Solving constrained optimization problems with hybrid particle swarm optimization |
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Authors: | Erwie Zahara Chia-Hsin Hu |
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Affiliation: | 1. Department of Industrial Engineering and Management , St. John's University , Tamsui, Taiwan, 251, Republic of China erwi@mail.sju.edu.tw;3. Department of Industrial Engineering and Management , St. John's University , Tamsui, Taiwan, 251, Republic of China |
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Abstract: | Constrained optimization problems (COPs) are very important in that they frequently appear in the real world. A COP, in which both the function and constraints may be nonlinear, consists of the optimization of a function subject to constraints. Constraint handling is one of the major concerns when solving COPs with particle swarm optimization (PSO) combined with the Nelder–Mead simplex search method (NM-PSO). This article proposes embedded constraint handling methods, which include the gradient repair method and constraint fitness priority-based ranking method, as a special operator in NM-PSO for dealing with constraints. Experiments using 13 benchmark problems are explained and the NM-PSO results are compared with the best known solutions reported in the literature. Comparison with three different meta-heuristics demonstrates that NM-PSO with the embedded constraint operator is extremely effective and efficient at locating optimal solutions. |
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Keywords: | constrained optimization Nelder–Mead simplex search method particle swarm optimization constraint handling |
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