{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:31:44Z","timestamp":1773801104338,"version":"3.50.1"},"reference-count":89,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2023,3,23]],"date-time":"2023-03-23T00:00:00Z","timestamp":1679529600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,3,23]],"date-time":"2023-03-23T00:00:00Z","timestamp":1679529600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62006124"],"award-info":[{"award-number":["62006124"]}],"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":["U20B2061"],"award-info":[{"award-number":["U20B2061"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","award":["BK20200811"],"award-info":[{"award-number":["BK20200811"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of the Jiangsu Higher Education Institutions of China","award":["20KJB520006"],"award-info":[{"award-number":["20KJB520006"]}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"crossref","award":["NRF-2021H1D3A2A01082705"],"award-info":[{"award-number":["NRF-2021H1D3A2A01082705"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2023,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>High-dimensional optimization problems are increasingly pervasive in real-world applications nowadays and become harder and harder to optimize due to increasingly interacting variables. To tackle such problems effectively, this paper designs a random elite ensemble learning swarm optimizer (REELSO) by taking inspiration from human observational learning theory. First, this optimizer partitions particles in the current swarm into two exclusive groups: the elite group consisting of the top best particles and the non-elite group containing the rest based on their fitness values. Next, it employs particles in the elite group to build random elite neighbors for each particle in the non-elite group to form a positive learning environment for the non-elite particle to observe. Subsequently, the non-elite particle is updated by cognitively learning from the best elite among the neighbors and collectively learning from all elites in the environment. For one thing, each non-elite particle is directed by superior ones, and thus the convergence of the swarm could be guaranteed. For another, the elite learning environment is randomly formed for each non-elite particle, and hence high swarm diversity could be maintained. Finally, this paper further devises a dynamic partition strategy to divide the swarm into the two groups dynamically during the evolution, so that the swarm gradually changes from exploring the immense solution space to exploiting the found optimal areas without serious diversity loss. With the above mechanisms, the devised REELSO is expected to explore the search space and exploit the found optimal areas properly. Abundant experiments on two popularly used high-dimensional benchmark sets prove that the devised optimizer performs competitively with or even significantly outperforms several state-of-the-art approaches designed for high-dimensional optimization.<\/jats:p>","DOI":"10.1007\/s40747-023-00993-w","type":"journal-article","created":{"date-parts":[[2023,3,23]],"date-time":"2023-03-23T04:27:23Z","timestamp":1679545643000},"page":"5467-5500","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["A random elite ensemble learning swarm optimizer for high-dimensional optimization"],"prefix":"10.1007","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0277-3077","authenticated-orcid":false,"given":"Qiang","family":"Yang","sequence":"first","affiliation":[]},{"given":"Gong-Wei","family":"Song","sequence":"additional","affiliation":[]},{"given":"Xu-Dong","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Zhen-Yu","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Sang-Woon","family":"Jeon","sequence":"additional","affiliation":[]},{"given":"Jun","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,3,23]]},"reference":[{"issue":"4","key":"993_CR1","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1109\/TEVC.2019.2950935","volume":"24","author":"S Wang","year":"2020","unstructured":"Wang S, Liu J, Jin Y (2020) Surrogate-assisted robust optimization of large-scale networks based on graph embedding. IEEE Trans Evol Comput 24(4):735\u2013749","journal-title":"IEEE Trans Evol Comput"},{"issue":"9","key":"993_CR2","doi-asserted-by":"crossref","first-page":"4053","DOI":"10.1109\/TCYB.2019.2922266","volume":"50","author":"WN Chen","year":"2020","unstructured":"Chen WN, Tan DZ, Yang Q, Gu T, Zhang J (2020) Ant colony optimization for the control of pollutant spreading on social networks. IEEE Trans Cybern 50(9):4053\u20134065","journal-title":"IEEE Trans Cybern"},{"issue":"1","key":"993_CR3","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1109\/TII.2018.2836189","volume":"15","author":"W Du","year":"2019","unstructured":"Du W, Zhong W, Tang Y, Du WL, Jin Y (2019) High-dimensional robust multi-objective optimization for order scheduling: a decision variable classification approach. IEEE Trans Ind Inform 15(1):293\u2013304","journal-title":"IEEE Trans Ind Inform"},{"issue":"12","key":"993_CR4","doi-asserted-by":"crossref","first-page":"12698","DOI":"10.1109\/TCYB.2021.3086501","volume":"52","author":"L Ma","year":"2022","unstructured":"Ma L, Li N, Guo Y, Wang X, Yang S, Huang M, Zhang H (2022) Learning to optimize: reference vector reinforcement learning adaption to constrained many-objective optimization of industrial copper burdening system. IEEE Trans Cybern 52(12):12698\u201312711","journal-title":"IEEE Trans Cybern"},{"issue":"11","key":"993_CR5","doi-asserted-by":"crossref","first-page":"7339","DOI":"10.1109\/TWC.2020.3010701","volume":"19","author":"Z Zhou","year":"2020","unstructured":"Zhou Z, Yu H, Mumtaz S, Al-Rubaye S, Tsourdos A, Hu RQ (2020) Power control optimization for large-scale multi-antenna systems. IEEE Trans Wirel Commun 19(11):7339\u20137352","journal-title":"IEEE Trans Wirel Commun"},{"issue":"4","key":"993_CR6","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1049\/cit2.12121","volume":"7","author":"XD Gao","year":"2022","unstructured":"Gao XD, Cao WJ, Yang Q, Wang HL, Wang XL, Jin G, Zhang J (2022) Parameter optimization of control system design for uncertain wireless power transfer systems using modified genetic algorithm. CAAI Trans Intell Technol 7(4):582\u2013593","journal-title":"CAAI Trans Intell Technol"},{"issue":"9","key":"993_CR7","doi-asserted-by":"crossref","first-page":"3984","DOI":"10.1109\/TCYB.2019.2935466","volume":"50","author":"Y Wang","year":"2020","unstructured":"Wang Y, Ru ZY, Wang K, Huang PQ (2020) Joint deployment and task scheduling optimization for large-scale mobile users in multi-UAV-enabled mobile edge computing. IEEE Trans Cybern 50(9):3984\u20133997","journal-title":"IEEE Trans Cybern"},{"key":"993_CR8","first-page":"1","volume":"60","author":"Z Lu","year":"2022","unstructured":"Lu Z, Liang S, Yang Q, Du B (2022) Evolving block-based convolutional neural network for hyperspectral image classification. IEEE Trans Geosci Remote Sens 60:1\u201321","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"2","key":"993_CR9","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1109\/TCYB.2018.2871673","volume":"50","author":"X Zhang","year":"2020","unstructured":"Zhang X, Zhou K, Pan H, Zhang L, Zeng X, Jin Y (2020) A network reduction-based multiobjective evolutionary algorithm for community detection in large-scale complex networks. IEEE Trans Cybern 50(2):703\u2013716","journal-title":"IEEE Trans Cybern"},{"key":"993_CR10","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1016\/j.ins.2014.10.042","volume":"295","author":"S Mahdavi","year":"2015","unstructured":"Mahdavi S, Shiri ME, Rahnamayan S (2015) Metaheuristics in large-scale global continues optimization: a survey. Inf Sci 295:407\u2013428","journal-title":"Inf Sci"},{"issue":"3","key":"993_CR11","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1007\/s13042-019-01030-4","volume":"11","author":"JR Jian","year":"2020","unstructured":"Jian JR, Zhan ZH, Zhang J (2020) Large-scale evolutionary optimization: a survey and experimental comparative study. Int J Mach Learn Cybern 11(3):729\u2013745","journal-title":"Int J Mach Learn Cybern"},{"key":"993_CR12","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1016\/j.ins.2014.09.031","volume":"316","author":"A LaTorre","year":"2015","unstructured":"LaTorre A, Muelas S, Pe\u00f1a JM (2015) A comprehensive comparison of large scale global optimizers. Inf Sci 316:517\u2013549","journal-title":"Inf Sci"},{"issue":"3","key":"993_CR13","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1109\/TEVC.2013.2262111","volume":"18","author":"RG Regis","year":"2014","unstructured":"Regis RG (2014) Evolutionary programming for high-dimensional constrained expensive black-box optimization using radial basis functions. IEEE Trans Evol Comput 18(3):326\u2013347","journal-title":"IEEE Trans Evol Comput"},{"key":"993_CR14","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1016\/j.ins.2014.12.062","volume":"316","author":"MN Omidvar","year":"2015","unstructured":"Omidvar MN, Li XD, Tang K (2015) Designing benchmark problems for large-scale continuous optimization. Inf Sci 316:419\u2013436","journal-title":"Inf Sci"},{"issue":"2","key":"993_CR15","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1109\/TEVC.2016.2591064","volume":"21","author":"Q Yang","year":"2017","unstructured":"Yang Q, Chen WN, Yu Z, Gu T, Li Y, Zhang H, Zhang J (2017) Adaptive multimodal continuous ant colony optimization. IEEE Trans Evol Comput 21(2):191\u2013205","journal-title":"IEEE Trans Evol Comput"},{"issue":"3","key":"993_CR16","doi-asserted-by":"crossref","first-page":"636","DOI":"10.1109\/TCYB.2016.2523000","volume":"47","author":"Q Yang","year":"2017","unstructured":"Yang Q, Chen WN, Li Y, Chen CLP, Xu XM, Zhang J (2017) Multimodal estimation of distribution algorithms. IEEE Trans Cybern 47(3):636\u2013650","journal-title":"IEEE Trans Cybern"},{"issue":"4","key":"993_CR17","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1109\/TSC.2014.2312946","volume":"8","author":"J Yao","year":"2015","unstructured":"Yao J, Liu X, Zhu X, Guan H (2015) Control of large-scale systems through dimension reduction. IEEE Trans Serv Comput 8(4):563\u2013575","journal-title":"IEEE Trans Serv Comput"},{"issue":"5","key":"993_CR18","doi-asserted-by":"crossref","first-page":"761","DOI":"10.3390\/math10050761","volume":"10","author":"Q Yang","year":"2022","unstructured":"Yang Q, Hua LT, Gao XD, Xu DD, Lu ZY, Jeon SW, Zhang J (2022) Stochastic cognitive dominance leading particle swarm optimization for multimodal problems. Mathematics 10(5):761","journal-title":"Mathematics"},{"issue":"11","key":"993_CR19","doi-asserted-by":"crossref","first-page":"6723","DOI":"10.1109\/TSMC.2020.2963943","volume":"51","author":"L Ma","year":"2021","unstructured":"Ma L, Cheng S, Shi Y (2021) Enhancing learning efficiency of brain storm optimization via orthogonal learning design. IEEE Trans Syst Man Cybern Syst 51(11):6723\u20136742","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"993_CR20","doi-asserted-by":"crossref","unstructured":"Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the international symposium on micro machine and human science, pp 39\u201343","DOI":"10.1109\/MHS.1995.494215"},{"key":"993_CR21","volume-title":"Swarm intelligence","author":"R Eberhart","year":"2001","unstructured":"Eberhart R, Shi YH, Kennedy JL (2001) Swarm intelligence. Morgan Kaufmann"},{"issue":"4","key":"993_CR22","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1109\/TEVC.2018.2885075","volume":"23","author":"Y Cao","year":"2019","unstructured":"Cao Y, Zhang H, Li W, Zhou M, Zhang Y, Chaovalitwongse WA (2019) Comprehensive learning particle swarm optimization algorithm with local search for multimodal functions. IEEE Trans Evol Comput 23(4):718\u2013731","journal-title":"IEEE Trans Evol Comput"},{"issue":"5","key":"993_CR23","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1109\/TEVC.2017.2754271","volume":"22","author":"C Yue","year":"2018","unstructured":"Yue C, Qu B, Liang J (2018) A multiobjective particle swarm optimizer using ring topology for solving multimodal multiobjective problems. IEEE Trans Evol Comput 22(5):805\u2013817","journal-title":"IEEE Trans Evol Comput"},{"issue":"2","key":"993_CR24","doi-asserted-by":"crossref","first-page":"1085","DOI":"10.1109\/TCYB.2019.2925015","volume":"51","author":"W Liu","year":"2021","unstructured":"Liu W, Wang Z, Yuan Y, Zeng N, Hone K, Liu X (2021) A novel sigmoid-function-based adaptive weighted particle swarm optimizer. IEEE Trans Cybern 51(2):1085\u20131093","journal-title":"IEEE Trans Cybern"},{"key":"993_CR25","unstructured":"Aguirre AH, Zavala AM, Diharce EV, Rionda SB (2007) COPSO: constrained optimization via PSO algorithm. Center for Research in Mathematics Technical Report No. I-07-04\/22-02-2007:77"},{"key":"993_CR26","doi-asserted-by":"crossref","first-page":"6617750","DOI":"10.1155\/2021\/6617750","volume":"2021","author":"MM Rosso","year":"2021","unstructured":"Rosso MM, Cucuzza R, Di Trapani F, Marano GC (2021) Nonpenalty machine learning constraint handling using PSO-SVM for structural optimization. Adv Civ Eng Mater 2021:6617750","journal-title":"Adv Civ Eng Mater"},{"key":"993_CR27","first-page":"214","volume":"76","author":"KE Parsopoulos","year":"2002","unstructured":"Parsopoulos KE, Vrahatis MN (2002) Particle swarm optimization method for constrained optimization problems. Intell Technol Theory Appl New Trends Intell Technol 76:214\u2013220","journal-title":"Intell Technol Theory Appl New Trends Intell Technol"},{"key":"993_CR28","unstructured":"Liang JJ, Suganthan PN (2006) Dynamic multi-swarm particle swarm optimizer with a novel constraint-handling mechanism. In: IEEE international conference on evolutionary computation, pp 9\u201316"},{"issue":"5","key":"993_CR29","doi-asserted-by":"crossref","first-page":"2285","DOI":"10.3390\/app12052285","volume":"12","author":"MM Rosso","year":"2022","unstructured":"Rosso MM, Cucuzza R, Aloisio A, Marano GC (2022) Enhanced multi-strategy particle swarm optimization for constrained problems with an evolutionary-strategies-based unfeasible local search operator. Appl Sci 12(5):2285","journal-title":"Appl Sci"},{"key":"993_CR30","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.jpdc.2018.11.008","volume":"126","author":"JA De Campos","year":"2019","unstructured":"De Campos JA, Pozo ATR, Duarte EP (2019) Parallel multi-swarm PSO strategies for solving many objective optimization problems. J Parallel Distrib Comput 126:13\u201333","journal-title":"J Parallel Distrib Comput"},{"issue":"2","key":"993_CR31","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1109\/TEVC.2020.3017865","volume":"25","author":"FF Wei","year":"2021","unstructured":"Wei FF, Chen WN, Yang Q, Deng J, Luo XN, Jin H, Zhang J (2021) A classifier-assisted level-based learning swarm optimizer for expensive optimization. IEEE Trans Evol Comput 25(2):219\u2013233","journal-title":"IEEE Trans Evol Comput"},{"issue":"6","key":"993_CR32","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1109\/TCYB.2018.2817020","volume":"49","author":"J Zhang","year":"2019","unstructured":"Zhang J, Zhu X, Wang Y, Zhou M (2019) Dual-environmental particle swarm optimizer in noisy and noise-free environments. IEEE Trans Cybern 49(6):2011\u20132021","journal-title":"IEEE Trans Cybern"},{"issue":"4","key":"993_CR33","doi-asserted-by":"crossref","first-page":"4268","DOI":"10.1109\/TIA.2019.2908609","volume":"55","author":"W Cao","year":"2019","unstructured":"Cao W, Liu K, Wu M, Xu S, Zhao J (2019) An improved current control strategy based on particle swarm optimization and steady-state error correction for SAPF. IEEE Trans Ind Appl 55(4):4268\u20134274","journal-title":"IEEE Trans Ind Appl"},{"issue":"3","key":"993_CR34","doi-asserted-by":"crossref","first-page":"1004","DOI":"10.1109\/TII.2017.2786782","volume":"14","author":"A Slowik","year":"2018","unstructured":"Slowik A, Kwasnicka H (2018) Nature inspired methods and their industry applications\u2014swarm intelligence algorithms. IEEE Trans Ind Inform 14(3):1004\u20131015","journal-title":"IEEE Trans Ind Inform"},{"key":"993_CR35","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1016\/j.asoc.2014.04.039","volume":"22","author":"G Quaranta","year":"2014","unstructured":"Quaranta G, Marano GC, Greco R, Monti G (2014) Parametric identification of seismic isolators using differential evolution and particle swarm optimization. Appl Soft Comput 22:458\u2013464","journal-title":"Appl Soft Comput"},{"issue":"7","key":"993_CR36","doi-asserted-by":"crossref","first-page":"2076","DOI":"10.1016\/j.ymssp.2010.04.006","volume":"24","author":"G Quaranta","year":"2010","unstructured":"Quaranta G, Monti G, Marano GC (2010) Parameters identification of Van der Pol-Duffing oscillators via particle swarm optimization and differential evolution. Mech Syst Signal Process 24(7):2076\u20132095","journal-title":"Mech Syst Signal Process"},{"key":"993_CR37","doi-asserted-by":"crossref","unstructured":"Zhan ZH, Zhang J (2010) Self-Adaptive differential evolution based on PSO learning strategy. In: Proceedings of conference genetics evolutionary computation, pp 39\u201346","DOI":"10.1145\/1830483.1830490"},{"key":"993_CR38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s40747-020-00148-1","volume":"7","author":"H Wang","year":"2021","unstructured":"Wang H, Liang MN, Sun CL, Zhang GC, Xie LP (2021) Multiple-strategy learning particle swarm optimization for large-scale optimization problems. Complex Intell Syst 7:1\u201316","journal-title":"Complex Intell Syst"},{"issue":"5","key":"993_CR39","doi-asserted-by":"crossref","first-page":"882","DOI":"10.1109\/TEVC.2020.2968743","volume":"24","author":"XF Song","year":"2020","unstructured":"Song XF, Zhang Y, Guo YN, Sun XY, Wang YL (2020) Variable-size cooperative coevolutionary particle swarm optimization for feature selection on high-dimensional data. IEEE Trans Evol Comput 24(5):882\u2013895","journal-title":"IEEE Trans Evol Comput"},{"key":"993_CR40","unstructured":"Tang K, Li XD, Suganthan PN, Yang Z, Weise T (2010) Benchmark functions for the CEC 2010 special session and competition on large-scale global optimization. Nat Inspired ComputAppl Lab, Univ Sci Technol China Anhui China Tech Rep"},{"issue":"3","key":"993_CR41","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1109\/TEVC.2018.2868770","volume":"23","author":"X Ma","year":"2019","unstructured":"Ma X, Li X, Zhang Q, Tang K, Liang Z, Xie W, Zhu Z (2019) A survey on cooperative co-evolutionary algorithms. IEEE Trans Evol Comput 23(3):421\u2013441","journal-title":"IEEE Trans Evol Comput"},{"issue":"7","key":"993_CR42","doi-asserted-by":"crossref","first-page":"1072","DOI":"10.3390\/math10071072","volume":"10","author":"Q Yang","year":"2022","unstructured":"Yang Q, Zhang KX, Gao XD, Xu DD, Lu ZY, Jeon SW, Zhang J (2022) A dimension group-based comprehensive elite learning swarm optimizer for large-scale optimization. Mathematics 10(7):1072","journal-title":"Mathematics"},{"issue":"2","key":"993_CR43","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1109\/TEVC.2011.2173577","volume":"17","author":"WN Chen","year":"2013","unstructured":"Chen WN, Zhang J, Lin Y, Chen E (2013) Particle swarm optimization with an aging leader and challengers. IEEE Trans Evol Comput 17(2):241\u2013258","journal-title":"IEEE Trans Evol Comput"},{"key":"993_CR44","unstructured":"Potter MA (1997) The design and analysis of a computational model of cooperative coevolution. Dissertation, George Mason University"},{"issue":"3","key":"993_CR45","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1109\/TEVC.2004.826069","volume":"8","author":"FVD Bergh","year":"2004","unstructured":"Bergh FVD, Engelbrecht AP (2004) A cooperative approach to particle swarm optimization. IEEE Trans Evol Comput 8(3):225\u2013239","journal-title":"IEEE Trans Evol Comput"},{"issue":"2","key":"993_CR46","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1109\/TEVC.2011.2112662","volume":"16","author":"LI Xd","year":"2012","unstructured":"Xd LI, Yao X (2012) Cooperatively coevolving particle swarms for large scale optimization. IEEE Trans Evol Comput 16(2):210\u2013224","journal-title":"IEEE Trans Evol Comput"},{"issue":"2","key":"993_CR47","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1109\/TCYB.2014.2322602","volume":"45","author":"R Cheng","year":"2015","unstructured":"Cheng R, Jin YC (2015) A competitive swarm optimizer for large scale optimization. IEEE Trans Cybern 45(2):191\u2013204","journal-title":"IEEE Trans Cybern"},{"key":"993_CR48","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.ins.2014.08.039","volume":"291","author":"R Cheng","year":"2015","unstructured":"Cheng R, Jin YC (2015) A social learning particle swarm optimization algorithm for scalable optimization. Inf Sci 291:43\u201360","journal-title":"Inf Sci"},{"issue":"4","key":"993_CR49","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1109\/TEVC.2017.2743016","volume":"22","author":"Q Yang","year":"2018","unstructured":"Yang Q, Chen WN, Deng JD, Li Y, Gu T, Zhang J (2018) A level-based learning swarm optimizer for large-scale optimization. IEEE Trans Evol Comput 22(4):578\u2013594","journal-title":"IEEE Trans Evol Comput"},{"issue":"3","key":"993_CR50","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1109\/TEVC.2013.2281543","volume":"18","author":"MN Omidvar","year":"2014","unstructured":"Omidvar MN, Li XD, Mei Y, Yao X (2014) Cooperative co-evolution with differential grouping for large scale optimization. IEEE Trans Evol Comput 18(3):378\u2013393","journal-title":"IEEE Trans Evol Comput"},{"issue":"5","key":"993_CR51","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1109\/TEVC.2017.2778089","volume":"22","author":"Y Sun","year":"2018","unstructured":"Sun Y, Kirley M, Halgamuge SK (2018) A recursive decomposition method for large scale continuous optimization. IEEE Trans Evol Comput 22(5):647\u2013661","journal-title":"IEEE Trans Evol Comput"},{"key":"993_CR52","doi-asserted-by":"crossref","first-page":"51084","DOI":"10.1109\/ACCESS.2018.2869334","volume":"6","author":"Q Yang","year":"2018","unstructured":"Yang Q, Chen WN, Zhang J (2018) Evolution consistency based decomposition for cooperative coevolution. IEEE Access 6:51084\u201351097","journal-title":"IEEE Access"},{"key":"993_CR53","unstructured":"Xie HY, Yang Q, XM Hu, WN Chen (2016) Cross-generation elites guided particle swarm optimization for large scale optimization. In: IEEE symposium series on computational intelligence, pp 1\u20138"},{"issue":"7","key":"993_CR54","doi-asserted-by":"crossref","first-page":"3393","DOI":"10.1109\/TCYB.2019.2904543","volume":"50","author":"Q Yang","year":"2020","unstructured":"Yang Q, Chen WN, Gu T, Zhang H, Yuan H, Kwong S, Zhang J (2020) A distributed swarm optimizer with adaptive communication for large-scale optimization. IEEE Trans Cybern 50(7):3393\u20133408","journal-title":"IEEE Trans Cybern"},{"issue":"12","key":"993_CR55","doi-asserted-by":"crossref","first-page":"6284","DOI":"10.1109\/TCYB.2020.2968400","volume":"51","author":"R Lan","year":"2020","unstructured":"Lan R, Zhu X, Lu H, Liu Z, Luo X (2020) A two-phase learning-based swarm optimizer for large-scale optimization. IEEE Trans Cybern 51(12):6284\u20136293","journal-title":"IEEE Trans Cybern"},{"issue":"3","key":"993_CR56","doi-asserted-by":"crossref","first-page":"1960","DOI":"10.1109\/TCYB.2020.3034427","volume":"52","author":"Q Yang","year":"2022","unstructured":"Yang Q, Chen WN, Gu T, Jin Y, Mao W, Zhang J (2022) An adaptive stochastic dominant learning swarm optimizer for high-dimensional optimization. IEEE Trans Cybern 52(3):1960\u20131976","journal-title":"IEEE Trans Cybern"},{"key":"993_CR57","volume":"60","author":"DY Li","year":"2021","unstructured":"Li DY, Guo WA, Lerch A, Li YM, Wang L, Wu QD (2021) An adaptive particle swarm optimizer with decoupled exploration and exploitation for large scale optimization. Swarm Evol Comput 60:100789","journal-title":"Swarm Evol Comput"},{"key":"993_CR58","volume-title":"Social learning theory","author":"A Bandura","year":"1977","unstructured":"Bandura A, McClelland DC (1977) Social learning theory. Prentice Hall, Englewood Cliffs"},{"key":"993_CR59","unstructured":"Bandura A, Walters RH (1963) Social learning and personality development. Holt, Rinehart, & Winston"},{"key":"993_CR60","unstructured":"Bandura A (1986) Social foundations of thought and action. Englewood Cliffs"},{"key":"993_CR61","unstructured":"Li XD, Tang K, Omidvar MN, Yang ZY, Qin K (2013) Benchmark functions for the CEC 2013 special session and competition on large-scale global optimization. EvolComput Mach Learn Group, RMIT Univ, Melbourne, VIC, Australia, tech rep"},{"issue":"3","key":"993_CR62","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1109\/TEVC.2004.826071","volume":"8","author":"A Ratnaweera","year":"2004","unstructured":"Ratnaweera A, Halgamuge SK, Watson HC (2004) Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans Evol Comput 8(3):240\u2013255","journal-title":"IEEE Trans Evol Comput"},{"issue":"4","key":"993_CR63","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1109\/TMAG.2006.871568","volume":"42","author":"JH Seo","year":"2006","unstructured":"Seo JH, Im CH, Heo CG, Kim JK, Jung HK, Lee CG (2006) Multimodal function optimization based on particle swarm optimization. IEEE Trans Magn 42(4):1095\u20131098","journal-title":"IEEE Trans Magn"},{"issue":"7","key":"993_CR64","doi-asserted-by":"crossref","first-page":"1127","DOI":"10.1109\/TCYB.2013.2279802","volume":"44","author":"Z Ren","year":"2014","unstructured":"Ren Z, Zhang A, Wen C, Feng Z (2014) A scatter learning particle swarm optimization algorithm for multimodal problems. IEEE Trans Cybern 44(7):1127\u20131140","journal-title":"IEEE Trans Cybern"},{"key":"993_CR65","first-page":"3659","volume":"4","author":"JJ Liang","year":"2004","unstructured":"Liang JJ, Qin AK, Suganthan PM, Baskar S (2004) Particle swarm optimization algorithms with novel learning strategies. IEEE Int Conf Syst Man Cybern 4:3659\u20133664","journal-title":"IEEE Int Conf Syst Man Cybern"},{"issue":"10","key":"993_CR66","doi-asserted-by":"crossref","first-page":"1620","DOI":"10.3390\/math10101620","volume":"10","author":"Q Yang","year":"2022","unstructured":"Yang Q, Jing YF, Gao XD, Xu DD, Lu ZY, Jeon SW, Zhang J (2022) Predominant cognitive learning particle swarm optimization for global numerical optimization. Mathematics 10(10):1620","journal-title":"Mathematics"},{"issue":"3","key":"993_CR67","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1109\/TEVC.2005.857610","volume":"10","author":"JJ Liang","year":"2006","unstructured":"Liang JJ, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10(3):281\u2013295","journal-title":"IEEE Trans Evol Comput"},{"issue":"6","key":"993_CR68","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1109\/TEVC.2010.2052054","volume":"15","author":"ZH Zhan","year":"2011","unstructured":"Zhan ZH, Zhang J, Li Y, Shi Y (2011) Orthogonal learning particle swarm optimization. IEEE Trans Evol Comput 15(6):832\u2013847","journal-title":"IEEE Trans Evol Comput"},{"issue":"8","key":"993_CR69","doi-asserted-by":"crossref","first-page":"1261","DOI":"10.3390\/math10081261","volume":"10","author":"Q Yang","year":"2022","unstructured":"Yang Q, Guo X, Gao XD, Xu DD, Lu ZY (2022) Differential elite learning particle swarm optimization for global numerical optimization. Mathematics 10(8):1261","journal-title":"Mathematics"},{"issue":"8","key":"993_CR70","doi-asserted-by":"crossref","first-page":"2694","DOI":"10.1016\/j.cam.2010.11.021","volume":"235","author":"R Akbari","year":"2011","unstructured":"Akbari R, Ziarati K (2011) A rank based particle swarm optimization algorithm with dynamic adaptation. J Comput App Math 235(8):2694\u20132714","journal-title":"J Comput App Math"},{"issue":"7","key":"993_CR71","doi-asserted-by":"crossref","first-page":"1032","DOI":"10.3390\/math10071032","volume":"10","author":"Q Yang","year":"2022","unstructured":"Yang Q, Bian YW, Gao XD, Xu DD, Lu ZY, Jeon SW, Zhang J (2022) Stochastic triad topology based particle swarm optimization for global numerical optimization. Mathematics 10(7):1032","journal-title":"Mathematics"},{"key":"993_CR72","doi-asserted-by":"crossref","unstructured":"Caraffini F, Neri F, Iacca G (2017) Large scale problems in practice: the effect of dimensionality on the interaction among variables. In: European conference on applied evolutionary computation, pp 636\u2013652","DOI":"10.1007\/978-3-319-55849-3_41"},{"issue":"9","key":"993_CR73","doi-asserted-by":"crossref","first-page":"2717","DOI":"10.1109\/TCYB.2016.2577587","volume":"47","author":"Y Zhang","year":"2017","unstructured":"Zhang Y, Chiang H (2017) A novel consensus-based particle swarm optimization-assisted trust-tech methodology for large-scale global optimization. IEEE Trans Cybern 47(9):2717\u20132729","journal-title":"IEEE Trans Cybern"},{"key":"993_CR74","doi-asserted-by":"crossref","unstructured":"Sun Y, Kirley M, Halgamuge SK (2015) Extended differential grouping for large scale global optimization with direct and indirect variable interactions. In: Proceedings of conference genetic and evolutionary computation, pp 313\u2013320","DOI":"10.1145\/2739480.2754666"},{"issue":"2","key":"993_CR75","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2791291","volume":"42","author":"Y Mei","year":"2016","unstructured":"Mei Y, Omidvar MN, Li XD, Yao X (2016) A competitive divide-and-conquer algorithm for unconstrained large-scale black-box optimization. ACM Trans Math Softw 42(2):1\u201324","journal-title":"ACM Trans Math Softw"},{"issue":"6","key":"993_CR76","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1109\/TEVC.2017.2694221","volume":"21","author":"MN Omidvar","year":"2017","unstructured":"Omidvar MN, Yang M, Mei Y, Li X, Yao X (2017) DG2: a faster and more accurate differential grouping for large-scale black-box optimization. IEEE Trans Evol Comput 21(6):929\u2013942","journal-title":"IEEE Trans Evol Comput"},{"key":"993_CR77","doi-asserted-by":"crossref","unstructured":"Sun Y, Omidvar MN, Kirley M, Li XD (2018) Adaptive threshold parameter estimation with recursive differential grouping for problem decomposition. In: Proceedings of genetic and evolutionary computation conference, pp 889\u2013896","DOI":"10.1145\/3205455.3205483"},{"issue":"1","key":"993_CR78","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1109\/TEVC.2020.3009390","volume":"25","author":"M Yang","year":"2021","unstructured":"Yang M, Zhou A, Li C, Yao X (2021) An efficient recursive differential grouping for large-scale continuous problems. IEEE Trans Evol Comput 25(1):159\u2013171","journal-title":"IEEE Trans Evol Comput"},{"key":"993_CR79","doi-asserted-by":"crossref","unstructured":"Molina D, Lozano M, Herrera F (2010) MA-SW-chains: memetic algorithm based on local search chains for large scale continuous global optimization. In: Proceedings of IEEE congress on evolutionary computation, pp 1\u20138","DOI":"10.1109\/CEC.2010.5586034"},{"key":"993_CR80","doi-asserted-by":"crossref","unstructured":"Zhao SZ, Liang JJ, Suganthan PN, Tasgetiren MF (2008) Dynamic multi-swarm particle swarm optimizer with local search for large scale global optimization. In: Proceedings of IEEE congress on evolutionary computation, pp 3845\u20133852","DOI":"10.1109\/CEC.2008.4631320"},{"key":"993_CR81","unstructured":"Cheng R, Sun CL, Jin YC (2013) A multi-swarm evolutionary framework based on a feedback mechanism. In: IEEE congress on evolutionary computation, pp 718\u2013724"},{"issue":"2","key":"993_CR82","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.asej.2016.07.008","volume":"8","author":"AF Ali","year":"2017","unstructured":"Ali AF, Tawhid MA (2017) A hybrid particle swarm optimization and genetic algorithm with population partitioning for large scale optimization problems. Ain Shams Eng J 8(2):191\u2013206","journal-title":"Ain Shams Eng J"},{"issue":"9","key":"993_CR83","doi-asserted-by":"crossref","first-page":"2896","DOI":"10.1109\/TCYB.2016.2616170","volume":"47","author":"Q Yang","year":"2017","unstructured":"Yang Q, Chen WN, Gu T, Zhang H, Deng JD, Li Y, Zhang J (2017) Segment-based predominant learning swarm optimizer for large-scale optimization. IEEE Trans Cybern 47(9):2896\u20132910","journal-title":"IEEE Trans Cybern"},{"key":"993_CR84","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.ins.2019.04.037","volume":"493","author":"HB Deng","year":"2019","unstructured":"Deng HB, Peng LZ, Zhang HB, Yang B, Chen ZX (2019) Ranking-based biased learning swarm optimizer for large-scale optimization. Inf Sci 493:120\u2013137","journal-title":"Inf Sci"},{"issue":"3","key":"993_CR85","doi-asserted-by":"crossref","first-page":"1175","DOI":"10.1109\/TCYB.2020.2977956","volume":"51","author":"ZJ Wang","year":"2021","unstructured":"Wang ZJ, Zhan ZH, Kwong S, Jin H, Zhang J (2021) Adaptive granularity learning distributed particle swarm optimization for large-scale optimization. IEEE Trans Cybern 51(3):1175\u20131188","journal-title":"IEEE Trans Cybern"},{"key":"993_CR86","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.swevo.2014.06.001","volume":"18","author":"MR Bonyadi","year":"2014","unstructured":"Bonyadi MR, Li X, Michalewicz Z (2014) A hybrid particle swarm with a time-adaptive topology for constrained optimization. Swarm Evol Comput 18:22\u201337","journal-title":"Swarm Evol Comput"},{"key":"993_CR87","doi-asserted-by":"crossref","unstructured":"Brest J, Boskovic B, Zamuda A, Fister I, Maucec MS (2012) Self-adaptive differential evolution algorithm with a small and varying population size. In: IEEE congress on evolutionary computation, pp 1\u20138","DOI":"10.1109\/CEC.2012.6252909"},{"issue":"1","key":"993_CR88","doi-asserted-by":"crossref","first-page":"10953","DOI":"10.1038\/s41598-022-14338-z","volume":"12","author":"MA Akbari","year":"2022","unstructured":"Akbari MA, Zare M, Azizipanah-abarghooee R, Mirjalili S, Deriche M (2022) The cheetah optimizer: a nature-inspired metaheuristic algorithm for large-scale optimization problems. Sci Rep 12(1):10953","journal-title":"Sci Rep"},{"key":"993_CR89","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2021.107543","volume":"233","author":"S Chakraborty","year":"2021","unstructured":"Chakraborty S, Saha AK, Chakraborty R, Saha M (2021) An enhanced whale optimization algorithm for large scale optimization problems. Knowl Based Syst 233:107543","journal-title":"Knowl Based Syst"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-023-00993-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-023-00993-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-023-00993-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T22:09:13Z","timestamp":1729116553000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-023-00993-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,23]]},"references-count":89,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["993"],"URL":"https:\/\/doi.org\/10.1007\/s40747-023-00993-w","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,23]]},"assertion":[{"value":"18 October 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 January 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 March 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"On behalf of all authors, the corresponding author states that there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"All authors have checked the manuscript and approved to submit to <i>Complex<\/i><i>and<\/i><i>Intelligent<\/i><i>Systems<\/i>. This paper has not been published elsewhere nor has it been submitted for publication elsewhere.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}