{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T09:28:06Z","timestamp":1780565286257,"version":"3.54.1"},"reference-count":80,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,12,14]],"date-time":"2021-12-14T00:00:00Z","timestamp":1639440000000},"content-version":"vor","delay-in-days":347,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61976239"],"award-info":[{"award-number":["61976239"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["71871069"],"award-info":[{"award-number":["71871069"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008326","name":"Guangdong University of Technology","doi-asserted-by":"publisher","award":["2019KZDZX1020"],"award-info":[{"award-number":["2019KZDZX1020"]}],"id":[{"id":"10.13039\/501100008326","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>Shuffled frog leaping algorithm, a novel heuristic method, is inspired by the foraging behavior of the frog population, which has been designed by the shuffled process and the PSO framework. To increase the convergence speed and effectiveness, the currently improved versions are focused on the local search ability in PSO framework, which limited the development of SFLA. Therefore, we first propose a new scheme based on evolutionary strategy, which is accomplished by quantum evolution and eigenvector evolution. In this scheme, the frog leaping rule based on quantum evolution is achieved by two potential wells with the historical information for the local search, and eigenvector evolution is achieved by the eigenvector evolutionary operator for the global search. To test the performance of the proposed approach, the basic benchmark suites, CEC2013 and CEC2014, and a parameter optimization problem of SVM are used to compare 15 well\u2010known algorithms. Experimental results demonstrate that the performance of the proposed algorithm is better than that of the other heuristic algorithms.<\/jats:p>","DOI":"10.1155\/2021\/8928182","type":"journal-article","created":{"date-parts":[[2021,12,15]],"date-time":"2021-12-15T01:05:05Z","timestamp":1639530305000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["An Evolutionary Frog Leaping Algorithm for Global Optimization Problems and Applications"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1782-6007","authenticated-orcid":false,"given":"Deyu","family":"Tang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3315-4447","authenticated-orcid":false,"given":"Jie","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1654-5414","authenticated-orcid":false,"given":"Jin","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4393-4420","authenticated-orcid":false,"given":"Zhen","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0971-8206","authenticated-orcid":false,"given":"Yongming","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,12,14]]},"reference":[{"key":"e_1_2_11_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.advengsoft.2017.05.014"},{"key":"e_1_2_11_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2014.05.009"},{"key":"e_1_2_11_3_2","doi-asserted-by":"crossref","unstructured":"KennedyJ.andEberhartR. C. Particle swarm optimization Proceedings of the IEEE International Conference Neural Network November 1995 Perth Western Australia 1942\u20131948.","DOI":"10.1109\/ICNN.1995.488968"},{"key":"e_1_2_11_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.advengsoft.2016.01.008"},{"key":"e_1_2_11_5_2","unstructured":"KarabogaD. An idea based on honey bee swarm for numerical optimization 2005 Erciyes University Kayseri Turkey Technical Report TR06."},{"key":"e_1_2_11_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.advengsoft.2013.12.007"},{"key":"e_1_2_11_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.advengsoft.2017.01.004"},{"key":"e_1_2_11_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2011.08.006"},{"key":"e_1_2_11_9_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-015-1849-4"},{"key":"e_1_2_11_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2015.03.003"},{"key":"e_1_2_11_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.advengsoft.2017.07.002"},{"key":"e_1_2_11_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2018.08.011"},{"key":"e_1_2_11_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/tsmc.1986.289288"},{"key":"e_1_2_11_14_2","first-page":"1","article-title":"Synthesis of Mathematical Programming Models with One-Class Evolutionary Strategies","volume":"44","author":"Pawlak T. P.","year":"2018","journal-title":"Swarm and Evolutionary Computation"},{"key":"e_1_2_11_15_2","doi-asserted-by":"crossref","unstructured":"HotaA. R.andPatA. An adaptive quantum-inspired differential evolution algorithm for 0-1 knapsack problem Proceedings of the 2nd World Congresson Nature and Biologically Inspired Computing (NaBIC\u201910) December 2010 Kitakyushu Japan 703\u2013708.","DOI":"10.1109\/NABIC.2010.5716320"},{"key":"e_1_2_11_16_2","first-page":"451","article-title":"Fast evolutionary programming","volume":"3","author":"Yao X.","year":"1996","journal-title":"Evolutionary Programming"},{"key":"e_1_2_11_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/59.574940"},{"key":"e_1_2_11_18_2","doi-asserted-by":"publisher","DOI":"10.1023\/a:1008202821328"},{"key":"e_1_2_11_19_2","doi-asserted-by":"crossref","unstructured":"TanabeR.andFukunagaA. Success-history Based Parameter Adaptation for Differential Evolution Proceedings of the 2013 IEEE Congress on Evolutionary Computation December 2013 Cancun Mexico 71\u201378.","DOI":"10.1109\/CEC.2013.6557555"},{"key":"e_1_2_11_20_2","doi-asserted-by":"crossref","unstructured":"TanabeR.andFukunagaA. S. Improving the Search Performance of SHADE Using Linear Population Size Reduction Proceedings of the 2014 IEEE Congress on Evolutionary Computation (CEC) July 2014 Beijing China 1658\u20131665.","DOI":"10.1109\/CEC.2014.6900380"},{"key":"e_1_2_11_21_2","doi-asserted-by":"crossref","unstructured":"BrestJ. Sepesy MaucecM. andBoskovicB. Improved L-SHADE Algorithm for Single Objective Real-Parameter Optimization Proceedings of the 2016 IEEE Congress on Evolutionary Computation (CEC) July 2016 Vancouver Canada 1188\u20131195.","DOI":"10.1109\/CEC.2016.7743922"},{"key":"e_1_2_11_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.advengsoft.2017.03.014"},{"key":"e_1_2_11_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2009.03.004"},{"key":"e_1_2_11_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2017.06.033"},{"key":"e_1_2_11_25_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2012.08.023"},{"key":"e_1_2_11_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compstruc.2012.09.003"},{"key":"e_1_2_11_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/4235.585893"},{"key":"e_1_2_11_28_2","unstructured":"Xue HuiL. YangY. andXiaL. Solving TSP with shuffled frog leaping algorithm. Intelligent Systems Design and Applications Proceedings of the 2008 Eighth International Conference on Intelligent Systems Design and Applications November 2008 Kaohsuing Taiwan."},{"key":"e_1_2_11_29_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2015.04.001"},{"key":"e_1_2_11_30_2","first-page":"803","article-title":"A new modified shuffle frog leaping algorithm for non-smooth economic dispatch","volume":"12","author":"Narimani M. R.","year":"2011","journal-title":"World Applied Sciences Journal"},{"key":"e_1_2_11_31_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2013.07.006"},{"key":"e_1_2_11_32_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2014.02.010"},{"key":"e_1_2_11_33_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2011.06.014"},{"key":"e_1_2_11_34_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cor.2011.07.010"},{"key":"e_1_2_11_35_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2015.08.025"},{"key":"e_1_2_11_36_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.amc.2012.03.018"},{"key":"e_1_2_11_37_2","first-page":"146","article-title":"Grid task scheduling strategy based on improved shuffled frog leaping algorithm","volume":"37","author":"Yang O.","year":"2011","journal-title":"Computer Engineering"},{"key":"e_1_2_11_38_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2010.07.016"},{"key":"e_1_2_11_39_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2014.08.031"},{"key":"e_1_2_11_40_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.amc.2014.04.035"},{"key":"e_1_2_11_41_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2015.02.003"},{"key":"e_1_2_11_42_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-014-0642-x"},{"key":"e_1_2_11_43_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-015-1164-z"},{"key":"e_1_2_11_44_2","doi-asserted-by":"publisher","DOI":"10.1155\/2016\/5675349"},{"key":"e_1_2_11_45_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2016.09.002"},{"key":"e_1_2_11_46_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2018.05.070"},{"key":"e_1_2_11_47_2","first-page":"325","article-title":"Particle swam optimization with particles having quantum behavior","volume":"1","author":"Sun J.","year":"2004","journal-title":"IEEE Congress on Evolutionary Computation"},{"key":"e_1_2_11_48_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.amc.2008.05.135"},{"key":"e_1_2_11_49_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.amc.2011.09.021"},{"key":"e_1_2_11_50_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2011.10.021"},{"key":"e_1_2_11_51_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2010.11.014"},{"key":"e_1_2_11_52_2","doi-asserted-by":"crossref","unstructured":"HuangL. XiM. L. andZhouY. H. An improved quantum-behaved particle swarm optimization with random selection of the optimal individual Proceedings of the 2010 WASE International Conference on Information Engineering (ICIE) August 2010 Beidai China 189\u2013193.","DOI":"10.1109\/ICIE.2010.336"},{"key":"e_1_2_11_53_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-012-0803-y"},{"key":"e_1_2_11_54_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2006.10.028"},{"key":"e_1_2_11_55_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2015.09.055"},{"key":"e_1_2_11_56_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2014.08.030"},{"key":"e_1_2_11_57_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-015-2014-9"},{"key":"e_1_2_11_58_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cnsns.2016.08.001"},{"key":"e_1_2_11_59_2","doi-asserted-by":"publisher","DOI":"10.1109\/tevc.2013.2297160"},{"key":"e_1_2_11_60_2","volume-title":"Body Tensor Fields in Continuum Mechanics with Applications to Polymer Theology","author":"Lodge A. S.","year":"1974"},{"key":"e_1_2_11_61_2","doi-asserted-by":"crossref","unstructured":"ChuW. GaoX. G. andSorooshianS. Fortify particle swam optimizer (PSO) with principal components analysis: A case study in improving bound-handling for optimizing high-dimensional and complex problems Proceedings of the 2011 IEEE Congress on Evolutionary Computation June 2011 New Orleans LA USA 1644\u20131648.","DOI":"10.1109\/CEC.2011.5949812"},{"key":"e_1_2_11_62_2","doi-asserted-by":"crossref","unstructured":"KuznetsovaA. Pons-MollG. andRosenhahnB. PCA-enhanced stochastic optimization methods Proceedings of the 2012 Joint DAGM (German Association for Pattern Recognition) and OAGM Symposium August 2012 Graz Austria 377\u2013386 Lecture Notes in Computer Sciencehttps:\/\/doi.org\/10.1007\/978-3-642-32717-9_38 2-s2.0-84865832740.","DOI":"10.1007\/978-3-642-32717-9_38"},{"key":"e_1_2_11_63_2","doi-asserted-by":"crossref","unstructured":"YangX. S.andDebS. Cuckoo Search via L\u00b4evy Flights Proceedings of the 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC) December 2009 Coimbatore India 210\u2013214.","DOI":"10.1109\/NABIC.2009.5393690"},{"key":"e_1_2_11_64_2","first-page":"31","article-title":"Enhancing differential evolution utilizing eigenvector-based crossover operator","volume":"19","author":"Guo S. M.","year":"2014","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"e_1_2_11_65_2","doi-asserted-by":"crossref","unstructured":"AwadN. H. AliM. Z. SuganthanP. N. andReynoldsR. G. An Ensemble Sinusoidal Parameter Adaptation Incorporated with L-SHADE for Solving CEC2014 Benchmark Problems Proceedings of the 2016 IEEE Congress on Evolutionary Computation (CEC) July 2016 Vancouver BC Canada 2958\u20132965.","DOI":"10.1109\/CEC.2016.7744163"},{"key":"e_1_2_11_66_2","first-page":"281","article-title":"Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization","volume":"201212","author":"Liang J. J.","year":"2013","journal-title":"Computational Intelligence Laboratory"},{"key":"e_1_2_11_67_2","article-title":"Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization","volume":"635","author":"Liang J. J.","year":"2013","journal-title":"Computational Intelligence Laboratory"},{"key":"e_1_2_11_68_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2018.07.039"},{"key":"e_1_2_11_69_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2017.06.033"},{"key":"e_1_2_11_70_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11721-013-0088-5"},{"key":"e_1_2_11_71_2","doi-asserted-by":"publisher","DOI":"10.1061\/(asce)0733-9496(2003)129:3(210)"},{"key":"e_1_2_11_72_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2015.12.022"},{"key":"e_1_2_11_73_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2012.11.013"},{"key":"e_1_2_11_74_2","doi-asserted-by":"publisher","DOI":"10.1109\/tevc.2008.927706"},{"key":"e_1_2_11_75_2","doi-asserted-by":"publisher","DOI":"10.1109\/tsmcb.2009.2015956"},{"key":"e_1_2_11_76_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2011.02.002"},{"key":"e_1_2_11_77_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-2440-0"},{"key":"e_1_2_11_78_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-018-3662-3"},{"key":"e_1_2_11_79_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2015.07.045"},{"key":"e_1_2_11_80_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijnonlinmec.2019.103288"}],"container-title":["Computational Intelligence and Neuroscience"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2021\/8928182.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2021\/8928182.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2021\/8928182","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,14]],"date-time":"2024-09-14T12:42:41Z","timestamp":1726317761000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2021\/8928182"}},"subtitle":[],"editor":[{"given":"Mario","family":"Versaci","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":80,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["10.1155\/2021\/8928182"],"URL":"https:\/\/doi.org\/10.1155\/2021\/8928182","archive":["Portico"],"relation":{},"ISSN":["1687-5265","1687-5273"],"issn-type":[{"value":"1687-5265","type":"print"},{"value":"1687-5273","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1]]},"assertion":[{"value":"2021-07-16","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-11-15","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-12-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"8928182"}}