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Published in:
2006 | OriginalPaper | Chapter
Evolving the Structure of the Particle Swarm Optimization Algorithms
Authors : Laura Dioşan, Mihai Oltean
Published in: Evolutionary Computation in Combinatorial Optimization
Publisher: Springer Berlin Heidelberg
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A new model for evolving the structure of a Particle Swarm Optimization (PSO) algorithm is proposed in this paper. The model is a hybrid technique that combines a Genetic Algorithm (GA) and a PSO algorithm. Each GA chromosome is an array encoding a meaning for updating the particles of the PSO algorithm. The evolved PSO algorithm is compared to a human-designed PSO algorithm by using ten artificially constructed functions and one real-world problem. Numerical experiments show that the evolved PSO algorithm performs similarly and sometimes even better than standard approaches for the considered problems.