Bat algorithm for constrained optimization tasks

In this study, we use a new metaheuristic optimization algorithm, called bat algorithm (BA), to solve constraint optimization tasks. BA is verified using several classical benchmark constraint problems. For further validation, BA is applied to three benchmark constraint engineering problems reported...

Full description

Saved in:

Bibliographic Details
Published in:	Neural computing & applications Vol. 22; no. 6; pp. 1239 - 1255
Main Authors:	Gandomi, Amir Hossein, Yang, Xin-She, Alavi, Amir Hossein, Talatahari, Siamak
Format:	Journal Article
Language:	English
Published:	London Springer-Verlag 01.05.2013 Springer
Subjects:	Applied sciences Artificial Intelligence Calculus of variations and optimal control Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Computer science; control theory; systems Data Mining and Knowledge Discovery Exact sciences and technology Image Processing and Computer Vision Learning and adaptive systems Mathematical analysis Mathematics Numerical analysis Numerical analysis. Scientific computation Numerical methods in mathematical programming, optimization and calculus of variations Numerical methods in optimization and calculus of variations Original Article Probability and Statistics in Computer Science Sciences and techniques of general use Constraint optimization Metaheuristic algorithm Bat algorithm Engineering Neural computation Algorithm performance Optimal solution Optimization method Benchmarks Neural network Algorithm Constrained optimization
ISSN:	0941-0643, 1433-3058
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	In this study, we use a new metaheuristic optimization algorithm, called bat algorithm (BA), to solve constraint optimization tasks. BA is verified using several classical benchmark constraint problems. For further validation, BA is applied to three benchmark constraint engineering problems reported in the specialized literature. The performance of the bat algorithm is compared with various existing algorithms. The optimal solutions obtained by BA are found to be better than the best solutions provided by the existing methods. Finally, the unique search features used in BA are analyzed, and their implications for future research are discussed in detail.
AbstractList	In this study, we use a new metaheuristic optimization algorithm, called bat algorithm (BA), to solve constraint optimization tasks. BA is verified using several classical benchmark constraint problems. For further validation, BA is applied to three benchmark constraint engineering problems reported in the specialized literature. The performance of the bat algorithm is compared with various existing algorithms. The optimal solutions obtained by BA are found to be better than the best solutions provided by the existing methods. Finally, the unique search features used in BA are analyzed, and their implications for future research are discussed in detail.
Author	Yang, Xin-She Gandomi, Amir Hossein Alavi, Amir Hossein Talatahari, Siamak
Author_xml	– sequence: 1 givenname: Amir Hossein surname: Gandomi fullname: Gandomi, Amir Hossein email: a.h.gandomi@gmail.com organization: Young Researchers Club, Central Tehran Branch, Islamic Azad University – sequence: 2 givenname: Xin-She surname: Yang fullname: Yang, Xin-She organization: Mathematics and Scientific Computing, National Physical Laboratory – sequence: 3 givenname: Amir Hossein surname: Alavi fullname: Alavi, Amir Hossein organization: Young Researchers Club, Mashhad Branch, Islamic Azad University – sequence: 4 givenname: Siamak surname: Talatahari fullname: Talatahari, Siamak organization: Marand Faculty of Engineering, University of Tabriz
BackLink	http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=27623550$$DView record in Pascal Francis
BookMark	eNp9kD1PwzAQhi0EEm3hB7BlYTSc4484I1R8SZVYYLYcxy4uqV3ZZoBfT0pgYeh0w73Pq7tnjo5DDBahCwJXBKC5zgC8JhhIjQnUErdHaEYYpZgCl8doBi0bt4LRUzTPeQMATEg-Q3CrS6WHdUy-vG0rF1NlYsglaR9sX8Vd8Vv_pYuPoSo6v-czdOL0kO3571yg1_u7l-UjXj0_PC1vVthQyQpuZN-J3khOWmC6dgQ666ixjAhmGTOiI66XpgUBQvRAed9w28megmuIbBhdoMupd6ez0YNLOhif1S75rU6fqm5ETTmHMddMOZNizsk6ZXz5uXf_w6AIqL0gNQlSoyC1F6TakST_yL_yQ0w9MXnMhrVNahM_UhhFHIC-AXakeP8
CitedBy_id	crossref_primary_10_1007_s00366_020_00979_z crossref_primary_10_1016_j_advengsoft_2022_103363 crossref_primary_10_1108_WJE_10_2020_0495 crossref_primary_10_1515_jisys_2015_0032 crossref_primary_10_1007_s00521_016_2799_1 crossref_primary_10_1061__ASCE_CP_1943_5487_0001042 crossref_primary_10_1007_s00521_019_04455_8 crossref_primary_10_3233_IDA_205455 crossref_primary_10_1007_s42235_023_00437_8 crossref_primary_10_1016_j_fluid_2013_09_066 crossref_primary_10_1108_COMPEL_01_2019_0010 crossref_primary_10_1007_s00521_015_1874_3 crossref_primary_10_1109_ACCESS_2022_3186308 crossref_primary_10_1109_TVT_2021_3124076 crossref_primary_10_1016_j_advengsoft_2015_11_004 crossref_primary_10_1007_s00521_020_05425_1 crossref_primary_10_1007_s12065_022_00714_1 crossref_primary_10_1016_j_eswa_2019_112945 crossref_primary_10_1007_s00366_018_0636_0 crossref_primary_10_1016_j_compstruc_2020_106395 crossref_primary_10_3390_app151810257 crossref_primary_10_1155_2020_4795793 crossref_primary_10_1007_s00366_016_0457_y crossref_primary_10_1016_j_eswa_2022_117735 crossref_primary_10_1016_j_istruc_2022_11_118 crossref_primary_10_1007_s42107_021_00374_z crossref_primary_10_1007_s10489_021_02980_5 crossref_primary_10_1007_s10489_021_03080_0 crossref_primary_10_1177_1729881417705969 crossref_primary_10_1016_j_cie_2020_106560 crossref_primary_10_1002_cpe_7906 crossref_primary_10_1007_s00366_018_00696_8 crossref_primary_10_1016_j_asoc_2022_108640 crossref_primary_10_1108_EC_07_2014_0158 crossref_primary_10_1007_s00500_018_3579_x crossref_primary_10_1016_j_eswa_2015_04_026 crossref_primary_10_1016_j_knosys_2018_06_013 crossref_primary_10_1016_j_fluid_2015_05_037 crossref_primary_10_1016_j_apm_2020_12_021 crossref_primary_10_1111_coin_12209 crossref_primary_10_1007_s00521_013_1508_6 crossref_primary_10_1016_j_amc_2015_11_036 crossref_primary_10_3390_math8091415 crossref_primary_10_1016_j_bica_2016_09_003 crossref_primary_10_1016_j_matpr_2018_03_044 crossref_primary_10_1016_j_eswa_2021_115496 crossref_primary_10_1007_s00521_024_09514_3 crossref_primary_10_1155_2017_8342694 crossref_primary_10_1007_s10845_018_1419_6 crossref_primary_10_1016_j_eswa_2019_112838 crossref_primary_10_1007_s00521_014_1577_1 crossref_primary_10_1007_s40997_016_0066_9 crossref_primary_10_3390_sym14020331 crossref_primary_10_1145_3382772 crossref_primary_10_1016_j_compbiolchem_2017_08_015 crossref_primary_10_1007_s00366_021_01542_0 crossref_primary_10_3390_app9020265 crossref_primary_10_1093_jcde_qwab082 crossref_primary_10_3390_math10193466 crossref_primary_10_1007_s42235_017_0010_8 crossref_primary_10_1016_j_physa_2019_122687 crossref_primary_10_1109_ACCESS_2021_3113323 crossref_primary_10_1007_s10586_025_05134_1 crossref_primary_10_1016_j_engappai_2019_103457 crossref_primary_10_32604_cmc_2022_017789 crossref_primary_10_1007_s11042_022_13532_5 crossref_primary_10_1016_j_knosys_2019_07_007 crossref_primary_10_1016_j_jocs_2013_10_002 crossref_primary_10_1016_j_eswa_2021_116026 crossref_primary_10_3390_math8101749 crossref_primary_10_1007_s10489_018_1301_4 crossref_primary_10_1016_j_asoc_2021_108343 crossref_primary_10_1016_j_eswa_2019_05_035 crossref_primary_10_1109_ACCESS_2020_2994984 crossref_primary_10_1016_j_jcde_2017_12_006 crossref_primary_10_1364_AO_58_002226 crossref_primary_10_3390_biomimetics8060462 crossref_primary_10_1007_s00500_015_1646_0 crossref_primary_10_1007_s00366_021_01591_5 crossref_primary_10_1007_s13369_016_2270_8 crossref_primary_10_1016_j_asoc_2015_06_056 crossref_primary_10_1016_j_ijepes_2014_12_063 crossref_primary_10_1016_j_knosys_2021_107555 crossref_primary_10_1002_widm_1138 crossref_primary_10_1080_15502287_2018_1431735 crossref_primary_10_1007_s00521_022_07369_0 crossref_primary_10_1007_s13369_020_04896_7 crossref_primary_10_1080_00207721_2023_2293687 crossref_primary_10_1155_2014_237102 crossref_primary_10_1002_tal_1151 crossref_primary_10_1155_2014_878262 crossref_primary_10_1016_j_ins_2014_02_123 crossref_primary_10_1155_2019_1805198 crossref_primary_10_1016_j_advengsoft_2017_03_014 crossref_primary_10_1007_s12652_021_03372_w crossref_primary_10_1007_s00500_017_2665_9 crossref_primary_10_1007_s00500_017_2952_5 crossref_primary_10_1016_j_datak_2022_102120 crossref_primary_10_1007_s00366_017_0562_6 crossref_primary_10_1007_s10845_022_01921_4 crossref_primary_10_1016_j_apm_2020_01_002 crossref_primary_10_3390_math10193604 crossref_primary_10_1016_j_bspc_2023_105681 crossref_primary_10_1016_j_knosys_2014_07_025 crossref_primary_10_1155_2013_742653 crossref_primary_10_1109_TASE_2019_2909866 crossref_primary_10_1007_s00521_015_1978_9 crossref_primary_10_1007_s11063_015_9465_y crossref_primary_10_3390_math10132211 crossref_primary_10_1016_j_seta_2021_101310 crossref_primary_10_1016_j_soildyn_2016_10_019 crossref_primary_10_3390_biomimetics8050441 crossref_primary_10_1007_s00500_023_09147_z crossref_primary_10_1007_s00521_019_04039_6 crossref_primary_10_1108_MMMS_07_2015_0035 crossref_primary_10_3390_mca23010014 crossref_primary_10_1049_iet_spr_2016_0010 crossref_primary_10_1007_s12206_015_0730_9 crossref_primary_10_1016_j_omega_2017_12_003 crossref_primary_10_1007_s42452_020_1993_z crossref_primary_10_1007_s00500_019_03806_w crossref_primary_10_1111_exsy_13170 crossref_primary_10_1007_s00521_013_1485_9 crossref_primary_10_1007_s11227_024_06817_z crossref_primary_10_1007_s00500_022_07283_6 crossref_primary_10_1007_s10916_018_1092_5 crossref_primary_10_1007_s10489_019_01558_6 crossref_primary_10_1016_j_asoc_2016_12_003 crossref_primary_10_1111_exsy_12779 crossref_primary_10_1016_j_engappai_2017_10_024 crossref_primary_10_1016_j_future_2020_03_055 crossref_primary_10_1080_0305215X_2018_1461853 crossref_primary_10_1007_s00521_016_2476_4 crossref_primary_10_1007_s13369_018_3188_0 crossref_primary_10_1155_2016_6097484 crossref_primary_10_1007_s10489_021_02510_3 crossref_primary_10_1016_j_eswa_2018_03_015 crossref_primary_10_1007_s00521_021_06747_4 crossref_primary_10_1007_s00500_019_04410_8 crossref_primary_10_1007_s10489_020_01947_2 crossref_primary_10_1016_j_rineng_2025_106256 crossref_primary_10_2166_ws_2022_010 crossref_primary_10_1177_01423312241284316 crossref_primary_10_1007_s40747_017_0050_z crossref_primary_10_1007_s10586_021_03247_x crossref_primary_10_1016_j_seppur_2023_124689 crossref_primary_10_1016_j_engappai_2013_11_008 crossref_primary_10_1155_2014_630280 crossref_primary_10_1155_2013_125625 crossref_primary_10_1016_j_apm_2018_03_005 crossref_primary_10_1007_s11277_019_06752_w crossref_primary_10_3390_math13091500 crossref_primary_10_1016_j_apm_2015_10_040 crossref_primary_10_1080_15325008_2021_1910381 crossref_primary_10_1109_ACCESS_2025_3525603 crossref_primary_10_1007_s11276_022_02982_2 crossref_primary_10_1007_s00521_017_3252_9 crossref_primary_10_1080_02533839_2020_1838949 crossref_primary_10_1109_ACCESS_2022_3146374 crossref_primary_10_1007_s10462_022_10173_w crossref_primary_10_1016_j_neucom_2014_07_034 crossref_primary_10_1016_j_eswa_2023_122830 crossref_primary_10_1007_s10479_018_3110_x crossref_primary_10_1109_ACCESS_2024_3390670 crossref_primary_10_1007_s00521_014_1624_y crossref_primary_10_1007_s11063_015_9463_0 crossref_primary_10_1016_j_advengsoft_2013_10_003 crossref_primary_10_1007_s10462_017_9559_1 crossref_primary_10_1016_j_asoc_2015_08_052 crossref_primary_10_1007_s00521_013_1471_2 crossref_primary_10_1016_j_eswa_2016_04_018 crossref_primary_10_1007_s00500_020_04853_4 crossref_primary_10_1016_j_egypro_2019_04_026 crossref_primary_10_1016_j_cja_2020_11_022 crossref_primary_10_1007_s00500_014_1502_7 crossref_primary_10_1016_j_dajour_2022_100125 crossref_primary_10_1155_2017_3498363 crossref_primary_10_1016_j_cma_2023_116097 crossref_primary_10_3390_pr9050859 crossref_primary_10_1007_s00521_018_3907_1 crossref_primary_10_1016_j_eswa_2014_04_015 crossref_primary_10_1016_j_apm_2019_03_031 crossref_primary_10_1016_j_knosys_2020_106552 crossref_primary_10_3390_ijms19103178 crossref_primary_10_1007_s00521_023_08229_1 crossref_primary_10_1016_j_cie_2019_106040 crossref_primary_10_1007_s00500_018_3381_9 crossref_primary_10_1007_s42107_022_00549_2 crossref_primary_10_1016_j_future_2019_02_028 crossref_primary_10_1093_jcde_qwac112 crossref_primary_10_1007_s12065_025_01052_8 crossref_primary_10_1260_1369_4332_18_7_1003 crossref_primary_10_3390_pr10020197 crossref_primary_10_1108_EC_11_2016_0408 crossref_primary_10_1016_j_swevo_2025_101891 crossref_primary_10_4018_IJEOE_2019070107 crossref_primary_10_1080_0305215X_2015_1076402 crossref_primary_10_1016_j_ijepes_2021_106800 crossref_primary_10_1016_j_neucom_2017_07_039 crossref_primary_10_1007_s00521_016_2308_6 crossref_primary_10_3390_math11112501 crossref_primary_10_1080_0954898X_2024_2367481 crossref_primary_10_1016_j_energy_2016_08_079 crossref_primary_10_1515_geo_2019_0031 crossref_primary_10_1007_s11042_021_11495_7 crossref_primary_10_1016_j_asoc_2016_09_030 crossref_primary_10_3390_a11050071 crossref_primary_10_1155_2015_194792 crossref_primary_10_1007_s00500_018_3586_y crossref_primary_10_1007_s00366_020_01025_8 crossref_primary_10_1007_s12145_020_00443_y crossref_primary_10_3390_electronics10050597 crossref_primary_10_3389_fenrg_2025_1625288 crossref_primary_10_3390_pr7090550 crossref_primary_10_1016_j_cie_2021_107408 crossref_primary_10_1109_TSMC_2015_2461188 crossref_primary_10_1016_j_asoc_2015_11_015 crossref_primary_10_1109_ACCESS_2021_3052800 crossref_primary_10_1007_s10845_020_01723_6 crossref_primary_10_1007_s11831_022_09817_5 crossref_primary_10_1007_s10772_019_09631_8 crossref_primary_10_1016_j_matcom_2022_12_022 crossref_primary_10_1007_s00521_019_04151_7 crossref_primary_10_1016_j_apm_2020_03_024 crossref_primary_10_1016_j_ins_2015_11_032 crossref_primary_10_1155_2022_7339647 crossref_primary_10_4028_www_scientific_net_AMM_635_637_270 crossref_primary_10_1007_s00500_015_1726_1 crossref_primary_10_1016_j_ijleo_2022_169731 crossref_primary_10_1155_2019_2981282 crossref_primary_10_1016_j_eswa_2022_117669 crossref_primary_10_1016_j_matpr_2020_04_679 crossref_primary_10_1109_ACCESS_2022_3173401 crossref_primary_10_1007_s11042_020_10255_3 crossref_primary_10_3390_jmse11081593 crossref_primary_10_1007_s10462_019_09704_9 crossref_primary_10_1016_j_jobe_2021_103848 crossref_primary_10_3390_math9182335 crossref_primary_10_1080_23302674_2024_2381828 crossref_primary_10_1155_2021_6639671 crossref_primary_10_1109_ACCESS_2019_2907289 crossref_primary_10_1016_j_compstruc_2016_01_008 crossref_primary_10_1016_j_neucom_2015_11_018 crossref_primary_10_1016_j_asoc_2022_108717 crossref_primary_10_1016_j_isatra_2019_12_001 crossref_primary_10_1007_s00521_016_2797_3 crossref_primary_10_1007_s42452_020_2426_8 crossref_primary_10_1093_jcde_qwae050 crossref_primary_10_1007_s00521_018_3785_6 crossref_primary_10_1155_2018_6421717 crossref_primary_10_5504_BBEQ_2013_0089 crossref_primary_10_1007_s00521_015_1914_z crossref_primary_10_1016_j_asoc_2017_11_032 crossref_primary_10_1007_s43926_024_00059_4 crossref_primary_10_1016_j_isatra_2021_06_032 crossref_primary_10_1155_2013_325973 crossref_primary_10_1007_s00366_021_01571_9 crossref_primary_10_1016_j_istruc_2020_07_058 crossref_primary_10_3390_sym11111409 crossref_primary_10_1007_s13042_022_01656_x crossref_primary_10_1080_19942060_2022_2098826 crossref_primary_10_3390_e23050628 crossref_primary_10_1007_s42235_022_00255_4 crossref_primary_10_1016_j_eswa_2016_10_050 crossref_primary_10_1371_journal_pone_0242612 crossref_primary_10_1007_s11071_020_06111_6 crossref_primary_10_1016_j_cma_2023_116582 crossref_primary_10_1007_s40997_023_00596_3 crossref_primary_10_1007_s12652_020_01777_7 crossref_primary_10_1007_s42235_023_00386_2 crossref_primary_10_1016_j_rineng_2025_104215 crossref_primary_10_1007_s11036_020_01565_9 crossref_primary_10_1016_j_asoc_2019_105686 crossref_primary_10_1016_j_eswa_2018_07_036 crossref_primary_10_55544_jrasb_4_2_21 crossref_primary_10_1016_j_knosys_2016_08_005 crossref_primary_10_1007_s00521_016_2665_1 crossref_primary_10_1108_EC_10_2012_0232 crossref_primary_10_4028_www_scientific_net_AMR_989_994_1646 crossref_primary_10_1016_j_jcde_2017_02_005 crossref_primary_10_1016_j_advengsoft_2022_103185 crossref_primary_10_1016_j_knosys_2019_104938 crossref_primary_10_1038_s41598_025_96559_6 crossref_primary_10_1007_s12530_023_09485_1 crossref_primary_10_1155_2013_392789 crossref_primary_10_1007_s13369_016_2222_3 crossref_primary_10_1016_j_knosys_2017_12_031 crossref_primary_10_1007_s12046_021_01572_w crossref_primary_10_1007_s00366_021_01578_2 crossref_primary_10_3390_jeta3020017 crossref_primary_10_1016_j_asoc_2018_07_033 crossref_primary_10_1109_ACCESS_2020_3014309 crossref_primary_10_3390_s20102958 crossref_primary_10_1007_s10489_022_03743_6 crossref_primary_10_1016_j_apm_2018_06_036 crossref_primary_10_1109_ACCESS_2019_2929305 crossref_primary_10_1016_j_jkss_2014_09_001 crossref_primary_10_4018_IJAEC_2018100102 crossref_primary_10_4018_IJCINI_318651 crossref_primary_10_1007_s00500_021_05874_3 crossref_primary_10_1108_EC_04_2018_0181 crossref_primary_10_3390_pr10122606 crossref_primary_10_3390_app10186538 crossref_primary_10_1080_0305215X_2013_836640 crossref_primary_10_1155_2020_1287306 crossref_primary_10_1007_s00521_015_2037_2 crossref_primary_10_3390_sym11081049 crossref_primary_10_1007_s00500_022_07726_0 crossref_primary_10_4018_IJAMC_2019040102 crossref_primary_10_1016_j_engstruct_2017_07_004 crossref_primary_10_1016_j_asoc_2014_11_029 crossref_primary_10_1016_j_knosys_2021_106937 crossref_primary_10_56294_dm2023162 crossref_primary_10_1007_s00521_018_3676_x crossref_primary_10_1007_s12065_019_00221_w crossref_primary_10_1007_s42417_019_00149_6 crossref_primary_10_1016_j_neunet_2025_107430 crossref_primary_10_1155_2020_9495281 crossref_primary_10_1016_j_chaos_2024_115408 crossref_primary_10_1109_TMC_2024_3523281
Cites_doi	10.1109/CEC.2007.4424532 10.1287/opre.45.1.92 10.1016/S0965-9978(98)00021-0 10.1002/9780470496916 10.1093/oso/9780198540755.001.0001 10.1007/978-3-642-48320-2 10.1080/03081070701303470 10.1080/03052150701252664 10.1080/03052159708940992 10.1080/03052150410001704845 10.1007/978-3-642-00185-7_1 10.1162/evco.1999.7.1.19 10.1080/02630250008970288 10.1162/evco.1996.4.1.1 10.1109/TEVC.2005.846817 10.1080/03052150701364022 10.1109/TEVC.2003.814902 10.1016/S1474-0346(02)00011-3 10.1016/j.ins.2008.02.014 10.1016/j.camwa.2011.11.010 10.1016/j.engappai.2006.03.003 10.1007/978-3-662-03423-1_27 10.1080/03052159808941241 10.1109/CEC.2008.4630985 10.1007/s00707-009-0270-4 10.1002/nme.899 10.1080/03052150500066646 10.1080/03052150212723 10.1109/ICEC.1997.592352 10.1080/03052150108940941 10.1109/4235.873238 10.1016/S0045-7825(99)00389-8 10.1016/j.eswa.2008.02.039 10.1177/003754979406200405 10.1016/S0166-3615(99)00046-9 10.1016/j.cam.2008.12.017 10.1080/03052159508941353 10.1109/ICEC.1994.349995 10.1016/S0377-2217(96)00310-4 10.1016/j.cma.2005.05.014 10.1016/j.compstruc.2011.08.002 10.1080/03052150410001647966 10.1007/s10898-005-3693-z 10.1080/03052159508941187 10.1115/1.3438995 10.1002/nme.2904 10.1016/j.amc.2006.07.105 10.1016/j.aei.2005.09.001 10.1080/03052159108941075 10.1108/02644401211235834 10.1016/j.cma.2005.09.006 10.1080/03052150215719 10.1109/CEC.2006.1688315 10.1016/j.amc.2009.01.048 10.1016/j.eswa.2009.06.044 10.1080/03052150410001704854 10.2514/3.10834 10.1115/1.2912596 10.1115/DETC1988-0012 10.1007/s00366-011-0241-y 10.1080/03052150008941317 10.1016/j.cnsns.2012.05.010
ContentType	Journal Article
Copyright	Springer-Verlag London Limited 2012 2014 INIST-CNRS
Copyright_xml	– notice: Springer-Verlag London Limited 2012 – notice: 2014 INIST-CNRS
DBID	AAYXX CITATION IQODW
DOI	10.1007/s00521-012-1028-9
DatabaseName	CrossRef Pascal-Francis
DatabaseTitle	CrossRef
DatabaseTitleList
DeliveryMethod	fulltext_linktorsrc
Discipline	Computer Science Applied Sciences Mathematics
EISSN	1433-3058
EndPage	1255
ExternalDocumentID	27623550 10_1007_s00521_012_1028_9
GroupedDBID	-4Z -59 -5G -BR -EM -Y2 -~C .4S .86 .DC .VR 06D 0R~ 0VY 123 1N0 1SB 2.D 203 28- 29N 2J2 2JN 2JY 2KG 2LR 2P1 2VQ 2~H 30V 4.4 406 408 409 40D 40E 53G 5QI 5VS 67Z 6NX 8FE 8FG 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AAOBN AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDBF ABDZT ABECU ABFTD ABFTV ABHLI ABHQN ABJNI ABJOX ABKCH ABKTR ABLJU ABMNI ABMQK ABNWP ABQBU ABQSL ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABWNU ABXPI ACAOD ACBXY ACDTI ACGFS ACHSB ACHXU ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACSNA ACUHS ACZOJ ADHHG ADHIR ADIMF ADINQ ADKNI ADKPE ADMLS ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFEXP AFGCZ AFKRA AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARAPS ARCSS ARMRJ ASPBG AVWKF AXYYD AYJHY AZFZN B-. B0M BA0 BBWZM BDATZ BENPR BGLVJ BGNMA BSONS CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 EAD EAP EBLON EBS ECS EDO EIOEI EJD EMI EMK EPL ESBYG EST ESX F5P FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNWQR GQ6 GQ7 GQ8 GXS H13 HCIFZ HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I-F I09 IHE IJ- IKXTQ ITM IWAJR IXC IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ KDC KOV KOW LAS LLZTM M4Y MA- N2Q N9A NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM P19 P2P P62 P9O PF0 PT4 PT5 QOK QOS R4E R89 R9I RHV RIG RNI RNS ROL RPX RSV RZK S16 S1Z S26 S27 S28 S3B SAP SCJ SCLPG SCO SDH SDM SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TSG TSK TSV TUC TUS U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WK8 YLTOR Z45 Z5O Z7R Z7S Z7V Z7W Z7X Z7Y Z7Z Z81 Z83 Z86 Z88 Z8M Z8N Z8P Z8Q Z8R Z8S Z8T Z8U Z8W Z92 ZMTXR ~8M ~EX AAPKM AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC ADHKG ADKFA AEZWR AFDZB AFFHD AFHIU AFOHR AGQPQ AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION PHGZM PHGZT PQGLB IQODW
ID	FETCH-LOGICAL-c384t-78db6dc851904a2f10bef3ce4164e44c6b1fd8c906066d035d75eb8d30f718743
IEDL.DBID	RSV
ISICitedReferencesCount	431
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000318003100022&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0941-0643
IngestDate	Mon Jul 21 09:13:29 EDT 2025 Sat Nov 29 08:02:25 EST 2025 Tue Nov 18 22:36:35 EST 2025 Fri Feb 21 02:34:27 EST 2025
IsPeerReviewed	true
IsScholarly	true
Issue	6
Keywords	Constraint optimization Metaheuristic algorithm Bat algorithm Engineering Neural computation Algorithm performance Optimal solution Optimization method Benchmarks Neural network Algorithm Constrained optimization
Language	English
License	http://www.springer.com/tdm CC BY 4.0
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c384t-78db6dc851904a2f10bef3ce4164e44c6b1fd8c906066d035d75eb8d30f718743
PageCount	17
ParticipantIDs	pascalfrancis_primary_27623550 crossref_citationtrail_10_1007_s00521_012_1028_9 crossref_primary_10_1007_s00521_012_1028_9 springer_journals_10_1007_s00521_012_1028_9
PublicationCentury	2000
PublicationDate	2013-05-01
PublicationDateYYYYMMDD	2013-05-01
PublicationDate_xml	– month: 05 year: 2013 text: 2013-05-01 day: 01
PublicationDecade	2010
PublicationPlace	London
PublicationPlace_xml	– name: London
PublicationTitle	Neural computing & applications
PublicationTitleAbbrev	Neural Comput & Applic
PublicationYear	2013
Publisher	Springer-Verlag Springer
Publisher_xml	– name: Springer-Verlag – name: Springer
References	Deb K, Gene AS (1997) A robust optimal design technique for mechanical component design. Evolutionary Algorithms in Engineering Applications, Springer, pp 497–514. GandomiAHYangX-SAlaviAHMixed variable structural optimization using firefly algorithmComput Struct20118923–242325233610.1016/j.compstruc.2011.08.002 MontesEMCoelloCACAn empirical study about the usefulness of evolution strategies to solve constrained optimization problemsInt J Gen Syst200837444347324408921219.9012910.1080/03081070701303470 AkhtarSTaiKRayTA socio-behavioral simulation model for engineering design optimizationEng Optim200234434135410.1080/03052150212723 HuangFZWangLHeQAn effective co-evolutionary differential evolution for constrained optimizationAppl Math Comput2007186134035623165191114.6506110.1016/j.amc.2006.07.105 Parsopoulos KE, Vrahatis MN (2005) Unified particle swarm optimization for solving constrained engineering optimization problems. In: Lecture notes in computer science (LNCS), vol 3612, pp 582–591 YangXSGandomiAHBat algorithm: a novel approach for global engineering optimizationEng Comput2012295464483 HomaifarALaiSHYQiXConstrained optimization via genetic algorithmsSimulation199462424225410.1177/003754979406200405 AragonVSEsquivelSCCoelloCCAA modified version of a T-cell algorithm for constrained optimization problemsInt J Numer Methods Eng2010843513781202.74128 Coello CAC, Montes EM (2001) Use of dominance-based tournament selection to handle constraints in genetic algorithms, In Intelligent Engineering Systems through Artificial Neural Networks (ANNIE2001), Vol. 11, ASME Press, St. Louis, Missouri, pp 177–182. LiuJ-LNovel orthogonal simulated annealing with fractional factorial analysis to solve global optimization problemsEng Optim2005375499519215301610.1080/03052150500066646 RunarssonTPYaoXStochastic ranking for constrained evolutionary optimizationIEEE Trans Evol Comput20004328429410.1109/4235.873238 KozielSMichalewiczZEvolutionary algorithms, homomorphous mappings, and constrained parameter optimizationEvol Comput199971194410.1162/evco.1999.7.1.19 HeQWangLAn effective co-evolutionary particle swarm optimization for engineering optimization problemsEng Appl Artif Intell200620899910.1016/j.engappai.2006.03.003 ZhuaWAliMMSolving nonlinearly constrained global optimization problem via an auxiliary function methodJ Comput Appl Math2009230491503253234310.1016/j.cam.2008.12.017 ZhangMLuoWWangXDifferential evolution with dynamic stochastic selection for constrained optimizationInf Sci2008178153043307410.1016/j.ins.2008.02.014 Yang X-S (2008) Nature-inspired metaheuristic algorithms. Luniver Press, Bristol TsaiJFLiHLHuNZGlobal optimization for signomial discrete programming problems in engineering designEng Optim200234661362210.1080/03052150215719 RayTSainiPEngineering design optimization using a swarm with an intelligent information sharing among individualsEng Optim200133373574810.1080/03052150108940941 CagninaLCEsquivelSCCoelloCACSolving engineering optimization problems with the simple constrained particle swarm optimizerInformatica2008323193261155.90482 DebKAn efficient constraint handling method for genetic algorithmsComput Methods Appl Mech Eng20001863113381028.9053310.1016/S0045-7825(99)00389-8 LiHLChouCTA global approach for nonlinear mixed discrete programming in design optimizationEng Optim19942210912210.1080/03052159308941328 Tessema B, Yen G (2006) A self adaptive penalty function based algorithm for constrained optimization. In: Proceedings 2006 IEEE congress on evolutionary computation, pp 246–253 HeSPrempainEWuQHAn improved particle swarm optimizer for mechanical design optimization problemsEng Optim2004365585605207290210.1080/03052150410001704854 GandomiAHYangXSTalatahariSDebSCoupled eagle strategy and differential evolution for unconstrained and constrained global optimizationComput Math Appl201263119120028634911238.9010910.1016/j.camwa.2011.11.010 KannanBKKramerSNAn augmented Lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical designJ Mech Des Trans1994116318320 Yang X-S (2010) A new metaheuristic bat-inspired algorithm. In: Gonzalez JR et al. (eds) Nature inspired cooperative strategies for optimization (NISCO 2010). Studies in computational intelligence, vol 284. Springer, Berlin, pp 65–74 Cortes NC, Trejo-Perez D, Coello CAC (2005) Handling constraints in global optimization using artificial immune system. In: Artificial immune systems, fourth international conference, ICARIS 2005, Banff, Canada. Lecture notes in computer science, vol 3627. Springer, Berlin, pp 234–247 Bernardino HS, Barbosa HJC, Lemonge ACC (2007) A hybrid genetic algorithm for constrained optimization problems in mechanical engineering. In: 2007 IEEE congress on evolutionary computation (CEC 2007), Singapore. IEEE Press, New York, pp 646–653 Yun YS (2005) Study on Adaptive hybrid genetic algorithm and its applications to engineering design problems. Waseda University, MSc Thesis KavehATalatahariSA novel heuristic optimization method: charged system searchActa Mech20102132672890579517010.1007/s00707-009-0270-4 RagsdellKMPhillipsDTOptimal design of a class of welded structures using geometric programmingASME J Eng Ind19769831021102510.1115/1.3438995 AroraJSIntroduction to optimum design1989New YorkMcGraw-Hill Hadj-AlouaneABBeanJCA genetic algorithm for the multiple-choice integer programOper Res1997459210114370750892.9013910.1287/opre.45.1.92 FuJFentonRGCleghornWLA mixed integer discrete continuous programming method and its application to engineering design optimizationEng Optim19911726328010.1080/03052159108941075 AmirjanovAThe development of a changing range genetic algorithmComput Methods Appl Mech Eng2006195c2495c2508220747910.1016/j.cma.2005.05.014 TalbiEMetaheuristics: from design to implementation2009HobokenWiley1190.90293 ShihCJLaiTKMixed-discrete fuzzy programming for nonlinear engineering optimizationEng Optim199523318719910.1080/03052159508941353 BelegunduADA study of mathematical programming methods for structural optimization1982IowaDepartment of Civil and Environmental Engineering, University of Iowa CoelloCACBecerraRLEfficient evolutionary through the use of a cultural algorithmEng Optim20043621923610.1080/03052150410001647966 CoelloCACConstraint-handling in genetic algorithms through the use of dominance-based tournament selectionAdv Eng Inform200216319320310.1016/S1474-0346(02)00011-3 RayTLiewKMSociety and civilization: an optimization algorithm based on the simulation of social behaviorIEEE Trans Evol Comput20037438639610.1109/TEVC.2003.814902 Dos Santos Coelho LGaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problemsExpert Syst Appl201037216761683 CoelloCACHybridizing a genetic algorithm with an artificial immune system for global optimizationEng Optim2004365607634207290310.1080/03052150410001704845 VenkatramanSYenGGA generic framework for constrained optimization using genetic algorithmsIEEE Trans Evol Comput20059442443510.1109/TEVC.2005.846817 LeiteJPBToppingBHVImproved genetic operators for structural engineering optimizationAdv Eng Softw1998297–952956210.1016/S0965-9978(98)00021-0 MichalewiczZSchoenauerMEvolutionary algorithms for constrained parameter optimization problemsEvol Comput19964113210.1162/evco.1996.4.1.1 Yang X-S (2009) Harmony search as a metaheuristic algorithm. In: Geem ZW (ed) Music-inspired harmony search: theory and applications. Springer, New York, pp 1–14 CoelloCACUse of a self-adaptive penalty approach for engineering optimization problemsComput Ind200041211312710.1016/S0166-3615(99)00046-9 Gandomi AH, Alavi AH (2012) Krill Herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul. doi:10.1016/j.cnsns.2012.05.010 Montes EM, Ocaña BH (2008) Bacterial foraging for engineering design problems: preliminary results. In: Proceedings of the 4th Mexican congress on evolutionary computation (COMCEV’2008), pp 33–38, CIMAT, México, Oct 2008 CaiJThieraufGEvolution strategies in engineering optimizationEng Optim199729117719910.1080/03052159708940992 HockWSchittkowskiKTest examples for nonlinear programming codes1981BerlinSpringer0452.9003810.1007/978-3-642-48320-2 LeeKSGeemZWA new meta-heuristic algorithm for continues engineering optimization: harmony search theory and practiceComput Methods Appl Mech Eng20041943902393310.1016/j.cma.2004.09.007 RichardsonPBats2008LondonNatural History Museum Michalewicz Z, Attia N (1994) Evolutionary optimization of constrained problems. In: Proceedings of the 3rd annual conf on evolutionary programming. World Scientific, pp 98–108 Barbosa HJC, Lemonge ACC (2002) An adaptive penalty scheme in genetic algorithms for constrained optimization problems. In: Langdon WB, Cantu-Paz E, Mathias K, Roy R, Davis D, Poli R, Balakrishnan K, Honavar V, Rudolph G, Wegener J, Bull L, Potter MA, Schultz AC, Miller JF, Burke E, Jonoska N (eds) Proceedings of the genetic and evolutionary computation conference (GECCO’2002). Morgan Kaufmann, San Francisco, pp 287–294 Cabrera JCF, Coello CAC (2007) Handling constraints in particle swarm optimization using a small population size. In: Alexander Gelbukh, Angel Fernando Kuri Morales (eds) MICAI 2007: advances in artificial intelligence, 6th international conference on artificial intelligence, lecture notes in artificial intelligence, vol 4827. Springer, Aguascalientes, Mexico, pp 41–51 AltringhamJDBats: biology and behavior1996OxfordOxford University Press Atiqullah MirMRaoSSSimulated annealing and parallel processing: an implementation for constrained global design optimizationEng Optim2000325659685 LiHLChangCTAn approximate approach of global optimization for polynomial programming problemsEur J Oper Res1998107362563219041660943.9007910.1016/S0377-2217(96)00310-4 HedarAFukushimaMDerivative-free filter simulated annealing method for constrained continuous global optimizationJ Glob Optim20063552154922495471133.9042110.1007/s10898-005-3693-z BecerraRLCoelloCACCultu AD Belegundu (1028_CR83) 1982 EM Montes (1028_CR61) 2007; 39 ACC Lemonge (1028_CR54) 2004; 59 BK Kannan (1028_CR53) 1994; 116 CJ Shih (1028_CR66) 1995; 23 C Zhang (1028_CR71) 1993; 17 1028_CR52 Q He (1028_CR47) 2006; 20 1028_CR50 K Deb (1028_CR73) 2000; 186 S-F Hwang (1028_CR77) 2006; 20 S Koziel (1028_CR25) 1999; 7 1028_CR12 1028_CR11 LC Cagnina (1028_CR33) 2008; 32 Z Michalewicz (1028_CR16) 1996; 4 JPB Leite (1028_CR72) 1998; 29 1028_CR58 CAC Coello (1028_CR37) 2000; 41 CAC Coello (1028_CR84) 2004; 36 KS Lee (1028_CR8) 2004; 194 A Homaifar (1028_CR49) 1994; 62 AH Gandomi (1028_CR79) 2011; 89 HL Li (1028_CR56) 1994; 22 P Richardson (1028_CR14) 2008 HL Li (1028_CR55) 1998; 107 S Venkatraman (1028_CR28) 2005; 9 EM Montes (1028_CR59) 2008; 37 RL Becerra (1028_CR21) 2006; 195 1028_CR42 JN Siddall (1028_CR80) 1972 CAC Coello (1028_CR39) 2002; 16 J-L Liu (1028_CR76) 2005; 37 S Akhtar (1028_CR6) 2002; 34 1028_CR44 1028_CR43 M Zhang (1028_CR82) 2008; 178 A Amirjanov (1028_CR19) 2006; 195 TP Runarsson (1028_CR17) 2000; 4 W Zhua (1028_CR29) 2009; 230 JD Altringham (1028_CR13) 1996 1028_CR9 1028_CR1 K Deb (1028_CR74) 1991; 29 1028_CR4 1028_CR31 1028_CR75 1028_CR30 1028_CR7 S He (1028_CR48) 2004; 36 JS Arora (1028_CR2) 1989 1028_CR35 JF Tsai (1028_CR67) 2002; 34 AH Gandomi (1028_CR5) 2012; 63 1028_CR32 CAC Coello (1028_CR40) 2004; 36 E Talbi (1028_CR3) 2009 W Hock (1028_CR15) 1981 VS Aragon (1028_CR20) 2010; 84 T Ray (1028_CR63) 2003; 7 VV Litinetski (1028_CR57) 1998; 30 A Hedar (1028_CR18) 2006; 35 YL Hsu (1028_CR85) 2007; 39 SJ Wu (1028_CR68) 1995; 24 J Cai (1028_CR34) 1997; 29 FZ Huang (1028_CR51) 2007; 186 1028_CR60 AB Hadj-Alouane (1028_CR46) 1997; 45 1028_CR64 J Fu (1028_CR45) 1991; 17 CAC Coello (1028_CR41) 2004; 36 1028_CR62 1028_CR24 CAC Coello (1028_CR36) 1999; 1 1028_CR23 T Ray (1028_CR86) 2001; 33 1028_CR22 CAC Coello (1028_CR38) 2000; 17 E Sandgren (1028_CR65) 1990; 112 A Kaveh (1028_CR10) 2010; 213 1028_CR27 J Zhang (1028_CR78) 2009; 211 1028_CR26 KM Ragsdell (1028_CR81) 1976; 98 1028_CR69 E Zahara (1028_CR70) 2009; 36
References_xml	– reference: Yang X-S (2009) Harmony search as a metaheuristic algorithm. In: Geem ZW (ed) Music-inspired harmony search: theory and applications. Springer, New York, pp 1–14 – reference: CoelloCACUse of a self-adaptive penalty approach for engineering optimization problemsComput Ind200041211312710.1016/S0166-3615(99)00046-9 – reference: SandgrenENonlinear integer and discrete programming in mechanical design optimizationASME J Mech Des1990112222322910.1115/1.2912596 – reference: Gandomi AH, Yang XS, Alavi AH (2012) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems, Eng Comput, in press. doi:10.1007/s00366-011-0241-y – reference: CoelloCACSelf-adaptive penalties for GA based optimizationProc Congr Evol Comput19991573580 – reference: HsuYLLiuTCDeveloping a fuzzy proportional-derivative controller optimization engine for engineering design optimization problemsEng Optim2007396679700234070010.1080/03052150701252664 – reference: Hadj-AlouaneABBeanJCA genetic algorithm for the multiple-choice integer programOper Res1997459210114370750892.9013910.1287/opre.45.1.92 – reference: KozielSMichalewiczZEvolutionary algorithms, homomorphous mappings, and constrained parameter optimizationEvol Comput199971194410.1162/evco.1999.7.1.19 – reference: CaiJThieraufGEvolution strategies in engineering optimizationEng Optim199729117719910.1080/03052159708940992 – reference: SiddallJNAnalytical decision-making in engineering design1972Englewood CliffsPrentice-Hall – reference: AmirjanovAThe development of a changing range genetic algorithmComput Methods Appl Mech Eng2006195c2495c2508220747910.1016/j.cma.2005.05.014 – reference: Montes EM, Ocaña BH (2008) Bacterial foraging for engineering design problems: preliminary results. In: Proceedings of the 4th Mexican congress on evolutionary computation (COMCEV’2008), pp 33–38, CIMAT, México, Oct 2008 – reference: Sandgren E (1988) Nonlinear integer and discrete programming in mechanical design. In: Proc ASME design technology conf, Kissimine, FL, pp 95–105 – reference: BecerraRLCoelloCACCultured differential evolution for constrained optimizationComput Methods Appl Mech Eng200619533–36430343221123.7403910.1016/j.cma.2005.09.006 – reference: Barbosa HJC, Lemonge ACC (2002) An adaptive penalty scheme in genetic algorithms for constrained optimization problems. In: Langdon WB, Cantu-Paz E, Mathias K, Roy R, Davis D, Poli R, Balakrishnan K, Honavar V, Rudolph G, Wegener J, Bull L, Potter MA, Schultz AC, Miller JF, Burke E, Jonoska N (eds) Proceedings of the genetic and evolutionary computation conference (GECCO’2002). Morgan Kaufmann, San Francisco, pp 287–294 – reference: HeQWangLAn effective co-evolutionary particle swarm optimization for engineering optimization problemsEng Appl Artif Intell200620899910.1016/j.engappai.2006.03.003 – reference: HeSPrempainEWuQHAn improved particle swarm optimizer for mechanical design optimization problemsEng Optim2004365585605207290210.1080/03052150410001704854 – reference: Gandomi AH, Alavi AH (2012) Krill Herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul. doi:10.1016/j.cnsns.2012.05.010 – reference: RichardsonPBats2008LondonNatural History Museum – reference: CagninaLCEsquivelSCCoelloCACSolving engineering optimization problems with the simple constrained particle swarm optimizerInformatica2008323193261155.90482 – reference: TsaiJFLiHLHuNZGlobal optimization for signomial discrete programming problems in engineering designEng Optim200234661362210.1080/03052150215719 – reference: HockWSchittkowskiKTest examples for nonlinear programming codes1981BerlinSpringer0452.9003810.1007/978-3-642-48320-2 – reference: ShihCJLaiTKMixed-discrete fuzzy programming for nonlinear engineering optimizationEng Optim199523318719910.1080/03052159508941353 – reference: GandomiAHYangX-SAlaviAHMixed variable structural optimization using firefly algorithmComput Struct20118923–242325233610.1016/j.compstruc.2011.08.002 – reference: WuSJChowPTGenetic algorithms for nonlinear mixed discrete-integer optimization problems via meta-genetic parameter optimizationEng Optim19952413715910.1080/03052159508941187 – reference: Yun YS (2005) Study on Adaptive hybrid genetic algorithm and its applications to engineering design problems. Waseda University, MSc Thesis – reference: HedarAFukushimaMDerivative-free filter simulated annealing method for constrained continuous global optimizationJ Glob Optim20063552154922495471133.9042110.1007/s10898-005-3693-z – reference: Hu X, Eberhart RC, Shi Y (2003) Engineering optimization with particle swarm. In: Proc 2003 IEEE swarm intelligence symposium, pp 53–57 – reference: Bernardino HS, Barbosa HJC, Lemonge ACC (2006) Constraints handling in genetic algorithms via artificial immune systems. In: Genetic and evolutionary computation-GECCO 2006, genetic and evolutionary computation conference-Late Breaking Paper, Seattle, WA, USA – reference: KannanBKKramerSNAn augmented Lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical designJ Mech Des Trans1994116318320 – reference: CoelloCACConstraint-handling in genetic algorithms through the use of dominance-based tournament selectionAdv Eng Inform200216319320310.1016/S1474-0346(02)00011-3 – reference: LiHLChouCTA global approach for nonlinear mixed discrete programming in design optimizationEng Optim19942210912210.1080/03052159308941328 – reference: VenkatramanSYenGGA generic framework for constrained optimization using genetic algorithmsIEEE Trans Evol Comput20059442443510.1109/TEVC.2005.846817 – reference: LiuJ-LNovel orthogonal simulated annealing with fractional factorial analysis to solve global optimization problemsEng Optim2005375499519215301610.1080/03052150500066646 – reference: MontesEMCoelloCACVelázquez-ReyesJMuñoz-DávilaLMultiple trial vectors in differential evolution for engineering designEng Optim2007395567589234450310.1080/03052150701364022 – reference: ZaharaEKaoYTHybrid Nelder–Mead simplex search and particle swarm optimization for constrained engineering design problemsExpert Syst Appl2009363880388610.1016/j.eswa.2008.02.039 – reference: Yang X-S (2008) Nature-inspired metaheuristic algorithms. Luniver Press, Bristol – reference: AkhtarSTaiKRayTA socio-behavioral simulation model for engineering design optimizationEng Optim200234434135410.1080/03052150212723 – reference: KavehATalatahariSA novel heuristic optimization method: charged system searchActa Mech20102132672890579517010.1007/s00707-009-0270-4 – reference: Dos Santos Coelho LGaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problemsExpert Syst Appl201037216761683 – reference: CoelloCACCortésNCHybridizing a genetic algorithm with an artificial immune system for global optimizationEng Optim2004365607634207290310.1080/03052150410001704845 – reference: Joines J, Houck C (1994) On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with GAs. In: Fogel D (ed) Proc first IEEE conf on evolutionary computation, Orlando, Florida. IEEE Press, pp 579–584 – reference: LemongeACCBarbosaHJCAn adaptive penalty scheme for genetic algorithms in structural optimizationInt J Numer Methods Eng2004597037361060.7458210.1002/nme.899 – reference: HwangS-FHeR-SA hybrid real-parameter genetic algorithm for function optimizationAdv Eng Inform20062072110.1016/j.aei.2005.09.001 – reference: CoelloCACConstraint-handling using an evolutionary multiobjective optimization techniqueCivil Eng Environ Syst20001731934610.1080/02630250008970288 – reference: TalbiEMetaheuristics: from design to implementation2009HobokenWiley1190.90293 – reference: FuJFentonRGCleghornWLA mixed integer discrete continuous programming method and its application to engineering design optimizationEng Optim19911726328010.1080/03052159108941075 – reference: HomaifarALaiSHYQiXConstrained optimization via genetic algorithmsSimulation199462424225410.1177/003754979406200405 – reference: GandomiAHYangXSTalatahariSDebSCoupled eagle strategy and differential evolution for unconstrained and constrained global optimizationComput Math Appl201263119120028634911238.9010910.1016/j.camwa.2011.11.010 – reference: Cortes NC, Trejo-Perez D, Coello CAC (2005) Handling constraints in global optimization using artificial immune system. In: Artificial immune systems, fourth international conference, ICARIS 2005, Banff, Canada. Lecture notes in computer science, vol 3627. Springer, Berlin, pp 234–247 – reference: Montes EM, Coello CAC (2003) A simple multimembered evolution strategy to solve constrained optimization problems, Technical Report EVOCINV-04-2003, Evolutionary Computation Group at CINVESTAV, Secci′on de Computaci′on, Departamento de Ingenierıa El′ectrica, CINVESTAV-IPN, Mexico DF, Mexico – reference: MichalewiczZSchoenauerMEvolutionary algorithms for constrained parameter optimization problemsEvol Comput19964113210.1162/evco.1996.4.1.1 – reference: LitinetskiVVAbramzonBMMars—a multistart adaptive random search method for global constrained optimization in engineering applicationsEng Optim199830212515410.1080/03052159808941241 – reference: LeiteJPBToppingBHVImproved genetic operators for structural engineering optimizationAdv Eng Softw1998297–952956210.1016/S0965-9978(98)00021-0 – reference: LeeKSGeemZWA new meta-heuristic algorithm for continues engineering optimization: harmony search theory and practiceComput Methods Appl Mech Eng20041943902393310.1016/j.cma.2004.09.007 – reference: Tessema B, Yen G (2006) A self adaptive penalty function based algorithm for constrained optimization. In: Proceedings 2006 IEEE congress on evolutionary computation, pp 246–253 – reference: RunarssonTPYaoXStochastic ranking for constrained evolutionary optimizationIEEE Trans Evol Comput20004328429410.1109/4235.873238 – reference: Bernardino HS, Barbosa HJC, Lemonge ACC, Fonseca LG (2008) A new hybrid AIS-GA for constrained optimization problems in mechanical engineering. In: 2008 congress on evolutionary computation (CEC’2008), Hong Kong. IEEE Service Center, Piscataway, pp 1455–1462 – reference: ZhangMLuoWWangXDifferential evolution with dynamic stochastic selection for constrained optimizationInf Sci2008178153043307410.1016/j.ins.2008.02.014 – reference: Cao YJ, Wu QH. Mechanical design optimization by mixed variable evolutionary programming, In Proc 1997 Int Conf on Evolutionary Computation, Indianapolis; 1997, p. 443–446. – reference: YangXSGandomiAHBat algorithm: a novel approach for global engineering optimizationEng Comput2012295464483 – reference: Yang X-S (2010) A new metaheuristic bat-inspired algorithm. In: Gonzalez JR et al. (eds) Nature inspired cooperative strategies for optimization (NISCO 2010). Studies in computational intelligence, vol 284. Springer, Berlin, pp 65–74 – reference: ZhuaWAliMMSolving nonlinearly constrained global optimization problem via an auxiliary function methodJ Comput Appl Math2009230491503253234310.1016/j.cam.2008.12.017 – reference: ZhangCWangHPMixed-discrete nonlinear optimization with simulated annealingEng Optim1993173263280 – reference: Coello CAC, Montes EM (2001) Use of dominance-based tournament selection to handle constraints in genetic algorithms, In Intelligent Engineering Systems through Artificial Neural Networks (ANNIE2001), Vol. 11, ASME Press, St. Louis, Missouri, pp 177–182. – reference: Atiqullah MirMRaoSSSimulated annealing and parallel processing: an implementation for constrained global design optimizationEng Optim2000325659685 – reference: BelegunduADA study of mathematical programming methods for structural optimization1982IowaDepartment of Civil and Environmental Engineering, University of Iowa – reference: ZhangJLiangCHuangYWuJYangSAn effective multiagent evolutionary algorithm integrating a novel roulette inversion operator for engineering optimizationAppl Math Comput200921139241625241681162.6535710.1016/j.amc.2009.01.048 – reference: AroraJSIntroduction to optimum design1989New YorkMcGraw-Hill – reference: Michalewicz Z, Attia N (1994) Evolutionary optimization of constrained problems. In: Proceedings of the 3rd annual conf on evolutionary programming. World Scientific, pp 98–108 – reference: Bernardino HS, Barbosa HJC, Lemonge ACC (2007) A hybrid genetic algorithm for constrained optimization problems in mechanical engineering. In: 2007 IEEE congress on evolutionary computation (CEC 2007), Singapore. IEEE Press, New York, pp 646–653 – reference: AragonVSEsquivelSCCoelloCCAA modified version of a T-cell algorithm for constrained optimization problemsInt J Numer Methods Eng2010843513781202.74128 – reference: MontesEMCoelloCACAn empirical study about the usefulness of evolution strategies to solve constrained optimization problemsInt J Gen Syst200837444347324408921219.9012910.1080/03081070701303470 – reference: DebKAn efficient constraint handling method for genetic algorithmsComput Methods Appl Mech Eng20001863113381028.9053310.1016/S0045-7825(99)00389-8 – reference: LiHLChangCTAn approximate approach of global optimization for polynomial programming problemsEur J Oper Res1998107362563219041660943.9007910.1016/S0377-2217(96)00310-4 – reference: AltringhamJDBats: biology and behavior1996OxfordOxford University Press – reference: Parsopoulos KE, Vrahatis MN (2005) Unified particle swarm optimization for solving constrained engineering optimization problems. In: Lecture notes in computer science (LNCS), vol 3612, pp 582–591 – reference: CoelloCACBecerraRLEfficient evolutionary through the use of a cultural algorithmEng Optim20043621923610.1080/03052150410001647966 – reference: RayTLiewKMSociety and civilization: an optimization algorithm based on the simulation of social behaviorIEEE Trans Evol Comput20037438639610.1109/TEVC.2003.814902 – reference: DebKOptimal design of a welded beam via genetic algorithmsAIAA J199129112013201510.2514/3.10834 – reference: RayTSainiPEngineering design optimization using a swarm with an intelligent information sharing among individualsEng Optim200133373574810.1080/03052150108940941 – reference: HuangFZWangLHeQAn effective co-evolutionary differential evolution for constrained optimizationAppl Math Comput2007186134035623165191114.6506110.1016/j.amc.2006.07.105 – reference: Cabrera JCF, Coello CAC (2007) Handling constraints in particle swarm optimization using a small population size. In: Alexander Gelbukh, Angel Fernando Kuri Morales (eds) MICAI 2007: advances in artificial intelligence, 6th international conference on artificial intelligence, lecture notes in artificial intelligence, vol 4827. Springer, Aguascalientes, Mexico, pp 41–51 – reference: Deb K, Gene AS (1997) A robust optimal design technique for mechanical component design. Evolutionary Algorithms in Engineering Applications, Springer, pp 497–514. – reference: RagsdellKMPhillipsDTOptimal design of a class of welded structures using geometric programmingASME J Eng Ind19769831021102510.1115/1.3438995 – reference: CoelloCACHybridizing a genetic algorithm with an artificial immune system for global optimizationEng Optim2004365607634207290310.1080/03052150410001704845 – ident: 1028_CR31 doi: 10.1109/CEC.2007.4424532 – volume: 1 start-page: 573 year: 1999 ident: 1028_CR36 publication-title: Proc Congr Evol Comput – volume: 45 start-page: 92 year: 1997 ident: 1028_CR46 publication-title: Oper Res doi: 10.1287/opre.45.1.92 – volume: 29 start-page: 529 issue: 7–9 year: 1998 ident: 1028_CR72 publication-title: Adv Eng Softw doi: 10.1016/S0965-9978(98)00021-0 – ident: 1028_CR24 – volume-title: Metaheuristics: from design to implementation year: 2009 ident: 1028_CR3 doi: 10.1002/9780470496916 – volume-title: Bats: biology and behavior year: 1996 ident: 1028_CR13 doi: 10.1093/oso/9780198540755.001.0001 – volume-title: Test examples for nonlinear programming codes year: 1981 ident: 1028_CR15 doi: 10.1007/978-3-642-48320-2 – volume: 37 start-page: 443 issue: 4 year: 2008 ident: 1028_CR59 publication-title: Int J Gen Syst doi: 10.1080/03081070701303470 – volume: 39 start-page: 679 issue: 6 year: 2007 ident: 1028_CR85 publication-title: Eng Optim doi: 10.1080/03052150701252664 – volume: 29 start-page: 177 issue: 1 year: 1997 ident: 1028_CR34 publication-title: Eng Optim doi: 10.1080/03052159708940992 – volume: 36 start-page: 607 issue: 5 year: 2004 ident: 1028_CR40 publication-title: Eng Optim doi: 10.1080/03052150410001704845 – ident: 1028_CR4 doi: 10.1007/978-3-642-00185-7_1 – volume: 7 start-page: 19 issue: 1 year: 1999 ident: 1028_CR25 publication-title: Evol Comput doi: 10.1162/evco.1999.7.1.19 – volume: 17 start-page: 319 year: 2000 ident: 1028_CR38 publication-title: Civil Eng Environ Syst doi: 10.1080/02630250008970288 – volume: 4 start-page: 1 issue: 1 year: 1996 ident: 1028_CR16 publication-title: Evol Comput doi: 10.1162/evco.1996.4.1.1 – volume: 9 start-page: 424 issue: 4 year: 2005 ident: 1028_CR28 publication-title: IEEE Trans Evol Comput doi: 10.1109/TEVC.2005.846817 – volume: 39 start-page: 567 issue: 5 year: 2007 ident: 1028_CR61 publication-title: Eng Optim doi: 10.1080/03052150701364022 – volume: 7 start-page: 386 issue: 4 year: 2003 ident: 1028_CR63 publication-title: IEEE Trans Evol Comput doi: 10.1109/TEVC.2003.814902 – volume-title: Analytical decision-making in engineering design year: 1972 ident: 1028_CR80 – ident: 1028_CR30 – volume: 16 start-page: 193 issue: 3 year: 2002 ident: 1028_CR39 publication-title: Adv Eng Inform doi: 10.1016/S1474-0346(02)00011-3 – ident: 1028_CR62 – volume: 178 start-page: 3043 issue: 15 year: 2008 ident: 1028_CR82 publication-title: Inf Sci doi: 10.1016/j.ins.2008.02.014 – volume: 63 start-page: 191 issue: 1 year: 2012 ident: 1028_CR5 publication-title: Comput Math Appl doi: 10.1016/j.camwa.2011.11.010 – volume: 20 start-page: 89 year: 2006 ident: 1028_CR47 publication-title: Eng Appl Artif Intell doi: 10.1016/j.engappai.2006.03.003 – ident: 1028_CR43 doi: 10.1007/978-3-662-03423-1_27 – volume: 30 start-page: 125 issue: 2 year: 1998 ident: 1028_CR57 publication-title: Eng Optim doi: 10.1080/03052159808941241 – ident: 1028_CR69 – volume-title: Bats year: 2008 ident: 1028_CR14 – ident: 1028_CR23 – ident: 1028_CR32 doi: 10.1109/CEC.2008.4630985 – ident: 1028_CR50 – volume: 213 start-page: 267 year: 2010 ident: 1028_CR10 publication-title: Acta Mech doi: 10.1007/s00707-009-0270-4 – volume: 59 start-page: 703 year: 2004 ident: 1028_CR54 publication-title: Int J Numer Methods Eng doi: 10.1002/nme.899 – ident: 1028_CR58 – volume: 37 start-page: 499 issue: 5 year: 2005 ident: 1028_CR76 publication-title: Eng Optim doi: 10.1080/03052150500066646 – volume: 34 start-page: 341 issue: 4 year: 2002 ident: 1028_CR6 publication-title: Eng Optim doi: 10.1080/03052150212723 – ident: 1028_CR12 – ident: 1028_CR35 doi: 10.1109/ICEC.1997.592352 – volume: 33 start-page: 735 issue: 3 year: 2001 ident: 1028_CR86 publication-title: Eng Optim doi: 10.1080/03052150108940941 – volume: 4 start-page: 284 issue: 3 year: 2000 ident: 1028_CR17 publication-title: IEEE Trans Evol Comput doi: 10.1109/4235.873238 – volume: 186 start-page: 311 year: 2000 ident: 1028_CR73 publication-title: Comput Methods Appl Mech Eng doi: 10.1016/S0045-7825(99)00389-8 – volume: 36 start-page: 3880 year: 2009 ident: 1028_CR70 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2008.02.039 – volume: 62 start-page: 242 issue: 4 year: 1994 ident: 1028_CR49 publication-title: Simulation doi: 10.1177/003754979406200405 – volume-title: Introduction to optimum design year: 1989 ident: 1028_CR2 – volume: 41 start-page: 113 issue: 2 year: 2000 ident: 1028_CR37 publication-title: Comput Ind doi: 10.1016/S0166-3615(99)00046-9 – ident: 1028_CR22 – ident: 1028_CR26 – volume: 230 start-page: 491 year: 2009 ident: 1028_CR29 publication-title: J Comput Appl Math doi: 10.1016/j.cam.2008.12.017 – volume: 194 start-page: 3902 year: 2004 ident: 1028_CR8 publication-title: Comput Methods Appl Mech Eng – volume-title: A study of mathematical programming methods for structural optimization year: 1982 ident: 1028_CR83 – volume: 23 start-page: 187 issue: 3 year: 1995 ident: 1028_CR66 publication-title: Eng Optim doi: 10.1080/03052159508941353 – ident: 1028_CR52 doi: 10.1109/ICEC.1994.349995 – volume: 107 start-page: 625 issue: 3 year: 1998 ident: 1028_CR55 publication-title: Eur J Oper Res doi: 10.1016/S0377-2217(96)00310-4 – volume: 195 start-page: c2495 year: 2006 ident: 1028_CR19 publication-title: Comput Methods Appl Mech Eng doi: 10.1016/j.cma.2005.05.014 – volume: 89 start-page: 2325 issue: 23–24 year: 2011 ident: 1028_CR79 publication-title: Comput Struct doi: 10.1016/j.compstruc.2011.08.002 – volume: 36 start-page: 219 year: 2004 ident: 1028_CR84 publication-title: Eng Optim doi: 10.1080/03052150410001647966 – volume: 35 start-page: 521 year: 2006 ident: 1028_CR18 publication-title: J Glob Optim doi: 10.1007/s10898-005-3693-z – ident: 1028_CR60 – volume: 32 start-page: 319 year: 2008 ident: 1028_CR33 publication-title: Informatica – volume: 24 start-page: 137 year: 1995 ident: 1028_CR68 publication-title: Eng Optim doi: 10.1080/03052159508941187 – volume: 98 start-page: 1021 issue: 3 year: 1976 ident: 1028_CR81 publication-title: ASME J Eng Ind doi: 10.1115/1.3438995 – volume: 84 start-page: 351 year: 2010 ident: 1028_CR20 publication-title: Int J Numer Methods Eng doi: 10.1002/nme.2904 – volume: 186 start-page: 340 issue: 1 year: 2007 ident: 1028_CR51 publication-title: Appl Math Comput doi: 10.1016/j.amc.2006.07.105 – volume: 20 start-page: 7 year: 2006 ident: 1028_CR77 publication-title: Adv Eng Inform doi: 10.1016/j.aei.2005.09.001 – volume: 17 start-page: 263 year: 1991 ident: 1028_CR45 publication-title: Eng Optim doi: 10.1080/03052159108941075 – ident: 1028_CR9 doi: 10.1108/02644401211235834 – volume: 195 start-page: 4303 issue: 33–36 year: 2006 ident: 1028_CR21 publication-title: Comput Methods Appl Mech Eng doi: 10.1016/j.cma.2005.09.006 – volume: 34 start-page: 613 issue: 6 year: 2002 ident: 1028_CR67 publication-title: Eng Optim doi: 10.1080/03052150215719 – ident: 1028_CR42 – ident: 1028_CR27 doi: 10.1109/CEC.2006.1688315 – volume: 211 start-page: 392 year: 2009 ident: 1028_CR78 publication-title: Appl Math Comput doi: 10.1016/j.amc.2009.01.048 – ident: 1028_CR44 doi: 10.1016/j.eswa.2009.06.044 – volume: 36 start-page: 585 issue: 5 year: 2004 ident: 1028_CR48 publication-title: Eng Optim doi: 10.1080/03052150410001704854 – volume: 116 start-page: 318 year: 1994 ident: 1028_CR53 publication-title: J Mech Des Trans – volume: 36 start-page: 607 issue: 5 year: 2004 ident: 1028_CR41 publication-title: Eng Optim doi: 10.1080/03052150410001704845 – ident: 1028_CR1 – volume: 29 start-page: 2013 issue: 11 year: 1991 ident: 1028_CR74 publication-title: AIAA J doi: 10.2514/3.10834 – volume: 17 start-page: 263 issue: 3 year: 1993 ident: 1028_CR71 publication-title: Eng Optim – volume: 112 start-page: 223 issue: 2 year: 1990 ident: 1028_CR65 publication-title: ASME J Mech Des doi: 10.1115/1.2912596 – volume: 22 start-page: 109 year: 1994 ident: 1028_CR56 publication-title: Eng Optim – ident: 1028_CR64 doi: 10.1115/DETC1988-0012 – ident: 1028_CR11 doi: 10.1007/s00366-011-0241-y – ident: 1028_CR75 doi: 10.1080/03052150008941317 – ident: 1028_CR7 doi: 10.1016/j.cnsns.2012.05.010
SSID	ssj0004685
Score	2.505241
Snippet	In this study, we use a new metaheuristic optimization algorithm, called bat algorithm (BA), to solve constraint optimization tasks. BA is verified using...
SourceID	pascalfrancis crossref springer
SourceType	Index Database Enrichment Source Publisher
StartPage	1239
SubjectTerms	Applied sciences Artificial Intelligence Calculus of variations and optimal control Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Computer science; control theory; systems Data Mining and Knowledge Discovery Exact sciences and technology Image Processing and Computer Vision Learning and adaptive systems Mathematical analysis Mathematics Numerical analysis Numerical analysis. Scientific computation Numerical methods in mathematical programming, optimization and calculus of variations Numerical methods in optimization and calculus of variations Original Article Probability and Statistics in Computer Science Sciences and techniques of general use
Title	Bat algorithm for constrained optimization tasks
URI	https://link.springer.com/article/10.1007/s00521-012-1028-9
Volume	22
WOSCitedRecordID	wos000318003100022&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: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 1433-3058 dateEnd: 20241213 omitProxy: false ssIdentifier: ssj0004685 issn: 0941-0643 databaseCode: P5Z dateStart: 20120101 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central Database Suite (ProQuest) customDbUrl: eissn: 1433-3058 dateEnd: 20241213 omitProxy: false ssIdentifier: ssj0004685 issn: 0941-0643 databaseCode: BENPR dateStart: 20120101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVAVX databaseName: Springer Online Journals customDbUrl: eissn: 1433-3058 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0004685 issn: 0941-0643 databaseCode: RSV dateStart: 19970101 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1JSwMxFH5o60EQ64p1KTl4UgaSJrMdrbR4KqUu9DZMltFiNzqjv9-86cxgcQG954XwkrflLR_AJVUmYSz2HJYghJm0IiUZZ46H2lIyyrnWOdiE3-8Ho1E4KPq407LavUxJ5pq6anbDH0wMfdsOGkUn3IS6tXYB4jUM758-NUPmOJw2bMGSHsHLVOZ3W6wZo51FnFq-JCtAiy-Z0dzg9Br_Ouoe7Bb-JblZPYh92DCzA2iU2A2kEOVDoJ04I_Hkeb4cZy9TYl1XotBVRMQIo8ncapJp0aJJsjh9TY_gsdd9uL1zCvQER_FAZI4faOlpZT2qkIq4nTAqTcKVsR6YMEIoT7JEByqkGMJoyl3tu0YGmtPER6A-fgy12XxmToDgN2ES-wKnxQvME0pXWzOPKc3AC92wCbRkY6SK0eJ43klUDUXOORJZjkTIkciSXFUki9Vcjd8Wt9bupqJoW1VuHSbahOvyIqJCBtOftzv90-oz2G7nEBhY5HgOtWz5Zi5gS71n43TZgnqn2x8MW_kb_ADydtFB
linkProvider	Springer Nature
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1JSwMxFA5aBQWxrliXmoMnZSCZpLMcVSwVaxGt0tsw2bTYjc7o7zdvOjNYXEDveSG85G15y4fQCZHaUBp7DjUAYSasSAnKqOOBthSUMKZUBjbhdzpBrxfe5X3cSVHtXqQkM01dNrvBDyaEvq4DRtEJF9EStwYLBubfPzx9aobMcDht2AIlPZwVqczvtpgzRmuTOLF8MTNAiy-Z0czgNKv_OuoGWs_9S3w-exCbaEGPtlC1wG7AuShvI3IRpzgePI-n_fRliK3riiW4ioAYoRUeW00yzFs0cRonr8kOemxedS9bTo6e4EgW8NTxAyU8Ja1HFRIeu4YSoQ2T2npgXHMuPUGNCmRIIIRRhDWU39AiUIwYH4D62C6qjMYjvYcwfBOa2OcwLZ5DnlA0lDXzkNIMvLAR1hAp2BjJfLQ4nHcQlUORM45EliMRcCSyJKclyWQ2V-O3xfW5uykpXKvKrcNEauisuIgol8Hk5-32_7T6GK20urftqH3duTlAq24GhwEFj4eokk7f9BFalu9pP5nWs3f4ATRH0os
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1JSwMxFA5aRQSxrliXmoMnZTCZZLajW1GUUnCht2EmixbbaemM_n7zZsPiAuI9L4SXvC1v-RA6IkJpSiPXohogzGIjUjFl1HJBW8aUMCZlDjbhdbt-vx_0SpzTtKp2r1KSRU8DTGlKstOJ1Kd14xv8ZkIYbFtgIK1gHi1wqKOHcP3-6VNjZI7JaUIYKO_hrEprfrfFjGFamUSp4ZEuwC2-ZElz49Np_vvYa2i19DvxWfFQ1tGcSjZQs8J0wKWIbyJyHmU4Gj6Pp4PsZYSNS4sFuJCAJKEkHhsNMypbN3EWpa_pFnrsXD1cXFslqoIlmM8zy_Nl7EphPK2A8MjWlMRKM6GMZ8YV58KNqZa-CAiENpIwR3qOin3JiPYAwI9to0YyTtQOwvB9qCOPwxR5DvnD2JHG_EOq03cDJ2ghUrE0FOXIcTjvMKyHJeccCQ1HQuBIaEiOa5JJMW_jt8XtmXuqKWyj4o0jRVropLqUsJTN9Oftdv-0-hAt9S474d1N93YPLds5SgbUQe6jRjZ9UwdoUbxng3Tazp_kB3pZ228
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=Bat+algorithm+for+constrained+optimization+tasks&rft.jtitle=Neural+computing+%26+applications&rft.au=Gandomi%2C+Amir+Hossein&rft.au=Yang%2C+Xin-She&rft.au=Alavi%2C+Amir+Hossein&rft.au=Talatahari%2C+Siamak&rft.date=2013-05-01&rft.pub=Springer-Verlag&rft.issn=0941-0643&rft.eissn=1433-3058&rft.volume=22&rft.issue=6&rft.spage=1239&rft.epage=1255&rft_id=info:doi/10.1007%2Fs00521-012-1028-9&rft.externalDocID=10_1007_s00521_012_1028_9
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0941-0643&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0941-0643&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0941-0643&client=summon