Abstract
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.
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Dioşan, L., Oltean, M. (2006). Evolving the Structure of the Particle Swarm Optimization Algorithms. In: Gottlieb, J., Raidl, G.R. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2006. Lecture Notes in Computer Science, vol 3906. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11730095_3
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DOI: https://doi.org/10.1007/11730095_3
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