{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T03:38:53Z","timestamp":1768448333137,"version":"3.49.0"},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,10,8]],"date-time":"2022-10-08T00:00:00Z","timestamp":1665187200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,10,8]],"date-time":"2022-10-08T00:00:00Z","timestamp":1665187200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100006999","name":"Nuclear Energy University Program","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006999","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2023,2]]},"DOI":"10.1007\/s00521-022-07878-y","type":"journal-article","created":{"date-parts":[[2022,10,8]],"date-time":"2022-10-08T04:02:42Z","timestamp":1665201762000},"page":"3221-3243","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Animorphic ensemble optimization: a large-scale island model"],"prefix":"10.1007","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0999-0111","authenticated-orcid":false,"given":"Dean","family":"Price","sequence":"first","affiliation":[]},{"given":"Majdi I.","family":"Radaideh","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,8]]},"reference":[{"key":"7878_CR1","doi-asserted-by":"publisher","DOI":"10.1002\/9781119454816","volume-title":"Engineering optimization: theory and practice","author":"SS Rao","year":"2019","unstructured":"Rao SS (2019) Engineering optimization: theory and practice. John Wiley & Sons, London"},{"key":"7878_CR2","doi-asserted-by":"crossref","unstructured":"Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN\u201995\u2014international conference on neural networks, vol 4. IEEE, pp 1942\u20131948","DOI":"10.1109\/ICNN.1995.488968"},{"key":"7878_CR3","doi-asserted-by":"crossref","unstructured":"Sivanandam S, Deepa S (2008) Genetic algorithms. In: Introduction to genetic algorithms. Springer, Berlin, pp 15\u201337","DOI":"10.1007\/978-3-540-73190-0_2"},{"issue":"4","key":"7878_CR4","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1023\/A:1008202821328","volume":"11","author":"R Storn","year":"1997","unstructured":"Storn R, Price K (1997) Differential evolution\u2014a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341\u2013359","journal-title":"J Global Optim"},{"issue":"1","key":"7878_CR5","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1023\/A:1015059928466","volume":"1","author":"H-G Beyer","year":"2002","unstructured":"Beyer H-G, Schwefel H-P (2002) Evolution strategies\u2014a comprehensive introduction. Nat Comput 1(1):3\u201352","journal-title":"Nat Comput"},{"key":"7878_CR6","doi-asserted-by":"crossref","unstructured":"Hansen N (2006) The CMA evolution strategy: a comparing review. Towards a new evolutionary computation, pp 75\u2013102","DOI":"10.1007\/3-540-32494-1_4"},{"key":"7878_CR7","doi-asserted-by":"crossref","unstructured":"Dorigo M, Di Caro G (1999) Ant colony optimization: a new meta-heuristic. In: Proceedings of the 1999 congress on evolutionary computation-CEC99 (Cat. No. 99TH8406), vol 2. IEEE, pp 1470\u20131477","DOI":"10.1109\/CEC.1999.782657"},{"issue":"3","key":"7878_CR8","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1007\/s10898-007-9149-x","volume":"39","author":"D Karaboga","year":"2007","unstructured":"Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Global Optim 39(3):459\u2013471","journal-title":"J Global Optim"},{"key":"7878_CR9","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","volume":"69","author":"S Mirjalili","year":"2014","unstructured":"Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46\u201361","journal-title":"Adv Eng Softw"},{"key":"7878_CR10","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1016\/j.knosys.2015.07.006","volume":"89","author":"S Mirjalili","year":"2015","unstructured":"Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl Based Syst 89:228\u2013249","journal-title":"Knowl Based Syst"},{"issue":"2","key":"7878_CR11","doi-asserted-by":"publisher","first-page":"849","DOI":"10.1016\/j.asoc.2007.07.002","volume":"8","author":"Y-T Kao","year":"2008","unstructured":"Kao Y-T, Zahara E (2008) A hybrid genetic algorithm and particle swarm optimization for multimodal functions. Appl Soft Comput 8(2):849\u2013857","journal-title":"Appl Soft Comput"},{"key":"7878_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.106836","volume":"217","author":"MI Radaideh","year":"2021","unstructured":"Radaideh MI, Shirvan K (2021) Rule-based reinforcement learning methodology to inform evolutionary algorithms for constrained optimization of engineering applications. Knowl Based Syst 217:106836","journal-title":"Knowl Based Syst"},{"issue":"4","key":"7878_CR13","doi-asserted-by":"publisher","first-page":"434","DOI":"10.1016\/j.autcon.2008.10.007","volume":"18","author":"P-H Chen","year":"2009","unstructured":"Chen P-H, Shahandashti SM (2009) Hybrid of genetic algorithm and simulated annealing for multiple project scheduling with multiple resource constraints. Autom Constr 18(4):434\u2013443","journal-title":"Autom Constr"},{"key":"7878_CR14","doi-asserted-by":"crossref","unstructured":"Ma PC, Tao F, Liu YL, Zhang L, Lu HX, Ding Z (2014) A hybrid particle swarm optimization and simulated annealing algorithm for job-shop scheduling. In: 2014 IEEE international conference on automation science and engineering (CASE). IEEE, pp 125\u2013130","DOI":"10.1109\/CoASE.2014.6899315"},{"key":"7878_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.106926","volume":"222","author":"G Dhiman","year":"2021","unstructured":"Dhiman G (2021) SSC: a hybrid nature-inspired meta-heuristic optimization algorithm for engineering applications. Knowl Based Syst 222:106926","journal-title":"Knowl Based Syst"},{"key":"7878_CR16","unstructured":"Radaideh MI, Du K, Seurin P, Seyler D, Gu X, Wang H, Shirvan K (2021) NEORL: NeuroEvolution optimization with reinforcement learning. arXiv:2112.07057"},{"key":"7878_CR17","doi-asserted-by":"publisher","unstructured":"Price D, Radaideh M, Kochunas B Multi-objective optimization of nuclear microreactor control system operation with swarm and evolutionary algorithms. Deep Blue Documents. https:\/\/doi.org\/10.7302\/3786","DOI":"10.7302\/3786"},{"issue":"1","key":"7878_CR18","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1109\/4235.585893","volume":"1","author":"DH Wolpert","year":"1997","unstructured":"Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67\u201382","journal-title":"IEEE Trans Evol Comput"},{"key":"7878_CR19","doi-asserted-by":"crossref","unstructured":"Wolpert DH (2002) The supervised learning no-free-lunch theorems. Soft Comput Ind 25\u201342","DOI":"10.1007\/978-1-4471-0123-9_3"},{"key":"7878_CR20","doi-asserted-by":"publisher","first-page":"695","DOI":"10.1016\/j.swevo.2018.08.015","volume":"44","author":"G Wu","year":"2019","unstructured":"Wu G, Mallipeddi R, Suganthan PN (2019) Ensemble strategies for population-based optimization algorithms-a survey. Swarm Evol Comput 44:695\u2013711","journal-title":"Swarm Evol Comput"},{"key":"7878_CR21","doi-asserted-by":"crossref","unstructured":"Skolicki Z (2005) An analysis of island models in evolutionary computation. In: Proceedings of the 7th annual workshop on Genetic and evolutionary computation, pp 386\u2013389","DOI":"10.1145\/1102256.1102343"},{"issue":"1","key":"7878_CR22","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1109\/4235.985692","volume":"6","author":"M Clerc","year":"2002","unstructured":"Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1):58\u201373","journal-title":"IEEE Trans Evol Comput"},{"key":"7878_CR23","doi-asserted-by":"crossref","unstructured":"Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: 1998 IEEE international conference on evolutionary computation proceedings. IEEE world congress on computational intelligence (Cat. No. 98TH8360). IEEE, pp 69\u201373","DOI":"10.1109\/ICEC.1998.699146"},{"key":"7878_CR24","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.advengsoft.2016.01.008","volume":"95","author":"S Mirjalili","year":"2016","unstructured":"Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51\u201367","journal-title":"Adv Eng Softw"},{"key":"7878_CR25","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/j.advengsoft.2017.07.002","volume":"114","author":"S Mirjalili","year":"2017","unstructured":"Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163\u2013191","journal-title":"Adv Eng Softw"},{"issue":"1","key":"7878_CR26","first-page":"19","volume":"7","author":"R Rao","year":"2016","unstructured":"Rao R (2016) Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput 7(1):19\u201334","journal-title":"Int J Ind Eng Comput"},{"key":"7878_CR27","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1016\/j.ins.2015.09.009","volume":"329","author":"G Wu","year":"2016","unstructured":"Wu G, Mallipeddi R, Suganthan PN, Wang R, Chen H (2016) Differential evolution with multi-population based ensemble of mutation strategies. Inf Sci 329:329\u2013345","journal-title":"Inf Sci"},{"issue":"20","key":"7878_CR28","doi-asserted-by":"publisher","first-page":"4515","DOI":"10.1016\/j.ins.2010.07.013","volume":"181","author":"Y Wang","year":"2011","unstructured":"Wang Y, Li B, Weise T, Wang J, Yuan B, Tian Q (2011) Self-adaptive learning based particle swarm optimization. Inf Sci 181(20):4515\u20134538","journal-title":"Inf Sci"},{"issue":"2","key":"7878_CR29","doi-asserted-by":"publisher","first-page":"567","DOI":"10.1007\/s13369-016-2270-8","volume":"42","author":"H Rakhshani","year":"2017","unstructured":"Rakhshani H, Rahati A (2017) Intelligent multiple search strategy cuckoo algorithm for numerical and engineering optimization problems. Arab J Sci Eng 42(2):567\u2013593","journal-title":"Arab J Sci Eng"},{"key":"7878_CR30","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1016\/j.asoc.2017.02.007","volume":"55","author":"N Lynn","year":"2017","unstructured":"Lynn N, Suganthan PN (2017) Ensemble particle swarm optimizer. Appl Soft Comput 55:533\u2013548","journal-title":"Appl Soft Comput"},{"key":"7878_CR31","first-page":"680","volume":"222","author":"SM Elsayed","year":"2013","unstructured":"Elsayed SM, Sarker RA, Essam DL (2013) Adaptive configuration of evolutionary algorithms for constrained optimization. Appl Math Comput 222:680\u2013711","journal-title":"Appl Math Comput"},{"issue":"2","key":"7878_CR32","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1109\/TEVC.2008.924428","volume":"13","author":"JA Vrugt","year":"2008","unstructured":"Vrugt JA, Robinson BA, Hyman JM (2008) Self-adaptive multimethod search for global optimization in real-parameter spaces. IEEE Trans Evol Comput 13(2):243\u2013259","journal-title":"IEEE Trans Evol Comput"},{"key":"7878_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107379","volume":"230","author":"RM Adnan","year":"2021","unstructured":"Adnan RM, Mostafa RR, Kisi O, Yaseen ZM, Shahid S, Zounemat-Kermani M (2021) Improving streamflow prediction using a new hybrid elm model combined with hybrid particle swarm optimization and grey wolf optimization. Knowl Based Syst 230:107379","journal-title":"Knowl Based Syst"},{"issue":"3","key":"7878_CR34","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1007\/s12293-015-0159-9","volume":"7","author":"SY Yuen","year":"2015","unstructured":"Yuen SY, Zhang X (2015) On composing an algorithm portfolio. Memet Comput 7(3):203\u2013214","journal-title":"Memet Comput"},{"key":"7878_CR35","doi-asserted-by":"crossref","unstructured":"Yuen SY, Zhang X (2014) Multiobjective evolutionary algorithm portfolio: choosing suitable algorithm for multiobjective optimization problem. In: 2014 IEEE congress on evolutionary computation (CEC). IEEE, pp 1967\u20131973","DOI":"10.1109\/CEC.2014.6900470"},{"key":"7878_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107044","volume":"223","author":"A Seyyedabbasi","year":"2021","unstructured":"Seyyedabbasi A, Aliyev R, Kiani F, Gulle MU, Basyildiz H, Shah MA (2021) Hybrid algorithms based on combining reinforcement learning and metaheuristic methods to solve global optimization problems. Knowl Based Syst 223:107044","journal-title":"Knowl Based Syst"},{"key":"7878_CR37","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.swevo.2015.05.002","volume":"24","author":"N Lynn","year":"2015","unstructured":"Lynn N, Suganthan PN (2015) Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation. Swarm Evol Comput 24:11\u201324","journal-title":"Swarm Evol Comput"},{"key":"7878_CR38","doi-asserted-by":"crossref","unstructured":"Mallipeddi R, Iacca G, Suganthan PN, Neri F, Mininno E (2011) Ensemble strategies in compact differential evolution. In: 2011 IEEE congress of evolutionary computation (CEC). IEEE, pp 1972\u20131977","DOI":"10.1109\/CEC.2011.5949857"},{"key":"7878_CR39","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1016\/j.asoc.2015.04.019","volume":"33","author":"MZ Ali","year":"2015","unstructured":"Ali MZ, Awad NH, Suganthan PN (2015) Multi-population differential evolution with balanced ensemble of mutation strategies for large-scale global optimization. Appl Soft Comput 33:304\u2013327","journal-title":"Appl Soft Comput"},{"issue":"9","key":"7878_CR40","doi-asserted-by":"publisher","first-page":"1571","DOI":"10.1016\/j.ins.2010.01.007","volume":"180","author":"R Mallipeddi","year":"2010","unstructured":"Mallipeddi R, Mallipeddi S, Suganthan PN (2010) Ensemble strategies with adaptive evolutionary programming. Inf Sci 180(9):1571\u20131581","journal-title":"Inf Sci"},{"issue":"5296","key":"7878_CR41","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1126\/science.275.5296.51","volume":"275","author":"BA Huberman","year":"1997","unstructured":"Huberman BA, Lukose RM, Hogg T (1997) An economics approach to hard computational problems. Science 275(5296):51\u201354","journal-title":"Science"},{"issue":"5","key":"7878_CR42","doi-asserted-by":"publisher","first-page":"782","DOI":"10.1109\/TEVC.2010.2040183","volume":"14","author":"F Peng","year":"2010","unstructured":"Peng F, Tang K, Chen G, Yao X (2010) Population-based algorithm portfolios for numerical optimization. IEEE Trans Evol Comput 14(5):782\u2013800","journal-title":"IEEE Trans Evol Comput"},{"key":"7878_CR43","doi-asserted-by":"crossref","unstructured":"L\u00e4ssig J, Sudholt D (2011) Analysis of speedups in parallel evolutionary algorithms for combinatorial optimization. In: International symposium on algorithms and computation. Springer, Berlin, pp 405\u2013414","DOI":"10.1007\/978-3-642-25591-5_42"},{"issue":"4","key":"7878_CR44","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1109\/TEVC.2010.2064322","volume":"15","author":"L Araujo","year":"2010","unstructured":"Araujo L, Merelo JJ (2010) Diversity through multiculturality: assessing migrant choice policies in an island model. IEEE Trans Evol Comput 15(4):456\u2013469","journal-title":"IEEE Trans Evol Comput"},{"key":"7878_CR45","doi-asserted-by":"crossref","unstructured":"Qu BY, Gouthanan P, Suganthan PN (2010) Dynamic grouping crowding differential evolution with ensemble of parameters for multi-modal optimization. In: International conference on swarm, evolutionary, and memetic computing. Springer, Berlin, pp 19\u201328","DOI":"10.1007\/978-3-642-17563-3_3"},{"issue":"9","key":"7878_CR46","first-page":"3356","volume":"215","author":"MF Tasgetiren","year":"2010","unstructured":"Tasgetiren MF, Suganthan PN, Pan Q-K (2010) An ensemble of discrete differential evolution algorithms for solving the generalized traveling salesman problem. Appl Math Comput 215(9):3356\u20133368","journal-title":"Appl Math Comput"},{"issue":"21","key":"7878_CR47","doi-asserted-by":"publisher","first-page":"7652","DOI":"10.1016\/j.eswa.2015.06.004","volume":"42","author":"KZ Gao","year":"2015","unstructured":"Gao KZ, Suganthan PN, Chua TJ, Chong CS, Cai TX, Pan QK (2015) A two-stage artificial bee colony algorithm scheduling flexible job-shop scheduling problem with new job insertion. Expert Syst Appl 42(21):7652\u20137663","journal-title":"Expert Syst Appl"},{"key":"7878_CR48","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1016\/j.ins.2014.01.051","volume":"277","author":"SM Elsayed","year":"2014","unstructured":"Elsayed SM, Sarker RA, Mezura-Montes E (2014) Self-adaptive mix of particle swarm methodologies for constrained optimization. Inf Sci 277:216\u2013233","journal-title":"Inf Sci"},{"key":"7878_CR49","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.engappai.2015.05.009","volume":"44","author":"H Ma","year":"2015","unstructured":"Ma H, Su S, Simon D, Fei M (2015) Ensemble multi-objective biogeography-based optimization with application to automated warehouse scheduling. Eng Appl Artif Intell 44:79\u201390","journal-title":"Eng Appl Artif Intell"},{"issue":"2","key":"7878_CR50","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1080\/0022250X.1979.9989891","volume":"6","author":"PM Sommers","year":"1979","unstructured":"Sommers PM, Conlisk J (1979) Eigenvalue immobility measures for Markov chains. J Math Sociol 6(2):253\u2013276","journal-title":"J Math Sociol"},{"issue":"2","key":"7878_CR51","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1177\/004912417900800203","volume":"8","author":"J Conlisk","year":"1979","unstructured":"Conlisk J, Sommers P (1979) Eigenvector status proxies in Markov chain mobility models. Sociol Methods Res 8(2):159\u2013178","journal-title":"Sociol Methods Res"},{"key":"7878_CR52","doi-asserted-by":"crossref","unstructured":"Shorrocks AF (1978) The measurement of mobility. Econometrica J Econometric Soc 1013\u20131024","DOI":"10.2307\/1911433"},{"issue":"2","key":"7878_CR53","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1109\/MSP.2005.1406483","volume":"22","author":"SU Pillai","year":"2005","unstructured":"Pillai SU, Suel T, Cha S (2005) The Perron\u2013Frobenius theorem: some of its applications. IEEE Signal Process Mag 22(2):62\u201375","journal-title":"IEEE Signal Process Mag"},{"key":"7878_CR54","unstructured":"Awad NH, Ali MZ, Suganthan PN, Liang JJ, Qu BY (2017) Problem definitions and evaluation criteria for the CEC 2017 special session and competition on single objective real-parameter numerical optimization. Tech. rep., Nanyang Technological University, Singapore"},{"key":"7878_CR55","doi-asserted-by":"crossref","unstructured":"Woolson RF (2007) Wilcoxon signed-rank test. Wiley encyclopedia of clinical trials, pp 1\u20133","DOI":"10.1002\/9780471462422.eoct979"},{"issue":"2","key":"7878_CR56","doi-asserted-by":"publisher","first-page":"1679","DOI":"10.1016\/j.asoc.2010.04.024","volume":"11","author":"R Mallipeddi","year":"2011","unstructured":"Mallipeddi R, Suganthan PN, Pan Q-K, Tasgetiren MF (2011) Differential evolution algorithm with ensemble of parameters and mutation strategies. Appl Soft Comput 11(2):1679\u20131696","journal-title":"Appl Soft Comput"},{"key":"7878_CR57","unstructured":"Awad N, Ali M, Liang J, Qu B, Suganthan P, Definitions P. Evaluation criteria for the CEC 2017 special session and competition on single objective real-parameter numerical optimization, Tech. Rep"},{"key":"7878_CR58","unstructured":"Yang X-S. Test problems in optimization. arXiv:1008.0549"},{"key":"7878_CR59","first-page":"2171","volume":"13","author":"F-A Fortin","year":"2012","unstructured":"Fortin F-A, De Rainville F-M, Gardner M-A, Parizeau M, Gagn\u00e9 C (2012) DEAP: evolutionary algorithms made easy. J Mach Learn Res 13:2171\u20132175","journal-title":"J Mach Learn Res"},{"key":"7878_CR60","volume-title":"Nature-inspired optimization algorithms","author":"X-S Yang","year":"2020","unstructured":"Yang X-S (2020) Nature-inspired optimization algorithms. Academic Press, London"},{"key":"7878_CR61","doi-asserted-by":"crossref","unstructured":"Xie Z, Huang X, Liu W (2022) Subpopulation particle swarm optimization with a hybrid mutation strategy. Comput Intell Neurosci","DOI":"10.1155\/2022\/9599417"},{"issue":"9","key":"7878_CR62","doi-asserted-by":"publisher","first-page":"883","DOI":"10.1016\/j.advengsoft.2009.01.004","volume":"40","author":"O Begambre","year":"2009","unstructured":"Begambre O, Laier JE (2009) A hybrid particle swarm optimization\u2013simplex algorithm (PSOS) for structural damage identification. Adv Eng Softw 40(9):883\u2013891","journal-title":"Adv Eng Softw"},{"issue":"2","key":"7878_CR63","first-page":"150","volume":"4","author":"M Jamil","year":"2013","unstructured":"Jamil M, Yang X-S (2013) A literature survey of benchmark functions for global optimisation problems. Int J Math Model Numer Optim 4(2):150\u2013194","journal-title":"Int J Math Model Numer Optim"},{"issue":"3","key":"7878_CR64","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1016\/0303-2647(96)01621-8","volume":"39","author":"R Salomon","year":"1996","unstructured":"Salomon R (1996) Re-evaluating genetic algorithm performance under coordinate rotation of benchmark functions. A survey of some theoretical and practical aspects of genetic algorithms. Biosystems 39(3):263\u2013278","journal-title":"Biosystems"},{"key":"7878_CR65","doi-asserted-by":"crossref","unstructured":"Beyer H-G, Finck S (2012) Happycat\u2014a simple function class where well-known direct search algorithms do fail. In: International conference on parallel problem solving from nature. Springer, Berlin, pp 367\u2013376","DOI":"10.1007\/978-3-642-32937-1_37"},{"issue":"2","key":"7878_CR66","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1007\/s10898-004-1936-z","volume":"33","author":"M Laguna","year":"2005","unstructured":"Laguna M, Marti R (2005) Experimental testing of advanced scatter search designs for global optimization of multimodal functions. J Global Optim 33(2):235\u2013255","journal-title":"J Global Optim"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07878-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07878-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07878-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,28]],"date-time":"2023-01-28T13:50:21Z","timestamp":1674913821000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07878-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,8]]},"references-count":66,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,2]]}},"alternative-id":["7878"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07878-y","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,8]]},"assertion":[{"value":"15 June 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 September 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 October 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflicts of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}