A hybrid self-adaptive sine cosine algorithm with opposition based learning
•A new method to solve global optimization and engineering problems called m-SCA.•The m-SCA improves the SCA using self-adaptation and opposition based learning.•Two set of benchmarks (classical and CEC 2014) is taken to evaluate the performance.•The m-SCA is also tested on engineering optimization...
Uložené v:
| Vydané v: | Expert systems with applications Ročník 119; s. 210 - 230 |
|---|---|
| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
New York
Elsevier Ltd
01.04.2019
Elsevier BV |
| Predmet: | |
| ISSN: | 0957-4174, 1873-6793 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | •A new method to solve global optimization and engineering problems called m-SCA.•The m-SCA improves the SCA using self-adaptation and opposition based learning.•Two set of benchmarks (classical and CEC 2014) is taken to evaluate the performance.•The m-SCA is also tested on engineering optimization problems.•Comparisons illustrate the improvement on the performance of m-SCA.
Real-world optimization problems demand an efficient meta-heuristic algorithm which maintains the diversity of solutions and properly exploits the search space of the problem to find the global optimal solution. Sine Cosine Algorithm (SCA) is a recently developed population-based meta-heuristic algorithm for solving global optimization problems. SCA uses the characteristics of sine and cosine trigonometric functions to update the solutions. But, like other population-based optimization algorithms, SCA also suffers the problem of low diversity, stagnation in local optima and skipping of true solutions. Therefore, in the present work, an attempt has been made towards the eradication of these issues, by proposing a modified version of SCA. The proposed algorithm is named as modified Sine Cosine Algorithm (m-SCA). In m-SCA, the opposite population is generated using opposite numbers based on perturbation rate to jump out from the local optima. Secondly, in the search equations of SCA self-adaptive component is added to exploit all the promising search regions which are pre-visited. To evaluate the effectiveness in solving the global optimization problems, m-SCA has been tested on two sets of benchmark problems – classical set of 23 well-known benchmark problems and standard IEEE CEC 2014 benchmark test problems. In the paper, the performance of proposed algorithm m-SCA is also tested on five engineering optimization problems. The conducted statistical, convergence and average distance analysis demonstrate the efficacy of the proposed algorithm to determine the efficient solution of real-life global optimization problems. |
|---|---|
| AbstractList | •A new method to solve global optimization and engineering problems called m-SCA.•The m-SCA improves the SCA using self-adaptation and opposition based learning.•Two set of benchmarks (classical and CEC 2014) is taken to evaluate the performance.•The m-SCA is also tested on engineering optimization problems.•Comparisons illustrate the improvement on the performance of m-SCA.
Real-world optimization problems demand an efficient meta-heuristic algorithm which maintains the diversity of solutions and properly exploits the search space of the problem to find the global optimal solution. Sine Cosine Algorithm (SCA) is a recently developed population-based meta-heuristic algorithm for solving global optimization problems. SCA uses the characteristics of sine and cosine trigonometric functions to update the solutions. But, like other population-based optimization algorithms, SCA also suffers the problem of low diversity, stagnation in local optima and skipping of true solutions. Therefore, in the present work, an attempt has been made towards the eradication of these issues, by proposing a modified version of SCA. The proposed algorithm is named as modified Sine Cosine Algorithm (m-SCA). In m-SCA, the opposite population is generated using opposite numbers based on perturbation rate to jump out from the local optima. Secondly, in the search equations of SCA self-adaptive component is added to exploit all the promising search regions which are pre-visited. To evaluate the effectiveness in solving the global optimization problems, m-SCA has been tested on two sets of benchmark problems – classical set of 23 well-known benchmark problems and standard IEEE CEC 2014 benchmark test problems. In the paper, the performance of proposed algorithm m-SCA is also tested on five engineering optimization problems. The conducted statistical, convergence and average distance analysis demonstrate the efficacy of the proposed algorithm to determine the efficient solution of real-life global optimization problems. Real-world optimization problems demand an efficient meta-heuristic algorithm which maintains the diversity of solutions and properly exploits the search space of the problem to find the global optimal solution. Sine Cosine Algorithm (SCA) is a recently developed population-based meta-heuristic algorithm for solving global optimization problems. SCA uses the characteristics of sine and cosine trigonometric functions to update the solutions. But, like other population-based optimization algorithms, SCA also suffers the problem of low diversity, stagnation in local optima and skipping of true solutions. Therefore, in the present work, an attempt has been made towards the eradication of these issues, by proposing a modified version of SCA. The proposed algorithm is named as modified Sine Cosine Algorithm (m-SCA). In m-SCA, the opposite population is generated using opposite numbers based on perturbation rate to jump out from the local optima. Secondly, in the search equations of SCA self-adaptive component is added to exploit all the promising search regions which are pre-visited. To evaluate the effectiveness in solving the global optimization problems, m-SCA has been tested on two sets of benchmark problems – classical set of 23 well-known benchmark problems and standard IEEE CEC 2014 benchmark test problems. In the paper, the performance of proposed algorithm m-SCA is also tested on five engineering optimization problems. The conducted statistical, convergence and average distance analysis demonstrate the efficacy of the proposed algorithm to determine the efficient solution of real-life global optimization problems. |
| Author | Deep, Kusum Gupta, Shubham |
| Author_xml | – sequence: 1 givenname: Shubham orcidid: 0000-0002-3779-2932 surname: Gupta fullname: Gupta, Shubham email: sgupta@ma.iitr.ac.in – sequence: 2 givenname: Kusum surname: Deep fullname: Deep, Kusum email: kusumfma@iitr.ac.in |
| BookMark | eNp9kDtPwzAUhS1UJNrCH2CKxJxgx4ntSCxVxUtUYoHZ8uOmdZXGwQ6t-u9JKBNDl3uko_vdx5mhSetbQOiW4Ixgwu63GcSDynJMxGBkuMQXaEoEpynjFZ2gKa5KnhaEF1doFuMWY8Ix5lP0tkg2Rx2cTSI0daqs6nq3hyS6FhLjf0U1ax9cv9klh6EmvusGv3e-TbSKYJMGVGhdu75Gl7VqItz86Rx9Pj1-LF_S1fvz63KxSg3NRZ9SXdJC14VQlaJUCwu0qIUmwlSUFbkVgjHOQRMLmgG1uGIlJ9hqaizjtKBzdHea2wX_9Q2xl1v_HdphpcwJz0nBRIWHLnHqMsHHGKCWxvVqPLsPyjWSYDlGJ7dyjE6O0Y3eEN2A5v_QLridCsfz0MMJguH1vYMgo3HQGrAugOml9e4c_gPrZoob |
| CitedBy_id | crossref_primary_10_1186_s43067_020_00023_6 crossref_primary_10_1007_s00500_019_04411_7 crossref_primary_10_1007_s42235_023_00437_8 crossref_primary_10_1016_j_eswa_2019_113113 crossref_primary_10_1016_j_eswa_2020_113486 crossref_primary_10_1109_ACCESS_2023_3296255 crossref_primary_10_1016_j_eswa_2021_116001 crossref_primary_10_1016_j_compbiomed_2022_106239 crossref_primary_10_1007_s10586_024_04982_7 crossref_primary_10_1016_j_amc_2019_124872 crossref_primary_10_3390_s22176420 crossref_primary_10_1016_j_jocs_2020_101219 crossref_primary_10_1007_s00607_024_01256_3 crossref_primary_10_1016_j_eswa_2022_118372 crossref_primary_10_3390_sym13122388 crossref_primary_10_1016_j_compbiomed_2021_104582 crossref_primary_10_1016_j_eswa_2023_121048 crossref_primary_10_1016_j_advengsoft_2023_103517 crossref_primary_10_1088_2632_2153_ad55a5 crossref_primary_10_1007_s00521_020_05610_2 crossref_primary_10_1371_journal_pone_0325272 crossref_primary_10_1038_s41598_022_17881_x crossref_primary_10_1007_s00500_019_03949_w crossref_primary_10_1016_j_asoc_2022_109869 crossref_primary_10_1007_s00366_020_01083_y crossref_primary_10_1016_j_eswa_2021_114950 crossref_primary_10_1016_j_engappai_2020_103718 crossref_primary_10_3389_feart_2023_1116664 crossref_primary_10_1007_s10614_024_10728_9 crossref_primary_10_1016_j_knosys_2024_111850 crossref_primary_10_1038_s41598_025_95678_4 crossref_primary_10_1007_s00500_022_07389_x crossref_primary_10_3390_diagnostics13081422 crossref_primary_10_1038_s41598_025_95545_2 crossref_primary_10_1016_j_eswa_2020_113395 crossref_primary_10_1016_j_eswa_2023_120849 crossref_primary_10_1371_journal_pone_0276210 crossref_primary_10_1016_j_apm_2021_02_002 crossref_primary_10_1007_s10489_023_04705_2 crossref_primary_10_1016_j_eswa_2022_116856 crossref_primary_10_1007_s00366_021_01448_x crossref_primary_10_3390_en12112189 crossref_primary_10_1002_int_22658 crossref_primary_10_1016_j_asoc_2021_107384 crossref_primary_10_1007_s00521_020_05500_7 crossref_primary_10_1007_s00521_021_05963_2 crossref_primary_10_1109_ACCESS_2021_3055367 crossref_primary_10_1016_j_asoc_2021_107146 crossref_primary_10_1016_j_asoc_2020_106933 crossref_primary_10_3390_biomimetics9060334 crossref_primary_10_1007_s42235_022_00323_9 crossref_primary_10_1016_j_phycom_2022_101996 crossref_primary_10_1007_s11042_023_17189_6 crossref_primary_10_1080_23302674_2024_2305817 crossref_primary_10_1371_journal_pone_0255269 crossref_primary_10_1016_j_jocs_2023_102105 crossref_primary_10_3390_pr13092707 crossref_primary_10_1007_s42235_024_00510_w crossref_primary_10_1016_j_cma_2023_115878 crossref_primary_10_1016_j_asoc_2021_107197 crossref_primary_10_1080_08839514_2020_1848276 crossref_primary_10_1007_s10489_022_03786_9 crossref_primary_10_1016_j_asoc_2022_108562 crossref_primary_10_1007_s42235_025_00675_y crossref_primary_10_1016_j_knosys_2020_106461 crossref_primary_10_1109_ACCESS_2021_3054053 crossref_primary_10_1007_s00500_019_03939_y crossref_primary_10_1007_s12065_019_00251_4 crossref_primary_10_32604_cmes_2023_024247 crossref_primary_10_3390_biomimetics10080494 crossref_primary_10_1109_ACCESS_2020_3003366 crossref_primary_10_1371_journal_pone_0322111 crossref_primary_10_1016_j_jocs_2021_101477 crossref_primary_10_1038_s41598_024_77440_4 crossref_primary_10_1016_j_geits_2022_100040 crossref_primary_10_1007_s10462_025_11289_5 crossref_primary_10_3390_math10183368 crossref_primary_10_1007_s10462_022_10277_3 crossref_primary_10_1016_j_jhydrol_2020_125223 crossref_primary_10_1166_jmihi_2021_3838 crossref_primary_10_1007_s11831_021_09562_1 crossref_primary_10_1155_2020_4873501 crossref_primary_10_1007_s00366_020_01252_z crossref_primary_10_1007_s12559_025_10415_3 crossref_primary_10_1007_s42235_021_0050_y crossref_primary_10_1016_j_knosys_2022_109326 crossref_primary_10_1007_s00366_021_01464_x crossref_primary_10_1007_s10462_022_10343_w crossref_primary_10_1038_s41598_025_15205_3 crossref_primary_10_1155_2022_6872162 crossref_primary_10_1016_j_compbiomed_2024_107950 crossref_primary_10_1016_j_matcom_2022_11_020 crossref_primary_10_1155_2022_8171164 crossref_primary_10_1002_aisy_202300406 crossref_primary_10_1007_s11227_024_06649_x crossref_primary_10_1080_00207160_2020_1775820 crossref_primary_10_1016_j_eswa_2023_119898 crossref_primary_10_3233_JIFS_200101 crossref_primary_10_1007_s00500_020_05057_6 crossref_primary_10_1007_s10115_023_01931_5 crossref_primary_10_1016_j_asoc_2021_107854 crossref_primary_10_1016_j_matcom_2022_10_007 crossref_primary_10_1007_s13042_021_01326_4 crossref_primary_10_1016_j_asoc_2025_113462 crossref_primary_10_1016_j_eswa_2021_114864 crossref_primary_10_1016_j_neucom_2022_01_001 crossref_primary_10_1007_s12065_021_00610_0 crossref_primary_10_1016_j_bspc_2024_107457 crossref_primary_10_7717_peerj_cs_2935 crossref_primary_10_1016_j_eswa_2022_118831 crossref_primary_10_1016_j_eswa_2022_117866 crossref_primary_10_1016_j_compbiomed_2023_107551 crossref_primary_10_1155_2020_8882086 crossref_primary_10_1007_s10586_024_04602_4 crossref_primary_10_1016_j_asoc_2019_105744 crossref_primary_10_1038_s41598_024_56919_0 crossref_primary_10_1038_s41598_024_69734_4 crossref_primary_10_1016_j_matcom_2022_08_020 crossref_primary_10_3390_en16010024 crossref_primary_10_1016_j_eswa_2020_113882 crossref_primary_10_1016_j_rineng_2025_103951 crossref_primary_10_1007_s00607_024_01290_1 crossref_primary_10_1016_j_dajour_2022_100125 crossref_primary_10_3233_JIFS_230132 crossref_primary_10_1007_s00521_023_08229_1 crossref_primary_10_1016_j_jksuci_2023_101704 crossref_primary_10_1093_jcde_qwac119 crossref_primary_10_1007_s00521_020_05056_6 crossref_primary_10_1109_ACCESS_2020_2971249 crossref_primary_10_1007_s12065_025_01052_8 crossref_primary_10_1016_j_jestch_2020_08_011 crossref_primary_10_1016_j_swevo_2020_100821 crossref_primary_10_1016_j_asoc_2021_107675 crossref_primary_10_1155_2020_6084917 crossref_primary_10_1007_s00500_020_05227_6 crossref_primary_10_1016_j_eswa_2024_123444 crossref_primary_10_1016_j_knosys_2018_12_008 crossref_primary_10_1111_exsy_12854 crossref_primary_10_1016_j_eswa_2020_114503 crossref_primary_10_1155_2019_9517568 crossref_primary_10_1016_j_eswa_2022_117562 crossref_primary_10_1016_j_asoc_2019_105521 crossref_primary_10_1007_s12530_025_09683_z crossref_primary_10_1016_j_asoc_2022_109682 crossref_primary_10_1007_s11227_024_06291_7 crossref_primary_10_1016_j_compbiomed_2023_107212 crossref_primary_10_1016_j_eswa_2021_116417 crossref_primary_10_1109_ACCESS_2019_2948939 crossref_primary_10_7717_peerj_cs_1420 crossref_primary_10_1007_s42235_023_00336_y crossref_primary_10_1016_j_isatra_2021_11_037 crossref_primary_10_1016_j_mseb_2024_117506 crossref_primary_10_1109_ACCESS_2019_2926444 crossref_primary_10_1016_j_knosys_2022_108833 crossref_primary_10_1007_s42235_022_00185_1 crossref_primary_10_4316_AECE_2020_02008 crossref_primary_10_1007_s00500_023_09471_4 crossref_primary_10_1038_s41598_025_02154_0 crossref_primary_10_1111_coin_12272 crossref_primary_10_1016_j_compbiomed_2022_105563 crossref_primary_10_1007_s00521_022_07842_w crossref_primary_10_1007_s11227_021_04050_6 crossref_primary_10_1038_s41598_022_24840_z crossref_primary_10_1016_j_aej_2025_02_046 crossref_primary_10_1093_jcde_qwad094 crossref_primary_10_1016_j_eswa_2022_119041 crossref_primary_10_1007_s12652_021_03183_z crossref_primary_10_1016_j_compbiomed_2023_106949 crossref_primary_10_3390_sym12081234 crossref_primary_10_1016_j_asoc_2021_108071 crossref_primary_10_3390_en13010215 crossref_primary_10_1016_j_compbiomed_2021_105137 crossref_primary_10_3390_app15116064 crossref_primary_10_3390_a17050172 crossref_primary_10_1155_2021_6639671 crossref_primary_10_1007_s12652_022_03731_1 crossref_primary_10_1016_j_asoc_2021_107900 crossref_primary_10_1007_s00500_020_05425_2 crossref_primary_10_1016_j_bspc_2022_104511 crossref_primary_10_1016_j_knosys_2022_108411 crossref_primary_10_1007_s00366_021_01571_9 crossref_primary_10_1007_s00500_020_05099_w crossref_primary_10_1016_j_eswa_2023_120027 crossref_primary_10_1007_s42835_021_00666_z crossref_primary_10_1007_s00366_021_01510_8 crossref_primary_10_1007_s00521_023_09023_9 crossref_primary_10_1016_j_cma_2021_114029 crossref_primary_10_1080_19942060_2022_2098826 crossref_primary_10_1109_ACCESS_2020_2970992 crossref_primary_10_1007_s11227_023_05618_0 crossref_primary_10_1109_ACCESS_2022_3219486 crossref_primary_10_1016_j_cma_2023_116582 crossref_primary_10_1016_j_eswa_2020_113974 crossref_primary_10_1016_j_compbiomed_2025_110495 crossref_primary_10_1007_s10489_022_04201_z crossref_primary_10_1007_s10489_019_01570_w crossref_primary_10_1007_s42235_023_00386_2 crossref_primary_10_1016_j_rineng_2025_104215 crossref_primary_10_1109_ACCESS_2022_3183902 crossref_primary_10_1109_ACCESS_2024_3433483 crossref_primary_10_1007_s12652_021_03391_7 crossref_primary_10_1080_0952813X_2022_2115144 crossref_primary_10_1093_jcde_qwae044 crossref_primary_10_1016_j_asoc_2020_106651 crossref_primary_10_1016_j_asoc_2022_109828 crossref_primary_10_1016_j_advengsoft_2024_103665 crossref_primary_10_1109_ACCESS_2021_3056520 crossref_primary_10_1016_j_advengsoft_2024_103784 crossref_primary_10_1016_j_eswa_2022_119095 crossref_primary_10_1016_j_knosys_2021_107348 crossref_primary_10_1007_s00500_023_08578_y crossref_primary_10_1007_s10586_024_04753_4 crossref_primary_10_1007_s10489_023_04473_z crossref_primary_10_1016_j_swevo_2024_101779 crossref_primary_10_1155_2021_6379469 crossref_primary_10_1155_2022_6215574 crossref_primary_10_1007_s13198_023_02008_w crossref_primary_10_3390_en17112559 crossref_primary_10_1007_s13198_023_01857_9 crossref_primary_10_1007_s42235_024_00590_8 crossref_primary_10_1155_2021_6636918 crossref_primary_10_1016_j_epsr_2023_110051 crossref_primary_10_1016_j_eswa_2020_113510 crossref_primary_10_1016_j_eswa_2023_119941 crossref_primary_10_1016_j_knosys_2023_111081 crossref_primary_10_1007_s11831_024_10218_z crossref_primary_10_1016_j_cma_2023_116238 crossref_primary_10_1016_j_swevo_2023_101462 crossref_primary_10_1111_coin_12341 crossref_primary_10_1155_2020_9495281 |
| Cites_doi | 10.1016/j.ins.2012.08.023 10.1002/(SICI)1097-0207(19960315)39:5<829::AID-NME884>3.0.CO;2-U 10.1007/s12293-013-0128-0 10.1007/s00521-015-1870-7 10.1016/j.asoc.2012.11.026 10.1504/IJBIC.2010.032124 10.1016/j.eswa.2010.07.086 10.1115/1.2919393 10.1115/1.2912596 10.1016/j.swevo.2018.01.008 10.2514/2.1657 10.1109/TEVC.2004.826069 10.1016/j.knosys.2014.07.025 10.1016/j.knosys.2015.07.006 10.1007/s10898-007-9149-x 10.1016/j.eswa.2017.10.042 10.1109/4235.585893 10.1145/2480741.2480752 10.1016/j.eswa.2017.04.023 10.1016/j.swevo.2018.01.009 10.1016/j.asoc.2007.07.010 10.1016/j.advengsoft.2016.01.008 10.1016/j.swevo.2017.09.010 10.1016/j.swevo.2018.02.011 10.1016/j.advengsoft.2013.12.007 10.1007/s00521-017-2837-7 10.1023/A:1008202821328 10.1016/j.amc.2006.10.047 10.3390/sym9100203 10.1016/j.eswa.2017.04.029 10.1080/03052150108940941 10.2514/1.1711 10.1007/s00366-011-0241-y 10.1016/j.eswa.2017.07.043 10.1016/j.knosys.2015.12.022 10.1016/j.neunet.2017.10.009 10.1177/003754970107600201 10.1016/j.asoc.2015.01.004 10.1109/TEVC.2005.857610 10.2528/PIER07082403 10.1080/03052150500066737 10.1613/jair.855 10.1016/j.ins.2009.03.004 10.1016/j.eswa.2016.04.018 10.1016/j.eswa.2017.08.038 10.1016/j.advengsoft.2015.01.010 10.1016/j.isatra.2014.03.018 10.1016/j.swevo.2016.12.005 10.1016/j.ins.2018.03.042 10.1016/j.advengsoft.2017.01.004 10.1016/j.advengsoft.2017.07.002 10.1007/s00466-004-0623-8 10.1016/j.compstruc.2012.09.003 |
| ContentType | Journal Article |
| Copyright | 2018 Elsevier Ltd Copyright Elsevier BV Apr 1, 2019 |
| Copyright_xml | – notice: 2018 Elsevier Ltd – notice: Copyright Elsevier BV Apr 1, 2019 |
| DBID | AAYXX CITATION 7SC 8FD JQ2 L7M L~C L~D |
| DOI | 10.1016/j.eswa.2018.10.050 |
| DatabaseName | CrossRef Computer and Information Systems Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional |
| DatabaseTitle | CrossRef Computer and Information Systems Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Advanced Technologies Database with Aerospace ProQuest Computer Science Collection Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | Computer and Information Systems Abstracts |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1873-6793 |
| EndPage | 230 |
| ExternalDocumentID | 10_1016_j_eswa_2018_10_050 S0957417418307164 |
| GroupedDBID | --K --M .DC .~1 0R~ 13V 1B1 1RT 1~. 1~5 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN 9JO AAAKF AABNK AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AARIN AAXUO AAYFN ABBOA ABFNM ABMAC ABMVD ABUCO ABYKQ ACDAQ ACGFS ACHRH ACNTT ACRLP ACZNC ADBBV ADEZE ADTZH AEBSH AECPX AEKER AENEX AFKWA AFTJW AGHFR AGJBL AGUBO AGUMN AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJOXV ALEQD ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD APLSM AXJTR BJAXD BKOJK BLXMC BNSAS CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ HAMUX IHE J1W JJJVA KOM LG9 LY1 LY7 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. PQQKQ Q38 RIG ROL RPZ SDF SDG SDP SDS SES SPC SPCBC SSB SSD SSL SST SSV SSZ T5K TN5 ~G- 29G 9DU AAAKG AAQXK AATTM AAXKI AAYWO AAYXX ABJNI ABKBG ABUFD 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 WUQ XPP ZMT ~HD 7SC 8FD JQ2 L7M L~C L~D |
| ID | FETCH-LOGICAL-c328t-3b534bf48a9a33b8de34f8b18c93642d886677eb1deb6e3d0965710db3cd67343 |
| ISICitedReferencesCount | 250 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000456222700015&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0957-4174 |
| IngestDate | Sun Nov 09 06:12:37 EST 2025 Sat Nov 29 06:14:29 EST 2025 Tue Nov 18 19:37:07 EST 2025 Fri Feb 23 02:24:25 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Engineering application problems Self-adaptation Sine Cosine algorithm (SCA) Benchmark test problems Population based algorithms Opposition based learning |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c328t-3b534bf48a9a33b8de34f8b18c93642d886677eb1deb6e3d0965710db3cd67343 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0002-3779-2932 |
| PQID | 2172146890 |
| PQPubID | 2045477 |
| PageCount | 21 |
| ParticipantIDs | proquest_journals_2172146890 crossref_citationtrail_10_1016_j_eswa_2018_10_050 crossref_primary_10_1016_j_eswa_2018_10_050 elsevier_sciencedirect_doi_10_1016_j_eswa_2018_10_050 |
| PublicationCentury | 2000 |
| PublicationDate | 2019-04-01 2019-04-00 20190401 |
| PublicationDateYYYYMMDD | 2019-04-01 |
| PublicationDate_xml | – month: 04 year: 2019 text: 2019-04-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York |
| PublicationTitle | Expert systems with applications |
| PublicationYear | 2019 |
| Publisher | Elsevier Ltd Elsevier BV |
| Publisher_xml | – name: Elsevier Ltd – name: Elsevier BV |
| References | Yang (bib0075) 2010; 2 Deep, Thakur (bib0009) 2007; 188 Sahlol, Ewees, Hemdan, Hassanien (bib0056) 2016 December Singh, Kumar, Balyan, Singh (bib0062) 2017 Tizhoosh (bib0064) 2005 November; Vol. 1 Yang, Gao, Liu, Song (bib0076) 2015; 29 Lingyun, Mei, Guangming, Guang (bib0038) 2005; 35 Karaboga, Basturk (bib0029) 2007; 39 Formato (bib0016) 2007; 77 Kumawat, Nanda, Maddila (bib0034) 2018 Hafez, Zawbaa, Emary, Hassanien (bib0022) 2016 August Saremi, Mirjalili, Lewis (bib0077) 2017; 105 Mirjalili (bib0043) 2016; 96 Dorigo, Birattari (bib0011) 2011 Rahnamayan, Tizhoosh, Salama (bib0051) 2008; 8 Ventresca, Tizhoosh (bib0068) 2008 Gandomi (bib0017) 2014; 53 El Azi, Ewees, Hassanien (bib0013) 2017; 83 Sedaghati, Suleman, Tabarrok (bib0059) 2002; 40 Hoffmann, Nebel (bib0024) 2001; 14 Khairuzzaman, Chaudhury (bib0032) 2017; 86 Liang, Qu, Suganthan (bib0037) 2013 Eberhart, Kennedy (bib0012) 1995 October Webster, Bernhard (bib0071) 2003 Salimi (bib0057) 2015; 75 Mirjalili, Mirjalili, Hatamlou (bib0046) 2016; 27 Das, Suganthan (bib0007) 2010 Hatamlou (bib0023) 2013; 222 Grandhi (bib0021) 1993; 31 Mirjalili (bib0042) 2015; 83 Woźniak, Połap (bib0073) 2018 Črepinšek, Liu, Mernik (bib0005) 2013; 45 Djenouri, Belhadi, Belkebir (bib0010) 2018; 94 Mirjalili, Mirjalili, Lewis (bib0047) 2014; 69 Nenavath, Jatoth, Das (bib0048) 2018 Ray, Saini (bib0053) 2001; 33 Ertenlice, Kalayci (bib0015) 2018; 39 Woźniak, Połap (bib0074) 2018; 98 Kar (bib0028) 2016; 59 Mavrovouniotis, Li, Yang (bib0040) 2017; 33 Mirjalili, Gandomi, Mirjalili, Saremi, Faris, Mirjalili (bib0044) 2017; 114 Li, Fang, Liu (bib0035) 2018; 91 Mirjalili (bib0041) 2015; 89 Wang, Zhang, Jiang (bib0070) 2004; 42 Gandomi, Yang, Alavi (bib0018) 2013; 29 Wolpert, Macready (bib0072) 1997; 1 Sandgren (bib0058) 1990; 112 Połap (bib0050) 2017; 9 Chickermane, Gea (bib0004) 1996; 39 Shi, Eberhart (bib0060) 1998 May Liang, Qin, Suganthan, Baskar (bib0036) 2006; 10 Van den Bergh, Engelbrecht (bib0066) 2004; 8 Kennedy (bib0031) 2011 Sindhu, Ngadiran, Yacob, Zahri, Hariharan (bib0061) 2017; 28 Gomes (bib0020) 2011; 38 Mahdavi, Rahnamayan, Deb (bib0039) 2018; 39 Nowcki (bib0049) 1974; Vol. 2 Ismael, Aleem, Abdelaziz (bib0026) 2017 December Deb, Goyal (bib0008) 1996; 26 Kumar, Kumar (bib0033) 2017 Storn, Price (bib0063) 1997; 11 Assad, Deep (bib0001) 2018; 450 Tsai (bib0065) 2005; 37 Auger, Hansen (bib0002) 2005, September; Vol. 2 Holland (bib0025) 1992 Rashedi, Nezamabadi-Pour, Saryazdi (bib0052) 2009; 179 Mirjalili, Lewis (bib0045) 2016; 95 Reddy, Panwar, Panigrahi, Kumar (bib0054) 2017 El Aziz, Oliva, Xiong (bib0014) 2017; 90 Sadollah, Bahreininejad, Eskandar, Hamdi (bib0055) 2013; 13 Kaveh, Khayatazad (bib0030) 2012; 112 Van Laarhoven, Aarts (bib0067) 1987 Bansal, Sharma, Jadon, Clerc (bib0003) 2014; 6 Geem, Kim, Loganathan (bib0019) 2001; 76 Kannan, Kramer (bib0027) 1994; 116 Chickermane (10.1016/j.eswa.2018.10.050_bib0004) 1996; 39 Woźniak (10.1016/j.eswa.2018.10.050_bib0074) 2018; 98 Assad (10.1016/j.eswa.2018.10.050_bib0001) 2018; 450 Kar (10.1016/j.eswa.2018.10.050_bib0028) 2016; 59 Ventresca (10.1016/j.eswa.2018.10.050_bib0068) 2008 Hoffmann (10.1016/j.eswa.2018.10.050_bib0024) 2001; 14 Singh (10.1016/j.eswa.2018.10.050_bib0062) 2017 Nowcki (10.1016/j.eswa.2018.10.050_bib0049) 1974; Vol. 2 Storn (10.1016/j.eswa.2018.10.050_bib0063) 1997; 11 Nenavath (10.1016/j.eswa.2018.10.050_bib0048) 2018 Mirjalili (10.1016/j.eswa.2018.10.050_bib0042) 2015; 83 Sandgren (10.1016/j.eswa.2018.10.050_bib0058) 1990; 112 Črepinšek (10.1016/j.eswa.2018.10.050_bib0005) 2013; 45 Mavrovouniotis (10.1016/j.eswa.2018.10.050_bib0040) 2017; 33 Ray (10.1016/j.eswa.2018.10.050_bib0053) 2001; 33 Sahlol (10.1016/j.eswa.2018.10.050_bib0056) 2016 Kannan (10.1016/j.eswa.2018.10.050_bib0027) 1994; 116 Van den Bergh (10.1016/j.eswa.2018.10.050_bib0066) 2004; 8 Mirjalili (10.1016/j.eswa.2018.10.050_bib0047) 2014; 69 Van Laarhoven (10.1016/j.eswa.2018.10.050_bib0067) 1987 Kumar (10.1016/j.eswa.2018.10.050_bib0033) 2017 Deb (10.1016/j.eswa.2018.10.050_bib0008) 1996; 26 Mahdavi (10.1016/j.eswa.2018.10.050_bib0039) 2018; 39 Wolpert (10.1016/j.eswa.2018.10.050_bib0072) 1997; 1 Kennedy (10.1016/j.eswa.2018.10.050_bib0031) 2011 Liang (10.1016/j.eswa.2018.10.050_bib0036) 2006; 10 Gomes (10.1016/j.eswa.2018.10.050_bib0020) 2011; 38 Sindhu (10.1016/j.eswa.2018.10.050_bib0061) 2017; 28 Mirjalili (10.1016/j.eswa.2018.10.050_bib0043) 2016; 96 Wang (10.1016/j.eswa.2018.10.050_bib0070) 2004; 42 Kumawat (10.1016/j.eswa.2018.10.050_bib0034) 2018 Mirjalili (10.1016/j.eswa.2018.10.050_bib0041) 2015; 89 Salimi (10.1016/j.eswa.2018.10.050_bib0057) 2015; 75 Bansal (10.1016/j.eswa.2018.10.050_bib0003) 2014; 6 Yang (10.1016/j.eswa.2018.10.050_bib0076) 2015; 29 Karaboga (10.1016/j.eswa.2018.10.050_bib0029) 2007; 39 Ismael (10.1016/j.eswa.2018.10.050_bib0026) 2017 Reddy (10.1016/j.eswa.2018.10.050_bib0054) 2017 Yang (10.1016/j.eswa.2018.10.050_bib0075) 2010; 2 El Azi (10.1016/j.eswa.2018.10.050_bib0013) 2017; 83 Formato (10.1016/j.eswa.2018.10.050_bib0016) 2007; 77 El Aziz (10.1016/j.eswa.2018.10.050_bib0014) 2017; 90 Li (10.1016/j.eswa.2018.10.050_bib0035) 2018; 91 Eberhart (10.1016/j.eswa.2018.10.050_bib0012) 1995 Rahnamayan (10.1016/j.eswa.2018.10.050_bib0051) 2008; 8 Saremi (10.1016/j.eswa.2018.10.050_bib0077) 2017; 105 Gandomi (10.1016/j.eswa.2018.10.050_bib0018) 2013; 29 Hafez (10.1016/j.eswa.2018.10.050_bib0022) 2016 Auger (10.1016/j.eswa.2018.10.050_bib0002) 2005; Vol. 2 Grandhi (10.1016/j.eswa.2018.10.050_bib0021) 1993; 31 Shi (10.1016/j.eswa.2018.10.050_bib0060) 1998 Deep (10.1016/j.eswa.2018.10.050_bib0009) 2007; 188 Rashedi (10.1016/j.eswa.2018.10.050_bib0052) 2009; 179 Gandomi (10.1016/j.eswa.2018.10.050_bib0017) 2014; 53 Khairuzzaman (10.1016/j.eswa.2018.10.050_bib0032) 2017; 86 Dorigo (10.1016/j.eswa.2018.10.050_bib0011) 2011 Geem (10.1016/j.eswa.2018.10.050_bib0019) 2001; 76 Djenouri (10.1016/j.eswa.2018.10.050_bib0010) 2018; 94 Mirjalili (10.1016/j.eswa.2018.10.050_bib0046) 2016; 27 Webster (10.1016/j.eswa.2018.10.050_bib0071) 2003 Hatamlou (10.1016/j.eswa.2018.10.050_bib0023) 2013; 222 Mirjalili (10.1016/j.eswa.2018.10.050_bib0044) 2017; 114 Sedaghati (10.1016/j.eswa.2018.10.050_bib0059) 2002; 40 Das (10.1016/j.eswa.2018.10.050_bib0007) 2010 Lingyun (10.1016/j.eswa.2018.10.050_bib0038) 2005; 35 Sadollah (10.1016/j.eswa.2018.10.050_bib0055) 2013; 13 Kaveh (10.1016/j.eswa.2018.10.050_bib0030) 2012; 112 Woźniak (10.1016/j.eswa.2018.10.050_bib0073) 2018 Mirjalili (10.1016/j.eswa.2018.10.050_bib0045) 2016; 95 Tsai (10.1016/j.eswa.2018.10.050_bib0065) 2005; 37 Połap (10.1016/j.eswa.2018.10.050_bib0050) 2017; 9 Liang (10.1016/j.eswa.2018.10.050_bib0037) 2013 Tizhoosh (10.1016/j.eswa.2018.10.050_bib0064) 2005; Vol. 1 Ertenlice (10.1016/j.eswa.2018.10.050_bib0015) 2018; 39 Holland (10.1016/j.eswa.2018.10.050_bib0025) 1992 |
| References_xml | – volume: 222 start-page: 175 year: 2013 end-page: 184 ident: bib0023 article-title: Black hole: A new heuristic optimization approach for data clustering publication-title: Information Sciences – volume: 14 start-page: 253 year: 2001 end-page: 302 ident: bib0024 article-title: The FF planning system: Fast plan generation through heuristic search publication-title: Journal of Artificial Intelligence Research – volume: 10 start-page: 281 year: 2006 end-page: 295 ident: bib0036 article-title: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions publication-title: IEEE Transactions on Evolutionary Computation – volume: 112 start-page: 283 year: 2012 end-page: 294 ident: bib0030 article-title: A new meta-heuristic method: Ray optimization publication-title: Computers & Structures – start-page: 35 year: 2016 December end-page: 40 ident: bib0056 article-title: Training feedforward neural networks using Sine-Cosine algorithm to improve the prediction of liver enzymes on fish farmed on nano-selenite publication-title: Computer engineering conference (ICENCO), 2016 12th international – volume: 94 start-page: 126 year: 2018 end-page: 136 ident: bib0010 article-title: Bees swarm optimization guided by data mining techniques for document information retrieval publication-title: Expert Systems with Applications – volume: 91 start-page: 63 year: 2018 end-page: 77 ident: bib0035 article-title: Parameter optimization of support vector regression based on sine cosine algorithm publication-title: Expert Systems with Applications – start-page: 255 year: 2008 end-page: 284 ident: bib0068 article-title: Two frameworks for Improving Gradient-Based Learning Algorithms publication-title: Oppositional concepts in computational intelligence – year: 1992 ident: bib0025 article-title: Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence – volume: 86 start-page: 64 year: 2017 end-page: 76 ident: bib0032 article-title: Multilevel thresholding using grey wolf optimizer for image segmentation publication-title: Expert Systems with Applications – year: 2017 ident: bib0062 article-title: Swarm intelligence optimized piecewise gamma corrected histogram equalization for dark image enhancement publication-title: Computers & Electrical Engineering – volume: 112 start-page: 223 year: 1990 end-page: 229 ident: bib0058 article-title: Nonlinear integer and discrete programming in mechanical design optimization publication-title: Journal of Mechanical Design – start-page: 760 year: 2011 end-page: 766 ident: bib0031 article-title: Particle swarm optimization publication-title: Encyclopedia of machine learning – volume: 90 start-page: 484 year: 2017 end-page: 500 ident: bib0014 article-title: An improved opposition-based sine cosine algorithm for global optimization publication-title: Expert Systems with Applications – volume: 40 start-page: 382 year: 2002 end-page: 388 ident: bib0059 article-title: Structural optimization with frequency constraints using the finite element force method publication-title: AIAA Journal – volume: 96 start-page: 120 year: 2016 end-page: 133 ident: bib0043 article-title: SCA: A sine cosine algorithm for solving optimization problems publication-title: Knowledge-Based Systems – start-page: 131 year: 2018 end-page: 139 ident: bib0034 article-title: Positioning LED panel for uniform illuminance in indoor VLC system using whale optimization publication-title: Optical and wireless technologies – volume: 33 start-page: 735 year: 2001 end-page: 748 ident: bib0053 article-title: Engineering design optimization using a swarm with an intelligent information sharing among individuals publication-title: Engineering Optimization – volume: 45 start-page: 35 year: 2013 ident: bib0005 article-title: Exploration and exploitation in evolutionary algorithms: A survey publication-title: ACM Computing Surveys (CSUR) – volume: 1 start-page: 67 year: 1997 end-page: 82 ident: bib0072 article-title: No free lunch theorems for optimization publication-title: IEEE Transactions on Evolutionary Computation – start-page: 39 year: 1995 October end-page: 43 ident: bib0012 article-title: A new optimizer using particle swarm theory publication-title: Micro machine and human science, 1995. MHS'95, proceedings of the sixth international symposium on – volume: Vol. 2 start-page: 1769 year: 2005, September end-page: 1776 ident: bib0002 article-title: A restart CMA evolution strategy with increasing population size publication-title: Evolutionary computation, 2005. The 2005 IEEE congress on – volume: 33 start-page: 1 year: 2017 end-page: 17 ident: bib0040 article-title: A survey of swarm intelligence for dynamic optimization: Algorithms and applications publication-title: Swarm and Evolutionary Computation – volume: 9 start-page: 203 year: 2017 ident: bib0050 article-title: Polar Bear Optimization Algorithm: Meta-heuristic with fast population movement and dynamic birth and death mechanism publication-title: Symmetry – volume: 450 start-page: 246 year: 2018 end-page: 266 ident: bib0001 article-title: A Hybrid Harmony search and Simulated Annealing algorithm for continuous optimization publication-title: Information Sciences – volume: 27 start-page: 495 year: 2016 end-page: 513 ident: bib0046 article-title: Multi-verse optimizer: A nature-inspired algorithm for global optimization publication-title: Neural Computing and Applications – volume: 37 start-page: 399 year: 2005 end-page: 409 ident: bib0065 article-title: Global optimization of nonlinear fractional programming problems in engineering design publication-title: Engineering Optimization – start-page: 255 year: 2003 end-page: 261 ident: bib0071 article-title: A local search optimization algorithm based on natural principles of gravitation publication-title: Proceedings of the 2003 international conference on information and knowledge engineering (IKE’03) – volume: 11 start-page: 341 year: 1997 end-page: 359 ident: bib0063 article-title: Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces publication-title: Journal of Global Optimization – volume: 39 start-page: 829 year: 1996 end-page: 846 ident: bib0004 article-title: Structural optimization using a new local approximation method publication-title: International Journal for Numerical Methods in Engineering – volume: 29 start-page: 17 year: 2013 end-page: 35 ident: bib0018 article-title: Cuckoo search algorithm: A metaheuristic approach to solve structural optimization problems publication-title: Engineering with Computers – volume: 95 start-page: 51 year: 2016 end-page: 67 ident: bib0045 article-title: The whale optimization algorithm publication-title: Advances in Engineering Software – volume: Vol. 2 start-page: 327 year: 1974 end-page: 338 ident: bib0049 article-title: Optimization in pre-contract ship design publication-title: Computer applications in the automation of shipyard operation and ship design – volume: 35 start-page: 361 year: 2005 end-page: 368 ident: bib0038 article-title: Truss optimization on shape and sizing with frequency constraints based on genetic algorithm publication-title: Computational Mechanics – start-page: 1 year: 2017 end-page: 16 ident: bib0054 article-title: A New Binary Variant of Sine–Cosine Algorithm: Development and application to solve profit-based unit commitment problem publication-title: Arabian Journal for Science and Engineering – volume: 116 start-page: 405 year: 1994 end-page: 411 ident: bib0027 article-title: An augmented Lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical design publication-title: Journal of Mechanical Design – volume: 105 start-page: 30 year: 2017 end-page: 47 ident: bib0077 article-title: Grasshopper optimisation algorithm: theory and application publication-title: Advances in Engineering Software – volume: 188 start-page: 895 year: 2007 end-page: 911 ident: bib0009 article-title: A new crossover operator for real coded genetic algorithms publication-title: Applied Mathematics and Computation – volume: 89 start-page: 228 year: 2015 end-page: 249 ident: bib0041 article-title: Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm publication-title: Knowledge-Based Systems – volume: 38 start-page: 957 year: 2011 end-page: 968 ident: bib0020 article-title: Truss optimization with dynamic constraints using a particle swarm algorithm publication-title: Expert Systems with Applications – volume: 39 start-page: 36 year: 2018 end-page: 52 ident: bib0015 article-title: A survey of swarm intelligence for portfolio optimization: Algorithms and applications publication-title: Swarm and Evolutionary Computation – volume: 83 start-page: 80 year: 2015 end-page: 98 ident: bib0042 article-title: The ant lion optimizer publication-title: Advances in Engineering Software – volume: 42 start-page: 622 year: 2004 end-page: 630 ident: bib0070 article-title: Truss optimization on shape and sizing with frequency constraints publication-title: AIAA Journal – volume: 28 start-page: 2947 year: 2017 end-page: 2958 ident: bib0061 article-title: Sine–Cosine algorithm for feature selection with elitism strategy and new updating mechanism publication-title: Neural Computing and Applications – volume: 31 start-page: 2296 year: 1993 end-page: 2303 ident: bib0021 article-title: Structural optimization with frequency constraints-a review publication-title: AIAAJournal – start-page: 103 year: 2017 December end-page: 107 ident: bib0026 article-title: Optimal selection of conductors in Egyptian radial distribution systems using sine-cosine optimization algorithm publication-title: Power systems conference (MEPCON), 2017 nineteenth international middle east – volume: 13 start-page: 2592 year: 2013 end-page: 2612 ident: bib0055 article-title: Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems publication-title: Applied Soft Computing – volume: 53 start-page: 1168 year: 2014 end-page: 1183 ident: bib0017 article-title: Interior search algorithm (ISA): A novel approach for global optimization publication-title: ISA Transactions – volume: 29 start-page: 386 year: 2015 end-page: 394 ident: bib0076 article-title: Low-discrepancy sequence initialized particle swarm optimization algorithm with high-order nonlinear time-varying inertia weight publication-title: Applied Soft Computing – volume: 77 start-page: 425 year: 2007 end-page: 491 ident: bib0016 article-title: Central force optimization publication-title: Progress in Electromagnetics Research – year: 2013 ident: bib0037 article-title: Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization – year: 2018 ident: bib0048 article-title: A synergy of the sine-cosine algorithm and particle swarm optimizer for improved global optimization and object tracking publication-title: Swarm and Evolutionary Computation. – volume: 76 start-page: 60 year: 2001 end-page: 68 ident: bib0019 article-title: A new heuristic optimization algorithm: Harmony search publication-title: Simulation – volume: 59 start-page: 20 year: 2016 end-page: 32 ident: bib0028 article-title: Bio inspired computing–A review of algorithms and scope of applications publication-title: Expert Systems with Applications – volume: 69 start-page: 46 year: 2014 end-page: 61 ident: bib0047 article-title: Grey wolf optimizer publication-title: Advances in Engineering Software – volume: 8 start-page: 906 year: 2008 end-page: 918 ident: bib0051 article-title: Opposition versus randomness in soft computing techniques publication-title: Applied Soft Computing – year: 2010 ident: bib0007 article-title: Problem definitions and evaluation criteria for CEC 2011 competition on testing evolutionary algorithms on real world optimization problems – volume: 75 start-page: 1 year: 2015 end-page: 18 ident: bib0057 article-title: Stochastic fractal search: A powerful metaheuristic algorithm publication-title: Knowledge-Based Systems – volume: 114 start-page: 163 year: 2017 end-page: 191 ident: bib0044 article-title: Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems publication-title: Advances in Engineering Software – start-page: 69 year: 1998 May end-page: 73 ident: bib0060 article-title: A modified particle swarm optimizer publication-title: Evolutionary computation proceedings, 1998. IEEE world congress on computational intelligence, the 1998 IEEE international conference on – volume: 2 start-page: 78 year: 2010 end-page: 84 ident: bib0075 article-title: Firefly algorithm, stochastic test functions and design optimisation publication-title: International Journal of Bio-Inspired Computation – volume: Vol. 1 start-page: 695 year: 2005 November end-page: 701 ident: bib0064 article-title: Opposition-based learning: A new scheme for machine intelligence publication-title: Computational intelligence for modelling, control and automation, 2005 and international conference on intelligent agents, web technologies and internet commerce, international conference on – start-page: 7 year: 1987 end-page: 15 ident: bib0067 article-title: Simulated annealing publication-title: Simulated annealing: Theory and applications – volume: 26 start-page: 30 year: 1996 end-page: 45 ident: bib0008 article-title: A combined genetic adaptive search (GeneAS) for engineering design publication-title: Computer Science and Informatics – start-page: 1 year: 2016 August end-page: 5 ident: bib0022 article-title: Sine cosine optimization algorithm for feature selection publication-title: Innovations in intelligent systems and applications (INISTA), 2016 International Symposium on – volume: 98 start-page: 16 year: 2018 end-page: 33 ident: bib0074 article-title: Adaptive neuro-heuristic hybrid model for fruit peel defects detection publication-title: Neural Networks – volume: 179 start-page: 2232 year: 2009 end-page: 2248 ident: bib0052 article-title: GSA: A gravitational search algorithm publication-title: Information Sciences – volume: 39 start-page: 1 year: 2018 end-page: 23 ident: bib0039 article-title: Opposition based learning: A literature review publication-title: Swarm and Evolutionary Computation – start-page: 715 year: 2017 end-page: 726 ident: bib0033 article-title: Data clustering using sine cosine algorithm: Data clustering using SCA publication-title: Handbook of research on machine learning innovations and trends – volume: 8 start-page: 225 year: 2004 end-page: 239 ident: bib0066 article-title: A cooperative approach to particle swarm optimization publication-title: IEEE Transactions on Evolutionary Computation – start-page: 36 year: 2011 end-page: 39 ident: bib0011 article-title: Ant colony optimization publication-title: Encyclopedia of machine learning – volume: 39 start-page: 459 year: 2007 end-page: 471 ident: bib0029 article-title: A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm publication-title: Journal of Global Optimization – volume: 83 start-page: 242 year: 2017 end-page: 256 ident: bib0013 article-title: Whale Optimization Algorithm and Moth-Flame Optimization for multilevel thresholding image segmentation publication-title: Expert Systems with Applications – volume: 6 start-page: 31 year: 2014 end-page: 47 ident: bib0003 article-title: Spider monkey optimization algorithm for numerical optimization publication-title: Memetic Computing – year: 2018 ident: bib0073 article-title: Bio-inspired methods modeled for respiratory disease detection from medical images publication-title: Swarm and Evolutionary Computation. – volume: 222 start-page: 175 year: 2013 ident: 10.1016/j.eswa.2018.10.050_bib0023 article-title: Black hole: A new heuristic optimization approach for data clustering publication-title: Information Sciences doi: 10.1016/j.ins.2012.08.023 – volume: 39 start-page: 829 issue: 5 year: 1996 ident: 10.1016/j.eswa.2018.10.050_bib0004 article-title: Structural optimization using a new local approximation method publication-title: International Journal for Numerical Methods in Engineering doi: 10.1002/(SICI)1097-0207(19960315)39:5<829::AID-NME884>3.0.CO;2-U – volume: 6 start-page: 31 issue: 1 year: 2014 ident: 10.1016/j.eswa.2018.10.050_bib0003 article-title: Spider monkey optimization algorithm for numerical optimization publication-title: Memetic Computing doi: 10.1007/s12293-013-0128-0 – start-page: 131 year: 2018 ident: 10.1016/j.eswa.2018.10.050_bib0034 article-title: Positioning LED panel for uniform illuminance in indoor VLC system using whale optimization – volume: 27 start-page: 495 issue: 2 year: 2016 ident: 10.1016/j.eswa.2018.10.050_bib0046 article-title: Multi-verse optimizer: A nature-inspired algorithm for global optimization publication-title: Neural Computing and Applications doi: 10.1007/s00521-015-1870-7 – start-page: 39 year: 1995 ident: 10.1016/j.eswa.2018.10.050_bib0012 article-title: A new optimizer using particle swarm theory – volume: 13 start-page: 2592 issue: 5 year: 2013 ident: 10.1016/j.eswa.2018.10.050_bib0055 article-title: Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2012.11.026 – volume: 2 start-page: 78 issue: 2 year: 2010 ident: 10.1016/j.eswa.2018.10.050_bib0075 article-title: Firefly algorithm, stochastic test functions and design optimisation publication-title: International Journal of Bio-Inspired Computation doi: 10.1504/IJBIC.2010.032124 – volume: 38 start-page: 957 issue: 1 year: 2011 ident: 10.1016/j.eswa.2018.10.050_bib0020 article-title: Truss optimization with dynamic constraints using a particle swarm algorithm publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2010.07.086 – volume: 116 start-page: 405 issue: 2 year: 1994 ident: 10.1016/j.eswa.2018.10.050_bib0027 article-title: An augmented Lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical design publication-title: Journal of Mechanical Design doi: 10.1115/1.2919393 – volume: 112 start-page: 223 issue: 2 year: 1990 ident: 10.1016/j.eswa.2018.10.050_bib0058 article-title: Nonlinear integer and discrete programming in mechanical design optimization publication-title: Journal of Mechanical Design doi: 10.1115/1.2912596 – year: 2018 ident: 10.1016/j.eswa.2018.10.050_bib0073 article-title: Bio-inspired methods modeled for respiratory disease detection from medical images publication-title: Swarm and Evolutionary Computation. doi: 10.1016/j.swevo.2018.01.008 – volume: 40 start-page: 382 issue: 2 year: 2002 ident: 10.1016/j.eswa.2018.10.050_bib0059 article-title: Structural optimization with frequency constraints using the finite element force method publication-title: AIAA Journal doi: 10.2514/2.1657 – volume: 8 start-page: 225 issue: 3 year: 2004 ident: 10.1016/j.eswa.2018.10.050_bib0066 article-title: A cooperative approach to particle swarm optimization publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2004.826069 – volume: 75 start-page: 1 year: 2015 ident: 10.1016/j.eswa.2018.10.050_bib0057 article-title: Stochastic fractal search: A powerful metaheuristic algorithm publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2014.07.025 – volume: 89 start-page: 228 year: 2015 ident: 10.1016/j.eswa.2018.10.050_bib0041 article-title: Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2015.07.006 – volume: Vol. 2 start-page: 327 year: 1974 ident: 10.1016/j.eswa.2018.10.050_bib0049 article-title: Optimization in pre-contract ship design – volume: 39 start-page: 459 issue: 3 year: 2007 ident: 10.1016/j.eswa.2018.10.050_bib0029 article-title: A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm publication-title: Journal of Global Optimization doi: 10.1007/s10898-007-9149-x – volume: 94 start-page: 126 year: 2018 ident: 10.1016/j.eswa.2018.10.050_bib0010 article-title: Bees swarm optimization guided by data mining techniques for document information retrieval publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2017.10.042 – volume: 1 start-page: 67 issue: 1 year: 1997 ident: 10.1016/j.eswa.2018.10.050_bib0072 article-title: No free lunch theorems for optimization publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/4235.585893 – volume: 45 start-page: 35 issue: 3 year: 2013 ident: 10.1016/j.eswa.2018.10.050_bib0005 article-title: Exploration and exploitation in evolutionary algorithms: A survey publication-title: ACM Computing Surveys (CSUR) doi: 10.1145/2480741.2480752 – start-page: 255 year: 2008 ident: 10.1016/j.eswa.2018.10.050_bib0068 article-title: Two frameworks for Improving Gradient-Based Learning Algorithms – volume: Vol. 1 start-page: 695 year: 2005 ident: 10.1016/j.eswa.2018.10.050_bib0064 article-title: Opposition-based learning: A new scheme for machine intelligence – volume: 83 start-page: 242 year: 2017 ident: 10.1016/j.eswa.2018.10.050_bib0013 article-title: Whale Optimization Algorithm and Moth-Flame Optimization for multilevel thresholding image segmentation publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2017.04.023 – volume: 39 start-page: 36 year: 2018 ident: 10.1016/j.eswa.2018.10.050_bib0015 article-title: A survey of swarm intelligence for portfolio optimization: Algorithms and applications publication-title: Swarm and Evolutionary Computation doi: 10.1016/j.swevo.2018.01.009 – volume: 8 start-page: 906 issue: 2 year: 2008 ident: 10.1016/j.eswa.2018.10.050_bib0051 article-title: Opposition versus randomness in soft computing techniques publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2007.07.010 – year: 2017 ident: 10.1016/j.eswa.2018.10.050_bib0062 article-title: Swarm intelligence optimized piecewise gamma corrected histogram equalization for dark image enhancement publication-title: Computers & Electrical Engineering – volume: 95 start-page: 51 year: 2016 ident: 10.1016/j.eswa.2018.10.050_bib0045 article-title: The whale optimization algorithm publication-title: Advances in Engineering Software doi: 10.1016/j.advengsoft.2016.01.008 – volume: 31 start-page: 2296 issue: 12 year: 1993 ident: 10.1016/j.eswa.2018.10.050_bib0021 article-title: Structural optimization with frequency constraints-a review publication-title: AIAAJournal – volume: 39 start-page: 1 year: 2018 ident: 10.1016/j.eswa.2018.10.050_bib0039 article-title: Opposition based learning: A literature review publication-title: Swarm and Evolutionary Computation doi: 10.1016/j.swevo.2017.09.010 – year: 2018 ident: 10.1016/j.eswa.2018.10.050_bib0048 article-title: A synergy of the sine-cosine algorithm and particle swarm optimizer for improved global optimization and object tracking publication-title: Swarm and Evolutionary Computation. doi: 10.1016/j.swevo.2018.02.011 – year: 2010 ident: 10.1016/j.eswa.2018.10.050_bib0007 – start-page: 1 year: 2016 ident: 10.1016/j.eswa.2018.10.050_bib0022 article-title: Sine cosine optimization algorithm for feature selection – start-page: 103 year: 2017 ident: 10.1016/j.eswa.2018.10.050_bib0026 article-title: Optimal selection of conductors in Egyptian radial distribution systems using sine-cosine optimization algorithm – start-page: 7 year: 1987 ident: 10.1016/j.eswa.2018.10.050_bib0067 article-title: Simulated annealing – start-page: 35 year: 2016 ident: 10.1016/j.eswa.2018.10.050_bib0056 article-title: Training feedforward neural networks using Sine-Cosine algorithm to improve the prediction of liver enzymes on fish farmed on nano-selenite – year: 2013 ident: 10.1016/j.eswa.2018.10.050_bib0037 – volume: 69 start-page: 46 year: 2014 ident: 10.1016/j.eswa.2018.10.050_bib0047 article-title: Grey wolf optimizer publication-title: Advances in Engineering Software doi: 10.1016/j.advengsoft.2013.12.007 – volume: 28 start-page: 2947 issue: 10 year: 2017 ident: 10.1016/j.eswa.2018.10.050_bib0061 article-title: Sine–Cosine algorithm for feature selection with elitism strategy and new updating mechanism publication-title: Neural Computing and Applications doi: 10.1007/s00521-017-2837-7 – volume: 11 start-page: 341 issue: 4 year: 1997 ident: 10.1016/j.eswa.2018.10.050_bib0063 article-title: Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces publication-title: Journal of Global Optimization doi: 10.1023/A:1008202821328 – volume: 188 start-page: 895 issue: 1 year: 2007 ident: 10.1016/j.eswa.2018.10.050_bib0009 article-title: A new crossover operator for real coded genetic algorithms publication-title: Applied Mathematics and Computation doi: 10.1016/j.amc.2006.10.047 – volume: 9 start-page: 203 issue: 10 year: 2017 ident: 10.1016/j.eswa.2018.10.050_bib0050 article-title: Polar Bear Optimization Algorithm: Meta-heuristic with fast population movement and dynamic birth and death mechanism publication-title: Symmetry doi: 10.3390/sym9100203 – volume: 26 start-page: 30 year: 1996 ident: 10.1016/j.eswa.2018.10.050_bib0008 article-title: A combined genetic adaptive search (GeneAS) for engineering design publication-title: Computer Science and Informatics – volume: 86 start-page: 64 year: 2017 ident: 10.1016/j.eswa.2018.10.050_bib0032 article-title: Multilevel thresholding using grey wolf optimizer for image segmentation publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2017.04.029 – volume: 33 start-page: 735 issue: 6 year: 2001 ident: 10.1016/j.eswa.2018.10.050_bib0053 article-title: Engineering design optimization using a swarm with an intelligent information sharing among individuals publication-title: Engineering Optimization doi: 10.1080/03052150108940941 – volume: 42 start-page: 622 issue: 3 year: 2004 ident: 10.1016/j.eswa.2018.10.050_bib0070 article-title: Truss optimization on shape and sizing with frequency constraints publication-title: AIAA Journal doi: 10.2514/1.1711 – volume: 29 start-page: 17 issue: 1 year: 2013 ident: 10.1016/j.eswa.2018.10.050_bib0018 article-title: Cuckoo search algorithm: A metaheuristic approach to solve structural optimization problems publication-title: Engineering with Computers doi: 10.1007/s00366-011-0241-y – volume: 90 start-page: 484 year: 2017 ident: 10.1016/j.eswa.2018.10.050_bib0014 article-title: An improved opposition-based sine cosine algorithm for global optimization publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2017.07.043 – volume: 96 start-page: 120 year: 2016 ident: 10.1016/j.eswa.2018.10.050_bib0043 article-title: SCA: A sine cosine algorithm for solving optimization problems publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2015.12.022 – volume: 98 start-page: 16 year: 2018 ident: 10.1016/j.eswa.2018.10.050_bib0074 article-title: Adaptive neuro-heuristic hybrid model for fruit peel defects detection publication-title: Neural Networks doi: 10.1016/j.neunet.2017.10.009 – volume: 76 start-page: 60 issue: 2 year: 2001 ident: 10.1016/j.eswa.2018.10.050_bib0019 article-title: A new heuristic optimization algorithm: Harmony search publication-title: Simulation doi: 10.1177/003754970107600201 – start-page: 36 year: 2011 ident: 10.1016/j.eswa.2018.10.050_bib0011 article-title: Ant colony optimization – start-page: 760 year: 2011 ident: 10.1016/j.eswa.2018.10.050_bib0031 article-title: Particle swarm optimization – volume: 29 start-page: 386 year: 2015 ident: 10.1016/j.eswa.2018.10.050_bib0076 article-title: Low-discrepancy sequence initialized particle swarm optimization algorithm with high-order nonlinear time-varying inertia weight publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2015.01.004 – volume: 10 start-page: 281 issue: 3 year: 2006 ident: 10.1016/j.eswa.2018.10.050_bib0036 article-title: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2005.857610 – volume: 77 start-page: 425 year: 2007 ident: 10.1016/j.eswa.2018.10.050_bib0016 article-title: Central force optimization publication-title: Progress in Electromagnetics Research doi: 10.2528/PIER07082403 – start-page: 1 year: 2017 ident: 10.1016/j.eswa.2018.10.050_bib0054 article-title: A New Binary Variant of Sine–Cosine Algorithm: Development and application to solve profit-based unit commitment problem publication-title: Arabian Journal for Science and Engineering – volume: 37 start-page: 399 issue: 4 year: 2005 ident: 10.1016/j.eswa.2018.10.050_bib0065 article-title: Global optimization of nonlinear fractional programming problems in engineering design publication-title: Engineering Optimization doi: 10.1080/03052150500066737 – volume: 14 start-page: 253 year: 2001 ident: 10.1016/j.eswa.2018.10.050_bib0024 article-title: The FF planning system: Fast plan generation through heuristic search publication-title: Journal of Artificial Intelligence Research doi: 10.1613/jair.855 – year: 1992 ident: 10.1016/j.eswa.2018.10.050_bib0025 – volume: 179 start-page: 2232 issue: 13 year: 2009 ident: 10.1016/j.eswa.2018.10.050_bib0052 article-title: GSA: A gravitational search algorithm publication-title: Information Sciences doi: 10.1016/j.ins.2009.03.004 – volume: Vol. 2 start-page: 1769 year: 2005 ident: 10.1016/j.eswa.2018.10.050_bib0002 article-title: A restart CMA evolution strategy with increasing population size – volume: 59 start-page: 20 year: 2016 ident: 10.1016/j.eswa.2018.10.050_bib0028 article-title: Bio inspired computing–A review of algorithms and scope of applications publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2016.04.018 – volume: 91 start-page: 63 year: 2018 ident: 10.1016/j.eswa.2018.10.050_bib0035 article-title: Parameter optimization of support vector regression based on sine cosine algorithm publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2017.08.038 – start-page: 255 year: 2003 ident: 10.1016/j.eswa.2018.10.050_bib0071 article-title: A local search optimization algorithm based on natural principles of gravitation – volume: 83 start-page: 80 year: 2015 ident: 10.1016/j.eswa.2018.10.050_bib0042 article-title: The ant lion optimizer publication-title: Advances in Engineering Software doi: 10.1016/j.advengsoft.2015.01.010 – volume: 53 start-page: 1168 issue: 4 year: 2014 ident: 10.1016/j.eswa.2018.10.050_bib0017 article-title: Interior search algorithm (ISA): A novel approach for global optimization publication-title: ISA Transactions doi: 10.1016/j.isatra.2014.03.018 – volume: 33 start-page: 1 year: 2017 ident: 10.1016/j.eswa.2018.10.050_bib0040 article-title: A survey of swarm intelligence for dynamic optimization: Algorithms and applications publication-title: Swarm and Evolutionary Computation doi: 10.1016/j.swevo.2016.12.005 – start-page: 69 year: 1998 ident: 10.1016/j.eswa.2018.10.050_bib0060 article-title: A modified particle swarm optimizer – volume: 450 start-page: 246 year: 2018 ident: 10.1016/j.eswa.2018.10.050_bib0001 article-title: A Hybrid Harmony search and Simulated Annealing algorithm for continuous optimization publication-title: Information Sciences doi: 10.1016/j.ins.2018.03.042 – volume: 105 start-page: 30 year: 2017 ident: 10.1016/j.eswa.2018.10.050_bib0077 article-title: Grasshopper optimisation algorithm: theory and application publication-title: Advances in Engineering Software doi: 10.1016/j.advengsoft.2017.01.004 – volume: 114 start-page: 163 year: 2017 ident: 10.1016/j.eswa.2018.10.050_bib0044 article-title: Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems publication-title: Advances in Engineering Software doi: 10.1016/j.advengsoft.2017.07.002 – start-page: 715 year: 2017 ident: 10.1016/j.eswa.2018.10.050_bib0033 article-title: Data clustering using sine cosine algorithm: Data clustering using SCA – volume: 35 start-page: 361 issue: 5 year: 2005 ident: 10.1016/j.eswa.2018.10.050_bib0038 article-title: Truss optimization on shape and sizing with frequency constraints based on genetic algorithm publication-title: Computational Mechanics doi: 10.1007/s00466-004-0623-8 – volume: 112 start-page: 283 year: 2012 ident: 10.1016/j.eswa.2018.10.050_bib0030 article-title: A new meta-heuristic method: Ray optimization publication-title: Computers & Structures doi: 10.1016/j.compstruc.2012.09.003 |
| SSID | ssj0017007 |
| Score | 2.6505392 |
| Snippet | •A new method to solve global optimization and engineering problems called m-SCA.•The m-SCA improves the SCA using self-adaptation and opposition based... Real-world optimization problems demand an efficient meta-heuristic algorithm which maintains the diversity of solutions and properly exploits the search space... |
| SourceID | proquest crossref elsevier |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 210 |
| SubjectTerms | Adaptive algorithms Algorithms Benchmark test problems Benchmarks Engineering application problems Expert systems Global optimization Heuristic Heuristic methods Machine learning Opposition based learning Optimization Population (statistical) Population based algorithms Searching Self-adaptation Sine Cosine algorithm (SCA) Stagnation Trigonometric functions |
| Title | A hybrid self-adaptive sine cosine algorithm with opposition based learning |
| URI | https://dx.doi.org/10.1016/j.eswa.2018.10.050 https://www.proquest.com/docview/2172146890 |
| Volume | 119 |
| WOSCitedRecordID | wos000456222700015&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: 1873-6793 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017007 issn: 0957-4174 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3Na9swFBeh3WGXfY917YYOuxkXOZIt-RhGx7ZAGayD3IS-3LSkjomdrvvvK1mSk2a0rIddbCNsIfv3_N7T-wTgk91VSS0ZSUlmREqUEKkQRqdYZDhHSpoCib7ZBD09ZbNZ-WM0msZcmOsFrWt2c1M2_xVqO2bBdqmzj4B7mNQO2GsLuj1a2O3xn4CfJPM_Lg0rac2iSoUWTR8d5OLbE7XsT2JxvlxddPMrb4ZdNjF0K3FCTcdWEud3zPauJnIXKj_HnLgt7_cQyLNuvEL6c76Wc3E1aMrG9K3wpus2VH8Itoas3ApRiUZDalH1fXUG_hl4XuSAaEuYjr3T5S8-7U0Gl8em_e2KP2Xs2IXY5WgjlaInfkdYDSGEMTrtkrs5uJvDDnDk7Df7Y5qXlkvvT76dzL4PTiWKfPZ8fImQQ-XD_XZXcp-esiOxezXk7AV4FvYPcOJxfwlGpn4FnsfeHDCw6tdgOoGeDOAdMoAOf-jJAA5kAB2acEMGsCcDGMngDfj15eTs89c0NM5IFR6zLsUyx0RWhIlSYCyZNphUTGZMldjuNzVjRUGpldLayMJg7SoAWU1TS6x0QTHBb8FevazNOwCFc8yTSmSMKZKjggmD7GxWvuZjqvLqAGTxM3EVqsq75iYLfj9AByAZnml8TZUH787j1-dBK_TaHrfE9OBzRxEqHn7Plrt2bC7ZsETvH7WIQ_B08z8cgb1utTYfwBN13V20q4-B0G4BlryO_Q |
| 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=A+hybrid+self-adaptive+sine+cosine+algorithm+with+opposition+based+learning&rft.jtitle=Expert+systems+with+applications&rft.au=Gupta%2C+Shubham&rft.au=Deep%2C+Kusum&rft.date=2019-04-01&rft.issn=0957-4174&rft.volume=119&rft.spage=210&rft.epage=230&rft_id=info:doi/10.1016%2Fj.eswa.2018.10.050&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_eswa_2018_10_050 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0957-4174&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0957-4174&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0957-4174&client=summon |