Solving constrained optimization problems with hybrid particle swarm optimization

Solving constrained optimization problems with hybrid particle swarm optimization

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Article ID: iaor200972066
Country: United Kingdom
Volume: 40
Issue: 11
Start Page Number: 1031
End Page Number: 1049
Publication Date: Nov 2008
Journal: Engineering Optimization
Authors: ,
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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