Abstract
This paper develops an improved hybrid optimization algorithm based on particle swarm optimization (PSO) and a genetic algorithm (GA). First, the population is evolved over a certain number of generations by PSO and the best M particles are retained, with the remaining particles excluded. Second, new individuals are generated by implementing selection, crossover and mutation GA operators for the best M particles. Finally, the new individuals are combined with the best M particles to form new a population for the next generation. The algorithm can exchange information several times during evolution so that the complement of two algorithms can be more fully exploited. The proposed method is applied to fifteen benchmark optimization problems and the results obtained show an improvement over published methods. The impact of M on algorithm performance is also discussed.
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