Multi-objective optimization of engineering systems using game theory and particle swarm optimization

Multi-objective optimization of engineering systems using game theory and particle swarm optimization

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Article ID: iaor20105494
Volume: 41
Issue: 8
Start Page Number: 737
End Page Number: 752
Publication Date: Aug 2009
Journal: Engineering Optimization
Authors: ,
Keywords: game theory, programming: multiple criteria
Abstract:

This study proposes particle swarm optimization (PSO) based algorithms to solve multi-objective engineering optimization problems involving continuous, discrete and/or mixed design variables. The original PSO algorithm is modified to include dynamic maximum velocity function and bounce method to enhance the computational efficiency and solution accuracy. The algorithm uses a closest discrete approach (CDA) to solve optimization problems with discrete design variables. A modified game theory (MGT) approach, coupled with the modified PSO, is used to solve multi-objective optimization problems. A dynamic penalty function is used to handle constraints in the optimization problem. The methodologies proposed are illustrated by several engineering applications and the results obtained are compared with those reported in the literature.

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