Abstract
When particle swarm optimization (PSO) solves multimodal problems, the loss of swarm diversity may bring about the premature convergence. This paper analyses the reasons leading to the loss of swarm diversity by computing and analyzing of the probabilistic characteristics of the learning factors in PSO. It also provides the relationship between the loss of swarm diversity and the probabilistic distribution and dependence of learning parameters. Experimental results show that the swarm diversity analysis is reasonable and the proposed strategies for maintaining swarm diversity are effective. The conclusions of the swarm diversity of PSO can be used to design PSO algorithm and improve its effectiveness. It is also helpful for understanding the working mechanism of PSO theoretically.
This work was supported by National Natural Science Foundation of China under Grant Nos.61300059. Provincial Project of Natural Science Research for Anhui Colleges of China (KJ2012Z031, KJ2012Z024).
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© 2015 Springer International Publishing Switzerland
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Shen, Y., Wei, L., Zeng, C. (2015). Swarm Diversity Analysis of Particle Swarm Optimization. In: Tan, Y., Shi, Y., Buarque, F., Gelbukh, A., Das, S., Engelbrecht, A. (eds) Advances in Swarm and Computational Intelligence. ICSI 2015. Lecture Notes in Computer Science(), vol 9140. Springer, Cham. https://doi.org/10.1007/978-3-319-20466-6_11
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DOI: https://doi.org/10.1007/978-3-319-20466-6_11
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