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Experimental Study on Boundary Constraints Handling in Particle Swarm Optimization

Experimental Study on Boundary Constraints Handling in Particle Swarm Optimization

Shi Cheng, Yuhui Shi
Copyright: © 2020 |Pages: 30
ISBN13: 9781799832225|ISBN10: 1799832228|ISBN13 Softcover: 9781799832232|EISBN13: 9781799832249
DOI: 10.4018/978-1-7998-3222-5.ch011
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MLA

Cheng, Shi, and Yuhui Shi. "Experimental Study on Boundary Constraints Handling in Particle Swarm Optimization." Handbook of Research on Advancements of Swarm Intelligence Algorithms for Solving Real-World Problems, edited by Shi Cheng and Yuhui Shi, IGI Global, 2020, pp. 217-246. https://doi.org/10.4018/978-1-7998-3222-5.ch011

APA

Cheng, S. & Shi, Y. (2020). Experimental Study on Boundary Constraints Handling in Particle Swarm Optimization. In S. Cheng & Y. Shi (Eds.), Handbook of Research on Advancements of Swarm Intelligence Algorithms for Solving Real-World Problems (pp. 217-246). IGI Global. https://doi.org/10.4018/978-1-7998-3222-5.ch011

Chicago

Cheng, Shi, and Yuhui Shi. "Experimental Study on Boundary Constraints Handling in Particle Swarm Optimization." In Handbook of Research on Advancements of Swarm Intelligence Algorithms for Solving Real-World Problems, edited by Shi Cheng and Yuhui Shi, 217-246. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-3222-5.ch011

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Abstract

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 (PD) is an effective way to monitor an algorithm's ability for exploration and exploitation. Through the PD measurement, useful search information can be obtained. PSO with a different topology structure and different boundary constraints handling strategy will have a different impact on particles' exploration and exploitation ability. In this chapter, the phenomenon of particles gets “stuck in” the boundary in PSO and 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 strategies on the algorithm's ability of exploration and exploitation.

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