Particle swarm optimization with chaotic opposition-based population initialization and stochastic search technique. (English) Zbl 1254.90300

Summary: Particle swarm optimization (PSO) is a relatively new optimization algorithm that has been applied to a variety of problems. However, it may easily get trapped in a local optima when solving complex multimodal problems. To address this concerning issue, we propose a novel PSO called as CSPSO to improve the performance of PSO on complex multimodal problems in the paper. Specifically, a stochastic search technique is used to execute the exploration in PSO, so as to help the algorithm to jump out of the likely local optima. In addition, to enhance the global convergence, when producing the initial population, both opposition-based learning method and chaotic maps are employed. Moreover, numerical simulation and comparisons with some typical existing algorithms demonstrate the superiority of the proposed algorithm.


90C59 Approximation methods and heuristics in mathematical programming
Full Text: DOI


[2] Poli, R.; Kennedy, J.; Blackwell, T., Particle swarm optimization – an overview, Swarm Intell, 1, 1, 33-57 (2007)
[3] Mendes, R.; Kennedy, J.; Neves, J., The fully informed particle swarm: simpler maybe better, IEEE Trans Evolut Comput, 8, 3, 204-210 (2004)
[4] Liang, J. J.; Qin, A. K.; Suganthan, P. N.; Baskar, S., Comprehensive learning particle swarm optimizer for global optimization of multimodal functions, IEEE Trans Evolut Comput, 10, 3, 281-295 (2006)
[6] Kennedy, J.; Mendes, R., Neighborhood topologies in fully informed and best-of-neighborhood particle swarms, IEEE Trans Syst Man Cybernet Part C, 36, 4, 515-519 (2006)
[7] Ratnaweera, A.; Halgamuge, S. K.; Watson, H. C., Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients, IEEE Trans Evolut Comput, 8, 3, 240-255 (2004)
[9] Chatterjee, A.; Siarry, P., Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization, Comput Oper Res, 33, 3, 859-871 (2004) · Zbl 1114.90159
[10] Cai, X. J., Dispersed particle swarm optimization, Inform Process Lett, 105, 6, 231-235 (2008) · Zbl 1189.90205
[11] Jiao, B.; Lian, Z. G.; Gu, X. S., A dynamic inertia weight particle swarm optimization algorithm, Chaos Solitons Fract, 37, 3, 698-705 (2008) · Zbl 1146.90533
[12] Chen, Y. P.; Peng, W. C.; Jian, M. C., Particle swarm optimization with recombination and dynamic linkage discovery, IEEE Trans Syst Man Cybernet Part B, 37, 6, 1460-1470 (2007)
[13] Coelho, L. S., A quantum particle swarm optimizer with chaotic mutation operator, Chaos Solitons Fract, 37, 5, 1409-1418 (2008)
[14] Alatas, B.; Akin, E.; Bedri, O., Chaos embedded particle optimization algorithms, Chaos Solitons Fract, 40, 4, 1715-1734 (2008) · Zbl 1198.90400
[15] Shinn, Y. H.; Hung, S. L.; Weei, H. L.; Shinn, J. H., Orthogonal particle swarm optimization and its application to task assignment problems, IEEE Trans Syst Man Cybernet Part B, 38, 2, 288-298 (2008)
[16] Zhan, Z. H.; Zhang, J.; Li, Y.; Chung, H. H., Adaptive particle swarm optimization, IEEE Trans Syst Man Cybernet Part B, 39, 6, 1362-1381 (2009)
[17] Zhan, Z. H.; Zhang, J.; Li, Y.; Chung, H. H., Orthogonal learning particle swarm optimization, IEEE Trans Evolution Comput, 15, 6, 832-847 (2011)
[18] Li, S. T., A hybrid PSO-BFGS strategy for global optimization of multimodal functions, IEEE Trans Syst Man Cybernet Part B, 41, 4, 1003-1014 (2011)
[19] Rahnamayan, S., Opposition-based differential evolution, IEEE Trans Evolution Comput, 12, 1, 64-79 (2008)
[20] Liu, B.; Wang, L.; Jin, Y. H.; Tang, F.; Huang, D. X., Improved particle swarm optimization combined with chaos, Chaos Solitons Fract, 25, 2, 1261-1271 (2005) · Zbl 1074.90564
[21] Gao, W. F.; Liu, S. Y., A modified artificial bee colony algorithm, Comput Oper Res, 39, 3, 687-697 (2012) · Zbl 1251.90006
[23] Chen, M. R., A novel particle swarm optimizer hybridized with extremal optimization, Appl Soft Comput, 10, 2, 367-373 (2010)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.