{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T04:44:59Z","timestamp":1774673099124,"version":"3.50.1"},"reference-count":91,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2019,7,12]],"date-time":"2019-07-12T00:00:00Z","timestamp":1562889600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,7,12]],"date-time":"2019-07-12T00:00:00Z","timestamp":1562889600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2020,2]]},"DOI":"10.1007\/s13042-019-00979-6","type":"journal-article","created":{"date-parts":[[2019,7,12]],"date-time":"2019-07-12T14:02:58Z","timestamp":1562940178000},"page":"359-401","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["A hybrid optimization approach based on clustering and chaotic sequences"],"prefix":"10.1007","volume":"11","author":[{"given":"Jorge","family":"G\u00e1lvez","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0358-6049","authenticated-orcid":false,"given":"Erik","family":"Cuevas","sequence":"additional","affiliation":[]},{"given":"H\u00e9ctor","family":"Becerra","sequence":"additional","affiliation":[]},{"given":"Omar","family":"Avalos","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,7,12]]},"reference":[{"key":"979_CR1","doi-asserted-by":"crossref","DOI":"10.1002\/9780470640425","volume-title":"Engineering optimization: an introduction with metaheuristic applications","author":"X-S Yang","year":"2010","unstructured":"Yang X-S (2010) Engineering optimization: an introduction with metaheuristic applications. Wiley, New York"},{"key":"979_CR2","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/S0377-0427(00)00425-8","volume":"124","author":"PM Pardalos","year":"2000","unstructured":"Pardalos PM, Romeijn HE, Tuy H (2000) Recent developments and trends in global optimization. J Comput Appl Math 124:209\u2013228","journal-title":"J Comput Appl Math"},{"key":"979_CR3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2014\/827206","volume":"2014","author":"E Cuevas","year":"2014","unstructured":"Cuevas E, G\u00e1lvez J, Hinojosa S, Avalos O, Zald\u00edvar D, P\u00e9rez-Cisneros M (2014) A comparison of evolutionary computation techniques for IIR model identification. J Appl Math 2014:1\u20139","journal-title":"J Appl Math"},{"key":"979_CR4","first-page":"382","volume":"185","author":"Y Ji","year":"2007","unstructured":"Ji Y, Zhang K-C, Qu S-J (2007) A deterministic global optimization algorithm. Appl Math Comput 185:382\u2013387","journal-title":"Appl Math Comput"},{"key":"979_CR5","volume-title":"Adaptation in natural and artificial systems","author":"JH Holland","year":"1975","unstructured":"Holland JH (1975) Adaptation in natural and artificial systems. Univ. Michigan Press, Ann Arbor"},{"key":"979_CR6","volume-title":"Genetic algorithms in search, optimization and machine learning","author":"DE Goldberg","year":"1989","unstructured":"Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Boston"},{"key":"979_CR7","unstructured":"Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proc. IEEE int. conf. neural networks, vol 4, pp 1942\u20131948"},{"key":"979_CR8","unstructured":"Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Comput. Eng. Dep. Eng. Fac. Erciyes Univ"},{"key":"979_CR9","unstructured":"Yang X-S, Deb S (2009) Cuckoo search via L\u00e9vy flights. In: Proc. world congr. nat. biol. inspired comput. (NABIC\u201909), pp 210\u2013214"},{"key":"979_CR10","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1023\/A:1022452626305","volume":"25","author":"SI Birbil","year":"2003","unstructured":"Birbil SI, Fang S-C (2003) An electromagnetism-like mechanism for global optimization. J Glob Optim 25:263\u2013282","journal-title":"J Glob Optim"},{"issue":"13","key":"979_CR11","doi-asserted-by":"crossref","first-page":"2232","DOI":"10.1016\/j.ins.2009.03.004","volume":"179","author":"E Rashedi","year":"2009","unstructured":"Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci (NY) 179(13):2232\u20132248","journal-title":"Inf Sci (NY)"},{"key":"979_CR12","doi-asserted-by":"crossref","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 Glob Optim 11:341\u2013359","journal-title":"J Glob Optim"},{"key":"979_CR13","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1007\/3-540-32494-1_4","volume-title":"Towards a new evolutionary computation","author":"N Hansen","year":"2006","unstructured":"Hansen N (2006) The CMA evolution strategy: a comparing review. In: Lozano JA, Larra\u00f1aga P, Inza I, Bengoetxea E (eds) Towards a new evolutionary computation. Springer, Berlin, pp 75\u2013102"},{"key":"979_CR14","doi-asserted-by":"crossref","first-page":"1012","DOI":"10.1016\/j.ins.2016.07.022","volume":"367\u2013368","author":"L Cui","year":"2016","unstructured":"Cui L, Li G, Lin Q, Du Z, Gao W, Chen J, Lu N (2016) A novel artificial bee colony algorithm with depth-first search framework and elite-guided search equation. Inf Sci (NY) 367\u2013368:1012\u20131044","journal-title":"Inf Sci (NY)"},{"key":"979_CR15","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.cor.2015.09.006","volume":"67","author":"L Cui","year":"2016","unstructured":"Cui L, Li G, Lin Q, Chen J, Lu N (2016) Adaptive differential evolution algorithm with novel mutation strategies in multiple sub-populations. Comput Oper Res 67:155\u2013173","journal-title":"Comput Oper Res"},{"issue":"2","key":"979_CR16","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1016\/j.ejor.2008.07.025","volume":"197","author":"KC Tan","year":"2009","unstructured":"Tan KC, Chiam SC, Mamun AA, Goh CK (2009) Balancing exploration and exploitation with adaptive variation for evolutionary multi-objective optimization. Eur J Oper Res 197(2):701\u2013713","journal-title":"Eur J Oper Res"},{"issue":"2","key":"979_CR17","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1109\/TEVC.2005.843751","volume":"9","author":"E Alba","year":"2005","unstructured":"Alba E, Dorronsoro B (2005) The exploration\/exploitation tradeoff in dynamic cellular genetic algorithms. IEEE Trans Evol Comput 9(2):126\u2013142","journal-title":"IEEE Trans Evol Comput"},{"issue":"2","key":"979_CR18","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1177\/1059712309103566","volume":"17","author":"I Paenke","year":"2009","unstructured":"Paenke I, Jin Y, Branke J (2009) Balancing population- and individual-level adaptation in changing environments. Adapt Behav 17(2):153\u2013174","journal-title":"Adapt Behav"},{"issue":"2","key":"979_CR19","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1007\/s10489-013-0458-0","volume":"40","author":"E Cuevas","year":"2014","unstructured":"Cuevas E, Echavarr\u00eda A, Ram\u00edrez-Orteg\u00f3n MA (2014) An optimization algorithm inspired by the States of Matter that improves the balance between exploration and exploitation. Appl Intell 40(2):256\u2013272","journal-title":"Appl Intell"},{"issue":"7-8","key":"979_CR20","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1002\/zamm.19620420718","volume":"42","author":"L Bittner","year":"1962","unstructured":"Bittner L (1962) R. Bellman, Adaptive control processes. A guided tour. XVI\u2009+\u2009255 S. Princeton, N. J., 1961. Princeton University Press. Preis geb. $ 6.50. ZAMM Z Angew Math Mech 42(7-8):364\u2013365","journal-title":"ZAMM Z Angew Math Mech"},{"key":"979_CR21","first-page":"281","volume":"13","author":"J Bergstra","year":"2012","unstructured":"Bergstra J, Ca Y (2012) Random search for hyper-parameter optimization. J Mach Learn Res 13:281\u2013305","journal-title":"J Mach Learn Res"},{"key":"979_CR22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/978-3-540-78295-7","volume-title":"Hybrid metaheuristics: an introduction","author":"C Blum","year":"2008","unstructured":"Blum C, Roli A (2008) Hybrid metaheuristics: an introduction. Springer, Berlin, pp 1\u201330"},{"issue":"6","key":"979_CR23","doi-asserted-by":"crossref","first-page":"4135","DOI":"10.1016\/j.asoc.2011.02.032","volume":"11","author":"C Blum","year":"2011","unstructured":"Blum C, Puchinger J, Raidl GR, Roli A (2011) Hybrid metaheuristics in combinatorial optimization: a survey. Appl Soft Comput 11(6):4135\u20134151","journal-title":"Appl Soft Comput"},{"key":"979_CR24","first-page":"1","volume-title":"Hybrid evolutionary algorithms: methodologies, architectures, and reviews","author":"C Grosan","year":"2007","unstructured":"Grosan C, Abraham A (2007) Hybrid evolutionary algorithms: methodologies, architectures, and reviews. Springer, Berlin, pp 1\u201317"},{"key":"979_CR25","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.engappai.2014.01.011","volume":"30","author":"H Ma","year":"2014","unstructured":"Ma H, Simon D, Fei M, Shu X, Chen Z (2014) Hybrid biogeography-based evolutionary algorithms. Eng Appl Artif Intell 30:213\u2013224","journal-title":"Eng Appl Artif Intell"},{"issue":"8","key":"979_CR26","doi-asserted-by":"crossref","first-page":"1340","DOI":"10.1016\/j.engappai.2010.02.005","volume":"23","author":"T Niknam","year":"2010","unstructured":"Niknam T, Farsani EA (2010) A hybrid self-adaptive particle swarm optimization and modified shuffled frog leaping algorithm for distribution feeder reconfiguration. Eng Appl Artif Intell 23(8):1340\u20131349","journal-title":"Eng Appl Artif Intell"},{"key":"979_CR27","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.swevo.2017.02.001","volume":"34","author":"X Lai","year":"2017","unstructured":"Lai X, Zhou Y (2017) Success rates analysis of three hybrid algorithms on SAT instances. Swarm Evol Comput 34:119\u2013129","journal-title":"Swarm Evol Comput"},{"key":"979_CR28","doi-asserted-by":"crossref","first-page":"955","DOI":"10.1016\/j.asoc.2015.10.053","volume":"38","author":"F Zhong","year":"2016","unstructured":"Zhong F, Yuan B, Li B (2016) A hybrid evolutionary algorithm for multiobjective variation tolerant logic mapping on nanoscale crossbar architectures. Appl Soft Comput 38:955\u2013966","journal-title":"Appl Soft Comput"},{"issue":"2","key":"979_CR29","doi-asserted-by":"crossref","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":"979_CR30","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1016\/j.asoc.2016.09.051","volume":"52","author":"SN Chaurasia","year":"2017","unstructured":"Chaurasia SN, Singh A (2017) Hybrid evolutionary approaches for the single machine order acceptance and scheduling problem. Appl Soft Comput 52:725\u2013747","journal-title":"Appl Soft Comput"},{"key":"979_CR31","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.ins.2016.02.051","volume":"352\u2013353","author":"Y Jin","year":"2016","unstructured":"Jin Y, Hao J-K (2016) Hybrid evolutionary search for the minimum sum coloring problem of graphs. Inf Sci (NY) 352\u2013353:15\u201334","journal-title":"Inf Sci (NY)"},{"key":"979_CR32","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1016\/j.asoc.2015.04.033","volume":"34","author":"Q Wu","year":"2015","unstructured":"Wu Q, Wang Y, L\u00fc Z (2015) A tabu search based hybrid evolutionary algorithm for the max-cut problem. Appl Soft Comput 34:827\u2013837","journal-title":"Appl Soft Comput"},{"key":"979_CR33","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.knosys.2016.12.026","volume":"120","author":"ZY Lim","year":"2017","unstructured":"Lim ZY, Ponnambalam SG, Izui K (2017) Multi-objective hybrid algorithms for layout optimization in multi-robot cellular manufacturing systems. Knowl Based Syst 120:87\u201398","journal-title":"Knowl Based Syst"},{"key":"979_CR34","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/j.asoc.2016.05.021","volume":"47","author":"J Liu","year":"2016","unstructured":"Liu J, Zhang S, Wu C, Liang J, Wang X, Teo KL (2016) A hybrid approach to constrained global optimization. Appl Soft Comput 47:281\u2013294","journal-title":"Appl Soft Comput"},{"key":"979_CR35","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1016\/j.asoc.2017.04.005","volume":"57","author":"WK Mashwani","year":"2017","unstructured":"Mashwani WK, Salhi A, Yeniay O, Jan MA, Khanum RA (2017) Hybrid adaptive evolutionary algorithm based on decomposition. Appl Soft Comput 57:363\u2013378","journal-title":"Appl Soft Comput"},{"issue":"3","key":"979_CR36","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1016\/j.cor.2009.02.010","volume":"37","author":"M Lozano","year":"2010","unstructured":"Lozano M, Garc\u00eda-Mart\u00ednez C (2010) Hybrid metaheuristics with evolutionary algorithms specializing in intensification and diversification: overview and progress report. Comput Oper Res 37(3):481\u2013497","journal-title":"Comput Oper Res"},{"key":"979_CR37","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.eswa.2016.11.025","volume":"71","author":"P Guo","year":"2017","unstructured":"Guo P, Cheng W, Wang Y (2017) Hybrid evolutionary algorithm with extreme machine learning fitness function evaluation for two-stage capacitated facility location problems. Expert Syst Appl 71:57\u201368","journal-title":"Expert Syst Appl"},{"key":"979_CR38","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.asoc.2016.04.014","volume":"45","author":"R Dash","year":"2016","unstructured":"Dash R, Dash PK (2016) An evolutionary hybrid fuzzy computationally efficient EGARCH model for volatility prediction. Appl Soft Comput 45:40\u201360","journal-title":"Appl Soft Comput"},{"key":"979_CR39","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.measurement.2016.04.052","volume":"90","author":"HA Illias","year":"2016","unstructured":"Illias HA, Chai XR, Abu Bakar AH (2016) Hybrid modified evolutionary particle swarm optimisation-time varying acceleration coefficient-artificial neural network for power transformer fault diagnosis. Measurement 90:94\u2013102","journal-title":"Measurement"},{"key":"979_CR40","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.asoc.2015.09.006","volume":"38","author":"N Verbiest","year":"2016","unstructured":"Verbiest N, Derrac J, Cornelis C, Garc\u00eda S, Herrera F (2016) Evolutionary wrapper approaches for training set selection as preprocessing mechanism for support vector machines: experimental evaluation and support vector analysis. Appl Soft Comput 38:10\u201322","journal-title":"Appl Soft Comput"},{"issue":"2","key":"979_CR41","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1007\/s40745-015-0040-1","volume":"2","author":"D Xu","year":"2015","unstructured":"Xu D, Tian Y (2015) A Comprehensive survey of clustering algorithms. Ann Data Sci 2(2):165\u2013193","journal-title":"Ann Data Sci"},{"key":"979_CR42","series-title":"Lecture notes in computer science","volume-title":"Genetic and evolutionary computation GECCO 2003","author":"F Streichert","year":"2003","unstructured":"Streichert F, Stein G, Ulmer H, Zell A (2003) A clustering based niching method for evolutionary algorithms. In: Cant\u00fa-Paz E et al (eds) Genetic and evolutionary computation GECCO 2003. Lecture notes in computer science. Springer, Berlin"},{"issue":"1","key":"979_CR43","first-page":"33","volume":"7","author":"D Whitley","year":"1998","unstructured":"Whitley D, Rana S, Heckendorn RB (1998) The island model genetic algorithm: on separability, population size and convergence. J Comput Inf Technol 7(1):33\u201347","journal-title":"J Comput Inf Technol"},{"key":"979_CR44","unstructured":"Tasoulis DK, Plagianakos VP, Vrahatis MN (2005) Clustering in evolutionary algorithms to efficiently compute simultaneously local and global minima"},{"issue":"2","key":"979_CR45","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1007\/s00500-014-1262-4","volume":"19","author":"X Liang","year":"2015","unstructured":"Liang X, Li W, Zhang Y, Zhou M (2015) An adaptive particle swarm optimization method based on clustering. Soft Comput 19(2):431\u2013448","journal-title":"Soft Comput"},{"key":"979_CR46","doi-asserted-by":"crossref","unstructured":"Li C, Yang S (2009) A clustering particle swarm optimizer for dynamic optimization. In: 2009 IEEE congress on evolutionary computation, pp 439\u2013446","DOI":"10.1109\/CEC.2009.4982979"},{"key":"979_CR47","doi-asserted-by":"crossref","unstructured":"Weise T, Niemczyk S, Chiong R, Wan M (2011) A framework for multi-model EDAs with model recombination, pp 304\u2013313","DOI":"10.1007\/978-3-642-20525-5_31"},{"key":"979_CR48","doi-asserted-by":"crossref","unstructured":"Tsou C-S, Fang H-H, Chang H-H, Kao C-H (2006) LNCS 4247\u2014an improved particle swarm Pareto optimizer with local search and clustering","DOI":"10.1007\/11903697_51"},{"key":"979_CR49","unstructured":"Coello CAC, Lechuga MS (2002) MOPSO: a proposal for multiple objective particle swarm optimization. In: Proceedings of the 2002 congress on evolutionary computation. CEC\u201902 (Cat. No.02TH8600), vol 2, pp 1051\u20131056"},{"issue":"7","key":"979_CR50","first-page":"1","volume":"49","author":"Y Hua","year":"2018","unstructured":"Hua Y, Jin Y, Hao K (2018) A clustering-based adaptive evolutionary algorithm for multiobjective optimization with irregular pareto fronts. IEEE Trans Cybern 49(7):1\u201313","journal-title":"IEEE Trans Cybern"},{"issue":"4","key":"979_CR51","doi-asserted-by":"crossref","first-page":"1366","DOI":"10.1016\/j.chaos.2006.04.057","volume":"34","author":"D Yang","year":"2007","unstructured":"Yang D, Li G, Cheng G (2007) On the efficiency of chaos optimization algorithms for global optimization. Chaos, Solitons Fractals 34(4):1366\u20131375","journal-title":"Chaos, Solitons Fractals"},{"issue":"2","key":"979_CR52","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/j.jocs.2013.10.002","volume":"5","author":"AH Gandomi","year":"2014","unstructured":"Gandomi AH, Yang X-S (2014) Chaotic bat algorithm. J Comput Sci 5(2):224\u2013232","journal-title":"J Comput Sci"},{"issue":"4","key":"979_CR53","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1080\/019697298125678","volume":"29","author":"B Li","year":"1998","unstructured":"Li B, Jiang W (1998) Optimizing complex functions by chaos search. Cybern Syst 29(4):409\u2013419","journal-title":"Cybern Syst"},{"issue":"3","key":"979_CR54","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1109\/TEVC.2003.810069","volume":"7","author":"R Caponetto","year":"2003","unstructured":"Caponetto R, Fortuna L, Fazzino S, Xibilia MG (2003) Chaotic sequences to improve the performance of evolutionary algorithms. IEEE Trans Evol Comput 7(3):289\u2013304","journal-title":"IEEE Trans Evol Comput"},{"issue":"1","key":"979_CR55","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1016\/j.procs.2015.05.248","volume":"51","author":"P Snaselova","year":"2015","unstructured":"Snaselova P, Zboril F (2015) Genetic algorithm using theory of chaos. Proc Comput Sci 51(1):316\u2013325","journal-title":"Proc Comput Sci"},{"key":"979_CR56","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.asoc.2016.08.010","volume":"49","author":"Y Sun","year":"2016","unstructured":"Sun Y, Liu X, Zhang Z, Wang Z, Yu Y, Zhang T, Zhu Y, Song Z (2016) A sparse probabilistic approach with chaotic artificial bee colony optimization for sea clutter soft computing. Appl Soft Comput 49:108\u2013119","journal-title":"Appl Soft Comput"},{"issue":"5\u20136","key":"979_CR57","doi-asserted-by":"crossref","first-page":"3860","DOI":"10.1016\/j.apm.2015.10.052","volume":"40","author":"L Huang","year":"2016","unstructured":"Huang L, Ding S, Yu S, Wang J, Lu K (2016) Chaos-enhanced Cuckoo search optimization algorithms for global optimization. Appl Math Model 40(5\u20136):3860\u20133875","journal-title":"Appl Math Model"},{"key":"979_CR58","first-page":"1","volume":"2016","author":"N Dong","year":"2016","unstructured":"Dong N, Fang X, Wu A (2016) A novel chaotic particle swarm optimization algorithm for parking space guidance. Math Prob Eng 2016:1\u201314","journal-title":"Math Prob Eng"},{"key":"979_CR59","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1016\/j.asoc.2017.01.008","volume":"53","author":"S Mirjalili","year":"2017","unstructured":"Mirjalili S, Gandomi AH (2017) Chaotic gravitational constants for the gravitational search algorithm. Appl Soft Comput 53:407\u2013419","journal-title":"Appl Soft Comput"},{"issue":"October","key":"979_CR60","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1007\/s00357-014-9161-z","volume":"31","author":"F Murtagh","year":"2014","unstructured":"Murtagh F, Legendre P (2014) Ward\u2019s hierarchical agglomerative clustering method: Which algorithms implement Ward\u2019s criterion? J Classif 31(October):274\u2013295","journal-title":"J Classif"},{"issue":"301","key":"979_CR61","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1080\/01621459.1963.10500845","volume":"58","author":"JH Ward","year":"1963","unstructured":"Ward JH (1963) Hierarchical grouping to optimize an objective function. J Am Stat Assoc 58(301):236\u2013244","journal-title":"J Am Stat Assoc"},{"key":"979_CR62","doi-asserted-by":"crossref","unstructured":"Chen L (2010) Web-age information management: 11th international conference, WAIM 2010, Jiuzhaigou, China, July 15\u201317, 2010: proceedings. Springer, Berlin","DOI":"10.1007\/978-3-642-14246-8"},{"key":"979_CR63","first-page":"904","volume":"269","author":"K Tatsumi","year":"2015","unstructured":"Tatsumi K, Ibuki T, Tanino T (2015) Particle swarm optimization with stochastic selection of perturbation-based chaotic updating system. Appl Math Comput 269:904\u2013929","journal-title":"Appl Math Comput"},{"key":"979_CR64","unstructured":"Lu X, Lei J, Li W, Pan Z (2019) A delayed feedback chaotic encryption algorithm based on polar codes. In: 2018 IEEE international conference on electronics and communication engineering, ICECE 2018, pp 27\u201331"},{"key":"979_CR65","doi-asserted-by":"crossref","first-page":"4587","DOI":"10.1109\/ACCESS.2017.2780323","volume":"6","author":"H Gan","year":"2018","unstructured":"Gan H, Xiao S, Zhao Y (2018) A novel secure data transmission scheme using chaotic compressed sensing. IEEE Access 6:4587\u20134598","journal-title":"IEEE Access"},{"issue":"12","key":"979_CR66","doi-asserted-by":"crossref","first-page":"728","DOI":"10.1038\/nphoton.2008.227","volume":"2","author":"A Uchida","year":"2008","unstructured":"Uchida A, Amano K, Inoue M, Hirano K, Naito S, Someya H, Oowada I, Kurashige T, Shiki M, Yoshimori S, Yoshimura K, Davis P (2008) Fast physical random bit generation with chaotic semiconductor lasers. Nat Photonics 2(12):728\u2013732","journal-title":"Nat Photonics"},{"key":"979_CR67","doi-asserted-by":"crossref","unstructured":"Singh S, Siddiqui TJ, Singh R, Singh HV (2011) DCT-domain robust data hiding using chaotic sequence. In: 2011 International conference on multimedia, signal processing and communication technologies, pp 300\u2013303","DOI":"10.1109\/MSPCT.2011.6150499"},{"issue":"3","key":"979_CR68","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1063\/1.165880","volume":"2","author":"H Nozawa","year":"1992","unstructured":"Nozawa H (1992) A neural network model as a globally coupled map and applications based on chaos. Chaos Interdiscip J Nonlinear Sci 2(3):377\u2013386","journal-title":"Chaos Interdiscip J Nonlinear Sci"},{"issue":"4","key":"979_CR69","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1109\/72.701185","volume":"9","author":"L Wang","year":"1998","unstructured":"Wang L, Smith K (1998) On chaotic simulated annealing. IEEE Trans Neural Netw 9(4):716\u2013718","journal-title":"IEEE Trans Neural Netw"},{"issue":"2","key":"979_CR70","first-page":"163","volume":"12","author":"M Javidi","year":"2015","unstructured":"Javidi M, Hosseinpourfard R (2015) Chaos Genetic Algorithm instead Genetic Algorithm. Int Arab J Inf Technol 12(2):163\u2013168","journal-title":"Int Arab J Inf Technol"},{"issue":"4","key":"979_CR71","doi-asserted-by":"crossref","first-page":"1229","DOI":"10.1016\/j.cnsns.2013.08.017","volume":"19","author":"D Yang","year":"2014","unstructured":"Yang D, Liu Z, Zhou J (2014) Chaos optimization algorithms based on chaotic maps with different probability distribution and search speed for global optimization. Commun Nonlinear Sci Numer Simul 19(4):1229\u20131246","journal-title":"Commun Nonlinear Sci Numer Simul"},{"key":"979_CR72","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.asoc.2013.12.016","volume":"17","author":"X Yuan","year":"2014","unstructured":"Yuan X, Zhao J, Yang Y, Wang Y (2014) Hybrid parallel chaos optimization algorithm with harmony search algorithm. Appl Soft Comput J 17:12\u201322","journal-title":"Appl Soft Comput J"},{"key":"979_CR73","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.neucom.2011.12.009","volume":"83","author":"C Li","year":"2012","unstructured":"Li C, Zhou J, Kou P, Xiao J (2012) A novel chaotic particle swarm optimization based fuzzy clustering algorithm. Neurocomputing 83:98\u2013109","journal-title":"Neurocomputing"},{"key":"979_CR74","unstructured":"He D, He C, Jiang L-G, Zhu H-W, Hu G-R (2000) A chaotic map with infinite collapses. In: 2000 TENCON proceedings. Intelligent systems and technologies for the new millennium (Cat. No.00CH37119), vol 2, pp 95\u201399"},{"issue":"5","key":"979_CR75","doi-asserted-by":"crossref","first-page":"3169","DOI":"10.1016\/j.chaos.2009.04.019","volume":"42","author":"Y-Y He","year":"2009","unstructured":"He Y-Y, Zhou J-Z, Xiang X-Q, Chen H, Qin H (2009) Comparison of different chaotic maps in particle swarm optimization algorithm for long-term cascaded hydroelectric system scheduling. Chaos, Solitons Fractals 42(5):3169\u20133176","journal-title":"Chaos, Solitons Fractals"},{"issue":"1\u20132","key":"979_CR76","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/S0167-2789(02)00787-X","volume":"178","author":"JA Gonz\u00e1lez","year":"2003","unstructured":"Gonz\u00e1lez JA, Reyes LI, Su\u00e1rez JJ, Guerrero LE, Guti\u00e9rrez G (2003) From exactly solvable chaotic maps to stochastic dynamics. Phys D Nonlinear Phenom 178(1\u20132):26\u201350","journal-title":"Phys D Nonlinear Phenom"},{"issue":"7","key":"979_CR77","doi-asserted-by":"crossref","first-page":"900","DOI":"10.1109\/81.933333","volume":"48","author":"D He","year":"2001","unstructured":"He D, He C, Jiang LG, Zhu HW, Hu GR (2001) \u201cChaotic characteristics of a one-dimensional iterative map with infinite collapses. IEEE Trans Circuits Syst I Fundam Theory Appl 48(7):900\u2013906","journal-title":"IEEE Trans Circuits Syst I Fundam Theory Appl"},{"key":"979_CR78","unstructured":"Fogarty TC (1989) Varying the probability of mutation in the genetic algorithm. In: Proc. 3rd int\u2019l conf. genet. algorithms, pp 104\u2013109"},{"key":"979_CR79","doi-asserted-by":"crossref","unstructured":"Lawnik M (2014) Generation of numbers with the distribution close to uniform with the use of chaotic maps","DOI":"10.5220\/0005090304510455"},{"key":"979_CR80","first-page":"27","volume":"79","author":"G Anescu","year":"2017","unstructured":"Anescu G (2017) Scalable test functions for multidimensional continuous optimization. U P B Sci Bull Ser C 79:27\u201342","journal-title":"U P B Sci Bull Ser C"},{"key":"979_CR81","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.advengsoft.2015.11.004","volume":"92","author":"MD Li","year":"2016","unstructured":"Li MD, Zhao H, Weng XW, Han T (2016) A novel nature-inspired algorithm for optimization: virus colony search. Adv Eng Softw 92:65\u201388","journal-title":"Adv Eng Softw"},{"key":"979_CR82","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compstruc.2016.03.001","volume":"169","author":"A Askarzadeh","year":"2016","unstructured":"Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169:1\u201312","journal-title":"Comput Struct"},{"key":"979_CR83","doi-asserted-by":"crossref","first-page":"614","DOI":"10.1016\/j.asoc.2015.02.014","volume":"30","author":"JJQ Yu","year":"2015","unstructured":"Yu JJQ, Li VOK (2015) A social spider algorithm for global optimization. Appl Soft Comput 30:614\u2013627","journal-title":"Appl Soft Comput"},{"key":"979_CR84","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.ins.2014.02.057","volume":"276","author":"M Han","year":"2014","unstructured":"Han M, Liu C, Xing J (2014) An evolutionary membrane algorithm for global numerical optimization problems. Inf Sci (NY) 276:219\u2013241","journal-title":"Inf Sci (NY)"},{"key":"979_CR85","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.knosys.2016.01.009","volume":"97","author":"Z Meng","year":"2016","unstructured":"Meng Z, Pan J-S (2016) Monkey king evolution: a new memetic evolutionary algorithm and its application in vehicle fuel consumption optimization. Knowl Based Syst 97:144\u2013157","journal-title":"Knowl Based Syst"},{"key":"979_CR86","doi-asserted-by":"crossref","first-page":"80","DOI":"10.2307\/3001968","volume":"1","author":"F Wilcoxon","year":"1945","unstructured":"Wilcoxon F (1945) Individual comparisons by ranking methods. Biometrics 1:80\u201383","journal-title":"Biometrics"},{"key":"979_CR87","doi-asserted-by":"crossref","first-page":"1942","DOI":"10.1109\/ICNN.1995.488968","volume":"4","author":"J Kennedy","year":"1995","unstructured":"Kennedy J, Eberhart RC (1995) Particle swarm optimization. Proc IEEE Int Conf. Neural Netw 4:1942\u20131948","journal-title":"Proc IEEE Int Conf. Neural Netw"},{"key":"979_CR88","volume-title":"Nature inspired cooperative strategies for optimization (NISCO 2010)","author":"XS Yang","year":"2010","unstructured":"Yang XS (2010) Nature inspired cooperative strategies for optimization (NISCO 2010). Springer, Berlin"},{"issue":"2","key":"979_CR89","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1177\/003754970107600201","volume":"76","author":"ZW Geem","year":"2001","unstructured":"Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60\u201368","journal-title":"Simulation"},{"key":"979_CR90","doi-asserted-by":"crossref","unstructured":"Olorunda O, Engelbrecht AP (2008) Measuring exploration\/exploitation in particle swarms using swarm diversity. In: 2008 IEEE congress on evolutionary computation (IEEE world congress on computational intelligence), pp 1128\u20131134","DOI":"10.1109\/CEC.2008.4630938"},{"key":"979_CR91","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.engappai.2018.03.003","volume":"71","author":"A Mortazavi","year":"2018","unstructured":"Mortazavi A, To\u011fan V, Nuho\u011flu A (2018) Interactive search algorithm: a new hybrid metaheuristic optimization algorithm. Eng Appl Artif Intell 71:275\u2013292","journal-title":"Eng Appl Artif Intell"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-019-00979-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s13042-019-00979-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-019-00979-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,7,10]],"date-time":"2020-07-10T23:22:35Z","timestamp":1594423355000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s13042-019-00979-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,12]]},"references-count":91,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2020,2]]}},"alternative-id":["979"],"URL":"https:\/\/doi.org\/10.1007\/s13042-019-00979-6","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,7,12]]},"assertion":[{"value":"8 January 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 July 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 July 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}