Dynamic Multi-swarm Particle Swarm Optimization with Center Learning Strategy
Authors: 
Zijian Zhu, Tian Zhong, Chenhan Wu, Bowen XueAuthors Info & Claims
Pages 141 - 147
Abstract
In this paper, we propose a novel variant of particle swarm optimization, called dynamic multi-swarm particle swarm optimization with center learning strategy (DMPSOC). In DMPSOC, all particles are divided into several sub-swarms. Then, a center-learning strategy is designed, in which each particle within the sub-swarms will learn from the historical optimal position of a particle or the center position in a sub-swarm. Also, an alternative learning factor is given to determine the particle learning strategy, which can be classified as center-learning or optimum-learning. Four benchmark functions are used in order to compare the performance of DMPSOC algorithm with the standard particle swarm optimization (SPSO). Experiments conducted illustrate that the proposed algorithm outperform SPSO in terms of convergence rate and solution accuracy.
References
[1]
Eberhart, R., Kennedy, J.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942--1948 (1995)
[2]
Lin A, Sun W, Yu H, Wu G, and Tang H Global genetic learning particle swarm optimization with diversity enhancement by ring topology Swarm Evol. Comput. 2019 44 571-583
[3]
Wang, Y.X., Xiang, Q.L.: Particle swarms with dynamic ring topology. In: 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp. 419--423. IEEE (2008)
[4]
Yue C, Qu B, and Liang J A multiobjective particle swarm optimizer using ring topology for solving multimodal multiobjective problems IEEE Trans. Evol. Comput. 2017 22 5 805-817
[5]
Yu Y, Xue B, Chen Z, and Qian Z Cluster tree topology construction method based on PSO algorithm to prolong the lifetime of zigbee wireless sensor networks EURASIP J. Wirel. Commun. Netw. 2019 2019 1 1-13
[6]
Niu B, Zhu Y, He X, and Wu H MCPSO: a multi-swarm cooperative particle swarm optimizer Appl. Math. Comput. 2007 185 2 1050-1062
[7]
Liang, J.J., Suganthan, P.N.: Dynamic multi-swarm particle swarm optimizer. In: Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005, pp. 124--129. IEEE (2005)
[8]
Ye W, Feng W, and Fan S A novel multi-swarm particle swarm optimization with dynamic learning strategy Appl. Soft Comput. 2017 61 832-843
[9]
Shi Y and Eberhart RC Porto VW, Saravanan N, Waagen D, and Eiben AE Parameter selection in particle swarm optimization Evolutionary Programming VII 1998 Heidelberg Springer 591-600
[10]
Nickabadi A, Ebadzadeh MM, and Safabakhsh R A novel particle swarm optimization algorithm with adaptive inertia weight Appl. Soft Comput. 2011 11 4 3658-3670
[11]
Li, L., Xue, B., Niu, B., Chai, Y., Wu, J.: The novel non-linear strategy of inertia weight in particle swarm optimization. In: 2009 Fourth International on Conference on Bio-Inspired Computing, pp. 1--5. IEEE (2009)
[12]
Cai X, Cui Y, and Tan Y Predicted modified PSO with time-varying accelerator coefficients Int. J. Bio-Inspired Comput. 2009 1 1–2 50-60
[13]
Clerc, M.: The swarm and the queen: towards a deterministic and adaptive particle swarm optimization. In: Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), vol. 3, pp. 1951--1957. IEEE (1999)
[14]
Yang, X., Jiao, Q., Liu, L.: Center particle swarm optimization algorithm. In: 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), pp. 2084--2087. IEEE (2019)
[15]
Sumpter D The principles of collective animal behaviour Philosophical Trans. Royal Soc. B: Biol. Sci. 2006 361 1465 5-22
[16]
Landeau L and Terborgh J Oddity and the ‘confusion effect’ in predation Anim. Behav. 1986 34 5 1372-1380
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Published: 15 July 2022
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