An improved evolutionary method with fuzzy logic for combining Particle Swarm Optimization and Genetic Algorithms

Authors:
Fevrier Valdez;Patricia Melin;Oscar Castillo
Affiliations:
Computer Science in the Graduate Division, Tijuana Institute of Technology, Tijuana 22500, BC, Mexico;Computer Science in the Graduate Division, Tijuana Institute of Technology, Tijuana 22500, BC, Mexico;Computer Science in the Graduate Division, Tijuana Institute of Technology, Tijuana 22500, BC, Mexico
Venue:
Applied Soft Computing
Year:
2011

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Abstract

We describe in this paper a new hybrid approach for optimization combining Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) using fuzzy logic to integrate the results of both methods and for parameters tuning. The new optimization method combines the advantages of PSO and GA to give us an improved FPSO+FGA hybrid approach. Fuzzy logic is used to combine the results of the PSO and GA in the best way possible. The new hybrid FPSO+FGA approach is compared with the PSO and GA methods with a set of benchmark mathematical functions. The improved hybrid FPSO+FGA method is shown to outperform both individual optimization methods.