Adjusting Parametersin Optimize Function PSO
Journal Title: International Journal of Social Science And Human Research - Year 2022, Vol 5, Issue 03
Abstract
Particel Swarm Optimization (PSO) is a form of population evolutionary algorithm introduced in the early 1995 by two American scientists, sociologist James Kennedy and electrical engineer. Russell. This thesis mainly deals with the PSO optimization algorithm and the methods of adaptive adjustment of the parameters of the PSO optimization. The thesis also presents some basic problems of PSO, from PSO history to two basic PSO algorithms and improved PSO algorithms. Some improved PSO algorithms will be presented in the thesis, including: airspeed limit, inertial weighting, and coefficient limit. These improvements are aimed at improving the quality of PSO, finding solutions to speed up the convergence of PSO. After presenting the basic problems of the PSO algorithm, the thesis focuses on studying the influence of adjusting parameters on the ability to converge in PSO algorithms. PSO algorithms with adaptively adjusted parameters are applied in solving real function optimization problems. The results are compared with the basic PSO algorithm, showing that the methods of adaptive adjustment of the parameters improve the efficiency of the PSO algorithm in finding the optimal solutions.
Authors and Affiliations
Le Thi Bao Tran, Nguyen Thu Nguyet Minh, Tra Van Dong
Keywords
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- EP ID EP703898
- DOI 10.47191/ijsshr/v5-i3-30
- Views 57
- Downloads 0
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