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A Particle Swarm Optimizer for Constrained Multiobjective Optimization

A Particle Swarm Optimizer for Constrained Multiobjective Optimization

Wen Fung Leong, Yali Wu, Gary G. Yen
Copyright: © 2015 |Pages: 31
ISBN13: 9781466674561|ISBN10: 1466674563|EISBN13: 9781466674578
DOI: 10.4018/978-1-4666-7456-1.ch054
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MLA

Leong, Wen Fung, et al. "A Particle Swarm Optimizer for Constrained Multiobjective Optimization." Research Methods: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2015, pp. 1246-1276. https://doi.org/10.4018/978-1-4666-7456-1.ch054

APA

Leong, W. F., Wu, Y., & Yen, G. G. (2015). A Particle Swarm Optimizer for Constrained Multiobjective Optimization. In I. Management Association (Ed.), Research Methods: Concepts, Methodologies, Tools, and Applications (pp. 1246-1276). IGI Global. https://doi.org/10.4018/978-1-4666-7456-1.ch054

Chicago

Leong, Wen Fung, Yali Wu, and Gary G. Yen. "A Particle Swarm Optimizer for Constrained Multiobjective Optimization." In Research Methods: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1246-1276. Hershey, PA: IGI Global, 2015. https://doi.org/10.4018/978-1-4666-7456-1.ch054

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Abstract

Generally, constraint-handling techniques are designed for evolutionary algorithms to solve Constrained Multiobjective Optimization Problems (CMOPs). Most Multiojective Particle Swarm Optimization (MOPSO) designs adopt these existing constraint-handling techniques to deal with CMOPs. In this chapter, the authors present a constrained MOPSO in which the information related to particles' infeasibility and feasibility status is utilized effectively to guide the particles to search for feasible solutions and to improve the quality of the optimal solution found. The updating of personal best archive is based on the particles' Pareto ranks and their constraint violations. The infeasible global best archive is adopted to store infeasible nondominated solutions. The acceleration constants are adjusted depending on the personal bests' and selected global bests' infeasibility and feasibility statuses. The personal bests' feasibility statuses are integrated to estimate the mutation rate in the mutation procedure. The simulation results indicate that the proposed constrained MOPSO is highly competitive in solving selected benchmark problems.

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