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Experimental Study on Boundary Constraints Handling in Particle Swarm Optimization: From Population Diversity Perspective

Shi Cheng, Yuhui Shi, Quande Qin

Source Title: International Journal of Swarm Intelligence Research (IJSIR)2(3)

ISSN: 1947-9263|EISSN: 1947-9271|EISBN13: 9781613509333|DOI: 10.4018/jsir.2011070104

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MLA

Cheng, Shi, et al. "Experimental Study on Boundary Constraints Handling in Particle Swarm Optimization: From Population Diversity Perspective." IJSIR vol.2, no.3 2011: pp.43-69. http://doi.org/10.4018/jsir.2011070104

APA

Cheng, S., Shi, Y., & Qin, Q. (2011). Experimental Study on Boundary Constraints Handling in Particle Swarm Optimization: From Population Diversity Perspective. International Journal of Swarm Intelligence Research (IJSIR), 2(3), 43-69. http://doi.org/10.4018/jsir.2011070104

Chicago

Cheng, Shi, Yuhui Shi, and Quande Qin. "Experimental Study on Boundary Constraints Handling in Particle Swarm Optimization: From Population Diversity Perspective," International Journal of Swarm Intelligence Research (IJSIR) 2, no.3: 43-69. http://doi.org/10.4018/jsir.2011070104

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Abstract

Premature convergence happens in Particle Swarm Optimization (PSO) for solving both multimodal problems and unimodal problems. With an improper boundary constraints handling method, particles may get “stuck in” the boundary. Premature convergence means that an algorithm has lost its ability of exploration. Population diversity is an effective way to monitor an algorithm’s ability of exploration and exploitation. Through the population diversity measurement, useful search information can be obtained. PSO with a different topology structure and a different boundary constraints handling strategy will have a different impact on particles’ exploration and exploitation ability. In this paper, the phenomenon of particles gets “stuck in” the boundary in PSO is experimentally studied and reported. The authors observe the position diversity time-changing curves of PSOs with different topologies and different boundary constraints handling techniques, and analyze the impact of these setting on the algorithm’s ability of exploration and exploitation. From these experimental studies, an algorithm’s ability of exploration and exploitation can be observed and the search information obtained; therefore, more effective algorithms can be designed to solve problems.

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Experimental Study on Boundary Constraints Handling in Particle Swarm Optimization: From Population Diversity Perspective

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