetic optimization algorithm publication-title: Int. J. Mach. Learn. Comput. doi: 10.7763/IJMLC.2012.V2.114 – ident: 10.1016/j.knosys.2018.08.030_b30 – year: 1984 ident: 10.1016/j.knosys.2018.08.030_b84 – volume: 29 start-page: 464 issue: 5 year: 2012 ident: 10.1016/j.knosys.2018.08.030_b20 article-title: Bat algorithm: a novel approach for global engineering optimization publication-title: Eng. Comput. doi: 10.1108/02644401211235834 – volume: 192 start-page: 120 year: 2012 ident: 10.1016/j.knosys.2018.08.030_b55 article-title: A modified artificial bee colony algorithm for real-parameter optimization publication-title: Inf. Sci. doi: 10.1016/j.ins.2010.07.015 – volume: 1 start-page: 67 issue: 1 year: 1997 ident: 10.1016/j.knosys.2018.08.030_b75 article-title: No free lunch theorems for optimization publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/4235.585893 – volume: 109 start-page: 218 year: 2016 ident: 10.1016/j.knosys.2018.08.030_b99 article-title: A mixed-strategy based gravitational search algorithm for parameter identification of hydraulic turbine governing system publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2016.07.005 – volume: 110 start-page: 151 year: 2012 ident: 10.1016/j.knosys.2018.08.030_b51 article-title: Water cycle algorithm-a novel metaheuristic optimization method for solving constrained engineering optimization problems publication-title: Comput. Struct. doi: 10.1016/j.compstruc.2012.07.010 – volume: 2 start-page: 78 issue: 2 year: 2010 ident: 10.1016/j.knosys.2018.08.030_b68 article-title: Firefly algorithm, stochastic test functions and design optimization publication-title: Int. J. Bio-Inspired Comput. doi: 10.1504/IJBIC.2010.032124 – volume: 32 start-page: 72 year: 2015 ident: 10.1016/j.knosys.2018.08.030_b42 article-title: Ions motion algorithm for solving optimization problems publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2015.03.035 – volume: 106 start-page: 463 issue: 738 year: 1924 ident: 10.1016/j.knosys.2018.08.030_b86 article-title: On the determination of molecular fields publication-title: Proc. R. Soc. Lond. A: Math. Phys. Eng. Sci. R. Soc. – volume: 217 start-page: 65 year: 2012 ident: 10.1016/j.knosys.2018.08.030_b67 article-title: Migrating Birds Optimization: A new metaheuristic approach and its performance on quadratic assignment problem publication-title: Inf. Sci. doi: 10.1016/j.ins.2012.06.032 – volume: 54 start-page: 62 year: 2016 ident: 10.1016/j.knosys.2018.08.030_b25 article-title: Yin-Yang-pair Optimization: a novel lightweight optimization algorithm publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2016.04.004 – volume: 42 start-page: 6686 issue: 19 year: 2015 ident: 10.1016/j.knosys.2018.08.030_b58 article-title: TSA: Tree-seed algorithm for continuous optimization publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2015.04.055 – volume: 136 start-page: A405 issue: 2A year: 1964 ident: 10.1016/j.knosys.2018.08.030_b80 article-title: Correlations in the motion of atoms in liquid argon publication-title: Phys. Rev. doi: 10.1103/PhysRev.136.A405 – year: 1996 ident: 10.1016/j.knosys.2018.08.030_b85 – volume: 207 start-page: 50 year: 2012 ident: 10.1016/j.knosys.2018.08.030_b13 article-title: Biological invasion-inspired migration in distributed evolutionary algorithms publication-title: Inf. Sci. doi: 10.1016/j.ins.2012.04.027 – volume: 22 start-page: 47 issue: 1 year: 2018 ident: 10.1016/j.knosys.2018.08.030_b15 article-title: A set-based genetic algorithm for interval many-objective optimization problems publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2016.2634625 – volume: 23 start-page: 327 issue: 3 year: 1977 ident: 10.1016/j.knosys.2018.08.030_b87 article-title: Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes publication-title: J. Comput. Phys. doi: 10.1016/0021-9991(77)90098-5 – volume: 129 start-page: 348 issue: 5 year: 2003 ident: 10.1016/j.knosys.2018.08.030_b102 article-title: Estimation of aquifer parameters from pumping test data by genetic algorithm optimization technique publication-title: J. Irrig. Drain. Div. doi: 10.1061/(ASCE)0733-9437(2003)129:5(348) – volume: 24 start-page: 11 year: 2015 ident: 10.1016/j.knosys.2018.08.030_b72 article-title: Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation publication-title: Swarm Evol. Comput. doi: 10.1016/j.swevo.2015.05.002 – volume: 36 start-page: 95 issue: 1 year: 1955 ident: 10.1016/j.knosys.2018.08.030_b101 article-title: Non-steady radial flow in an infinite leaky aquifer publication-title: Trans. Amer. Geophys. Union doi: 10.1029/TR036i001p00095 – volume: 22 start-page: 52 issue: 3 year: 2002 ident: 10.1016/j.knosys.2018.08.030_b12 article-title: Biomimicry of bacterial foraging for distributed optimization and control publication-title: IEEE Control Syst. doi: 10.1109/MCS.2002.1004010 – start-page: 918 year: 2009 ident: 10.1016/j.knosys.2018.08.030_b48 article-title: Light ray optimization and its parameter analysis – volume: 47 start-page: 2689 issue: 9 year: 2017 ident: 10.1016/j.knosys.2018.08.030_b4 article-title: A many-objective evolutionary algorithm using a one-by-one selection strategy publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2016.2638902 – volume: 9 start-page: 126 issue: 2 year: 2005 ident: 10.1016/j.knosys.2018.08.030_b71 article-title: The exploration/exploitation tradeoff in dynamic cellular genetic algorithms publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2005.843751 – start-page: 3157 year: 2007 ident: 10.1016/j.knosys.2018.08.030_b37 article-title: Integrated radiation optimization: inspired by the gravitational radiation in the curvature of space–time – year: 1870 ident: 10.1016/j.knosys.2018.08.030_b76 article-title: Divisions of matter – volume: 99 start-page: 2796 issue: 4 year: 1993 ident: 10.1016/j.knosys.2018.08.030_b88 article-title: Efficient molecular dynamics and hybrid Monte Carlo algorithms for path integrals publication-title: J. Chem. Phys. doi: 10.1063/1.465188 – volume: 167 start-page: 69 year: 2016 ident: 10.1016/j.knosys.2018.08.030_b32 article-title: Water evaporation optimization: a novel physically inspired optimization algorithm publication-title: Comput. Struct. doi: 10.1016/j.compstruc.2016.01.008 – ident: 10.1016/j.knosys.2018.08.030_b52 – volume: 10 issue: 5 year: 1996 ident: 10.1016/j.knosys.2018.08.030_b82 article-title: The art of molecular dynamics simulation publication-title: Comput. Phys. doi: 10.1063/1.4822471 – ident: 10.1016/j.knosys.2018.08.030_b94 – volume: 76 start-page: 60 issue: 2 year: 2001 ident: 10.1016/j.knosys.2018.08.030_b10 article-title: A new heuristic optimization algorithm: harmony search publication-title: Trans. Simul. doi: 10.1177/003754970107600201 – volume: 1 start-page: 355 issue: 4 year: 2006 ident: 10.1016/j.knosys.2018.08.030_b26 article-title: A novel numerical optimization algorithm inspired from weed colonization publication-title: Ecol. Inf. doi: 10.1016/j.ecoinf.2006.07.003 – start-page: 162 year: 2007 ident: 10.1016/j.knosys.2018.08.030_b69 article-title: Monkey search: a novel metaheuristic search for global optimization – volume: 142 start-page: 190 year: 2017 ident: 10.1016/j.knosys.2018.08.030_b64 article-title: Optimal power flow using moth swarm algorithm publication-title: Electr. Power Syst. Res. doi: 10.1016/j.epsr.2016.09.025 – year: 2018 ident: 10.1016/j.knosys.2018.08.030_b73 article-title: Online Vehicle routing: The edge of optimization in large-scale applications publication-title: Oper. Res. – volume: 6 start-page: 132 issue: 1–2 year: 2011 ident: 10.1016/j.knosys.2018.08.030_b35 article-title: Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimisation publication-title: Int. J. Comput. Sci. Eng. – start-page: 703 year: 1993 ident: 10.1016/j.knosys.2018.08.030_b53 article-title: Swarm intelligence in cellular robotic systems – volume: 59 start-page: 53 year: 2013 ident: 10.1016/j.knosys.2018.08.030_b65 article-title: A new optimization method: dolphin echolocation publication-title: Adv. Eng. Softw. doi: 10.1016/j.advengsoft.2013.03.004 – year: 2000 ident: 10.1016/j.knosys.2018.08.030_b78 – year: 2005 ident: 10.1016/j.knosys.2018.08.030_b77 – volume: 220 start-page: 671 issue: 4598 year: 1983 ident: 10.1016/j.knosys.2018.08.030_b1 article-title: Optimization by simulated annealing publication-title: Science doi: 10.1126/science.220.4598.671 – volume: 293 start-page: 125 year: 2015 ident: 10.1016/j.knosys.2018.08.030_b31 article-title: A new metaheuristic for numerical function optimization: Vortex Search algorithm publication-title: Inf. Sci. doi: 10.1016/j.ins.2014.08.053 – volume: 26 start-page: 69 year: 2012 ident: 10.1016/j.knosys.2018.08.030_b21 article-title: A new fruit fly optimization algorithm: taking the financial distress model as an example publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2011.07.001 – volume: 59 start-page: 19 year: 2013 ident: 10.1016/j.knosys.2018.08.030_b6 article-title: A survey of non-gradient optimization methods in structural engineering publication-title: Adv. Eng. Softw. doi: 10.1016/j.advengsoft.2013.03.001 – volume: 233 start-page: 141 year: 2013 ident: 10.1016/j.knosys.2018.08.030_b8 article-title: Evolutionary algorithms with preference polyhedron for interval multi-objective optimization problems publication-title: Inf. Sci. doi: 10.1016/j.ins.2013.01.020 – volume: 329 start-page: 719 year: 2016 ident: 10.1016/j.knosys.2018.08.030_b92 article-title: An effective bacterial foraging optimizer for global optimization publication-title: Inf. Sci. doi: 10.1016/j.ins.2015.10.001 – volume: 52 start-page: 301 year: 2018 ident: 10.1016/j.knosys.2018.08.030_b93 article-title: A bare bones bacterial foraging optimization algorithm publication-title: Cognit. Syst. Res. doi: 10.1016/j.cogsys.2018.07.022 – volume: 21 start-page: 862 issue: 7 year: 2007 ident: 10.1016/j.knosys.2018.08.030_b103 article-title: Parameter identification for leaky aquifers using global optimization methods publication-title: Hydrol. Process. doi: 10.1002/hyp.6274 – year: 2018 ident: 10.1016/j.knosys.2018.08.030_b70 article-title: A novel nature-inspired algorithm for optimization: Squirrel search algorithm publication-title: Swarm Evol. Comput. doi: 10.1016/j.swevo.2018.02.013 – volume: 88 start-page: 56 issue: 1 year: 2007 ident: 10.1016/j.knosys.2018.08.030_b19 article-title: Benchmarking a memetic algorithm for ordering microarray data publication-title: Biosystems doi: 10.1016/j.biosystems.2006.04.005 – volume: 26 start-page: 29 issue: 1 year: 1996 ident: 10.1016/j.knosys.2018.08.030_b54 article-title: Ant system: optimization by a colony of cooperating agents publication-title: IEEE Trans. Syst. Man Cybern. Part B doi: 10.1109/3477.484436 – ident: 10.1016/j.knosys.2018.08.030_b61 doi: 10.1109/NABIC.2009.5393690 – volume: 61 start-page: 2745 issue: 5 year: 2013 ident: 10.1016/j.knosys.2018.08.030_b96 article-title: The wind driven optimization technique and its application in electromagnetics publication-title: IEEE Trans. Antennas Propag. doi: 10.1109/TAP.2013.2238654 – volume: 39 start-page: 65 issue: 1 year: 2018 ident: 10.1016/j.knosys.2018.08.030_b5 article-title: Solving chiller loading optimization problems using an improved teaching-learning-based optimization algorithm publication-title: Optim. Control Appl. Methods doi: 10.1002/oca.2334 – volume: 114 start-page: 163 year: 2017 ident: 10.1016/j.knosys.2018.08.030_b56 article-title: Salp Swarm Algorithm: a bio-inspired optimizer for engineering design problems publication-title: Adv. Eng. Softw. doi: 10.1016/j.advengsoft.2017.07.002 – volume: 55 start-page: 1 year: 2015 ident: 10.1016/j.knosys.2018.08.030_b43 article-title: Water wave optimization: a new nature-inspired metaheuristic publication-title: Comput. Oper. Res. doi: 10.1016/j.cor.2014.10.008 – volume: 105 start-page: 30 year: 2017 ident: 10.1016/j.knosys.2018.08.030_b62 article-title: Grasshopper optimisation algorithm: theory and application publication-title: Adv. Eng. Softw. doi: 10.1016/j.advengsoft.2017.01.004 – volume: 1 start-page: 71 issue: 1–2 year: 2009 ident: 10.1016/j.knosys.2018.08.030_b38 article-title: The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm publication-title: Int. J. Bio-Inspired Comput. doi: 10.1504/IJBIC.2009.022775 – volume: 31 start-page: 459 issue: 2 year: 1959 ident: 10.1016/j.knosys.2018.08.030_b79 article-title: Studies in molecular dynamics, I. general method publication-title: J. Chem. Phys. doi: 10.1063/1.1730376 – volume: 89 start-page: 150 issue: 15 year: 2002 ident: 10.1016/j.knosys.2018.08.030_b46 article-title: Using hysteresis for optimization publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.89.150201 – volume: 25 start-page: 263 issue: 3 year: 2003 ident: 10.1016/j.knosys.2018.08.030_b29 article-title: An electromagnetism-like mechanism for global optimization publication-title: J. Global Optim. doi: 10.1023/A:1022452626305 – volume: 324 start-page: 217 year: 2015 ident: 10.1016/j.knosys.2018.08.030_b49 article-title: Heat transfer search (HTS): a novel optimization algorithm publication-title: Inf. Sci. doi: 10.1016/j.ins.2015.06.044 – volume: 60 start-page: 2087 issue: 7 year: 2010 ident: 10.1016/j.knosys.2018.08.030_b66 article-title: A novel meta-heuristic optimization algorithm inspired by group hunting of animals: hunting search publication-title: Comput. Math. Appl. doi: 10.1016/j.camwa.2010.07.049 – volume: 36 start-page: 844 issue: 5 year: 1998 ident: 10.1016/j.knosys.2018.08.030_b100 article-title: Practical approximations of the well function publication-title: Groundwater doi: 10.1111/j.1745-6584.1998.tb02203.x – start-page: 881 year: 2010 ident: 10.1016/j.knosys.2018.08.030_b34 article-title: Big Bang-Big Crunch optimization algorithm hybridized with local directional moves and application to target motion analysis problem – volume: 14 start-page: 1452 issue: 4 year: 1999 ident: 10.1016/j.knosys.2018.08.030_b18 article-title: An evolutionary programming solution to the unit commitment problem publication-title: IEEE Trans. Power Syst. doi: 10.1109/59.801925 – volume: 31 start-page: 153 year: 2015 ident: 10.1016/j.knosys.2018.08.030_b23 article-title: Artificial algae algorithm (AAA) for nonlinear global optimization publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2015.03.003 – volume: 354 start-page: 222 year: 2016 ident: 10.1016/j.knosys.2018.08.030_b27 article-title: A novel meta-heuristic algorithm: Dynamic Virtual Bats Algorithm publication-title: Inf. Sci. doi: 10.1016/j.ins.2016.03.025 – start-page: 169 year: 2013 ident: 10.1016/j.knosys.2018.08.030_b11 article-title: A survey of swarm algorithms applied to discrete optimization problems – volume: 183 start-page: 1 issue: 1 year: 2012 ident: 10.1016/j.knosys.2018.08.030_b45 article-title: Teaching-learning-based optimization: an optimization method for continuous non-linear large scale problems publication-title: Inf. Sci. doi: 10.1016/j.ins.2011.08.006 – volume: 17 start-page: 4831 issue: 12 year: 2012 ident: 10.1016/j.knosys.2018.08.030_b57 article-title: Krill herd: a new bio-inspired optimization algorithm publication-title: Commun. Nonlinear Sci. Numer. Simul. doi: 10.1016/j.cnsns.2012.05.010 – volume: 213 start-page: 267 issue: 3 year: 2010 ident: 10.1016/j.knosys.2018.08.030_b39 article-title: A novel heuristic optimization method: charged system search publication-title: Acta Mech. doi: 10.1007/s00707-009-0270-4 – ident: 10.1016/j.knosys.2018.08.030_b36 – volume: 1 start-page: 3 issue: 1 year: 2002 ident: 10.1016/j.knosys.2018.08.030_b16 article-title: Evolution strategies-a comprehensive introduction publication-title: Nat. Comput. doi: 10.1023/A:1015059928466 – volume: 427 start-page: 161 year: 2016 ident: 10.1016/j.knosys.2018.08.030_b90 article-title: Molecular dynamics simulation of fluid sodium publication-title: Fluid Phase Equilib. doi: 10.1016/j.fluid.2016.07.008 – start-page: 2323 year: 2005 ident: 10.1016/j.knosys.2018.08.030_b33 article-title: A novel optimization algorithm: space gravitational optimization – volume: 40 start-page: 6374 issue: 16 year: 2013 ident: 10.1016/j.knosys.2018.08.030_b59 article-title: A swarm optimization algorithm inspired in the behavior of the social-spider publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2013.05.041 – start-page: 226 year: 2011 ident: 10.1016/j.knosys.2018.08.030_b44 article-title: Gravitational interactions optimization – volume: 179 start-page: 2232 issue: 13 year: 2009 ident: 10.1016/j.knosys.2018.08.030_b28 article-title: GSA: a gravitational search algorithm publication-title: Inf. Sci. doi: 10.1016/j.ins.2009.03.004 – volume: 17 start-page: 489 issue: 3 year: 2013 ident: 10.1016/j.knosys.2018.08.030_b97 article-title: An optimization algorithm for multimodal functions inspired by collective animal behavior publication-title: Soft Comput. doi: 10.1007/s00500-012-0921-6 – volume: 7 start-page: 1955 issue: 4 year: 2017 ident: 10.1016/j.knosys.2018.08.030_b74 article-title: Pollutant source identification model for water pollution incidents in small straight rivers based on genetic algorithm publication-title: Appl. Water Sci. doi: 10.1007/s13201-015-0374-z – volume: 110 start-page: 69 year: 2017 ident: 10.1016/j.knosys.2018.08.030_b47 article-title: A novel meta-heuristic optimization algorithm: thermal exchange optimization publication-title: Adv. Eng. Softw. doi: 10.1016/j.advengsoft.2017.03.014 – volume: 95 start-page: 51 year: 2016 ident: 10.1016/j.knosys.2018.08.030_b95 article-title: The whale optimization algorithm publication-title: Adv. Eng. Softw. doi: 10.1016/j.advengsoft.2016.01.008 – volume: 5 start-page: 32 issue: 1 year: 2010 ident: 10.1016/j.knosys.2018.08.030_b41 article-title: Gravitation field algorithm and its application in gene cluster publication-title: Algorithms Mol. Biol. doi: 10.1186/1748-7188-5-32 – volume: 219 start-page: 8121 issue: 15 year: 2013 ident: 10.1016/j.knosys.2018.08.030_b98 article-title: Backtracking search optimization algorithm for numerical optimization problems publication-title: Appl. Math. Comput. doi: 10.1016/j.amc.2013.02.017 – volume: 12 start-page: 702 issue: 6 year: 2009 ident: 10.1016/j.knosys.2018.08.030_b24 article-title: Biogeography-based optimization publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2008.919004 – volume: 53 start-page: 38 issue: 1 year: 2011 ident: 10.1016/j.knosys.2018.08.030_b17 article-title: Differential evolution as applied to electromagnetics publication-title: IEEE Antennas Propag. Mag. doi: 10.1109/MAP.2011.5773566 – year: 1975 ident: 10.1016/j.knosys.2018.08.030_b14 – volume: 7 start-page: 65 issue: 1 year: 1988 ident: 10.1016/j.knosys.2018.08.030_b7 article-title: Evolution algorithms in combinatorial optimization publication-title: Parallel Comput. doi: 10.1016/0167-8191(88)90098-1 – volume: 19 start-page: 1213 issue: 4 year: 2014 ident: 10.1016/j.knosys.2018.08.030_b60 article-title: Bird mating optimizer: an optimization algorithm inspired by bird mating strategies publication-title: Commun. Nonlinear Sci. Numer. Simul. doi: 10.1016/j.cnsns.2013.08.027 – volume: 97 start-page: 144 year: 2016 ident: 10.1016/j.knosys.2018.08.030_b22 article-title: Monkey King Evolution: a new memetic evolutionary algorithm and its application in vehicle fuel consumption optimization publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2016.01.009 – volume: 432 start-page: 354 year: 2016 ident: 10.1016/j.knosys.2018.08.030_b91 article-title: Density effects on the structure of irradiated sodium borosilicate glass: a molecular dynamics study publication-title: J. Non-cryst. Solids doi: 10.1016/j.jnoncrysol.2015.10.031 – ident: 10.1016/j.knosys.2018.08.030_b2 doi: 10.1109/ICNN.1995.488968 – volume: 96 start-page: 120 year: 2016 ident: 10.1016/j.knosys.2018.08.030_b63 article-title: SCA: a sine cosine algorithm for solving optimization problems publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2015.12.022 – year: 1981 ident: 10.1016/j.knosys.2018.08.030_b83 |
| SSID | ssj0002218 |
| Score | 2.667558 |
| Snippet | In recent years, various metaheuristic optimization methods have been proposed in scientific and engineering fields. In this study, a novel physics-inspired... |
| SourceID | proquest crossref elsevier |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 283 |
| SubjectTerms | Algorithms Atom search optimization Benchmark functions Engineering Estimating techniques Estimation Global optimization Heuristic algorithm Heuristic methods Lennard-Jones potential Mathematical models Metaheuristic Molecular dynamics Novels Optimization Optimization algorithm Optimization algorithms Parameter estimation Physics World problems |
| Title | Atom search optimization and its application to solve a hydrogeologic parameter estimation problem |
| URI | https://dx.doi.org/10.1016/j.knosys.2018.08.030 https://www.proquest.com/docview/2165064710 |
| Volume | 163 |
| WOSCitedRecordID | wos000454468200023&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1872-7409 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002218 issn: 0950-7051 databaseCode: AIEXJ dateStart: 19950201 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3Pb9MwFLaqjgMXxvghBmPygVtklNpJnBwrNMSPaUJiQLVL5NjO2rEmU5dVncQfz3NspykVGiBxidqkSdy8L58_P_u9h9ArkBgq4VSQTI0kiahiRCheEhUqVqgkLGnrh_x6zE9O0skk-zQY_PCxMMtLXlXpapVd_VdTwz4wtgmd_QtzdxeFHfAZjA5bMDts_8jw46aeB86ZUQMhzF2kZTdN0JuzNsoTWrPUgQimt2pRn2tLhoFJCT43S2UCk4bDxjcGrvpMX9B-9D45YvpD5TJDd0L9bCpaX-w3PTu_qdfee8swx7Nb33P2fddnU12t_AHnkTBBUBseie1QGedvDAkPXXZZbdk25SDvozDboGNHeI5QbZkb1zczW6p4i_atB-Li9feqhr9pFuylbWJWN-WzmVD7s2mKaQmwmak0AmPnHcrjLB2infH7o8mHrientPUPd033oZft-sDte_1O2vzSybfK5fQheuCGHHhsobKHBrp6hHZ9OQ_s2P0xKgxysEUO7iMHA3IwIAf3kIObGrfIwQJvIAd3yMFr5GCHnCfoy9uj0zfviCvBQSSQe0O4ghFuTAsmR4wlIgt1XBYmGRZ8kzAajpMIBKziIPI0lxkXNC5ZnHDJRQQkQNlTNKzqSj9DWIiwTDMRazOElTpMRZlEBehDySnTOtlHzD-9XLr89KZMymXuFyJe5PaZ5-aZ56Z6Kgv3EenOurL5We74PfeGyZ3GtNoxByzdceaBt2PuXnc4PkpMxkeQ6c__-cIv0P31W3SAhs3iRr9E9-SymV0vDh0mfwLLtbCh |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Atom+search+optimization+and+its+application+to+solve+a+hydrogeologic+parameter+estimation+problem&rft.jtitle=Knowledge-based+systems&rft.au=Zhao%2C+Weiguo&rft.au=Wang%2C+Liying&rft.au=Zhang%2C+Zhenxing&rft.date=2019-01-01&rft.pub=Elsevier+B.V&rft.issn=0950-7051&rft.eissn=1872-7409&rft.volume=163&rft.spage=283&rft.epage=304&rft_id=info:doi/10.1016%2Fj.knosys.2018.08.030&rft.externalDocID=S0950705118304271 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0950-7051&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0950-7051&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0950-7051&client=summon |