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Experimental Study on Boundary Constraints Handling in Particle Swarm Optimization from a Population Diversity Perspective
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Author(s): Shi Cheng (University of Nottingham Ningbo, China), Yuhui Shi (Xi'an Jiaotong-Liverpool University, China)and Quande Qin (Shenzhen University, China)
Copyright: 2015
Pages: 29
Source title:
Emerging Research on Swarm Intelligence and Algorithm Optimization
Source Author(s)/Editor(s): Yuhui Shi (Southern University of Science and Technology (SUSTech), China)
DOI: 10.4018/978-1-4666-6328-2.ch005
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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 chapter, the phenomenon of particles getting “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 settings on the algorithm's abilities of exploration and exploitation. From these experimental studies, an algorithm's abilities 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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