Abstract
Over the last years, interest in hybrid metaheuristics has risen considerably in the field of optimization. Combinations of operators andmetaheuristics have provided very powerful search techniques. In this chapter we incorporate active components of Particle Swarm Optimization (PSO) into the Cellular Genetic Algorithm(cGA).We replace themutation operator by amutation based on concepts of PSO. We present two hybrid algorithms and analyze their performance using a set of different problems. The results obtained are quite satisfactory in efficacy and efficiency, outperforming in most cases existing algorithms for a set of problems.
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Alba, E., Villagra, A. (2013). Hybridizing cGAs with PSO-like Mutation. In: Tantar, E., et al. EVOLVE- A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation. Studies in Computational Intelligence, vol 447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32726-1_8
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DOI: https://doi.org/10.1007/978-3-642-32726-1_8
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