Hybrid optimization algorithms based on particle swarm optimization and genetic algorithm

This paper develops a hybrid optimization algorithms based on particle swarm optimization (PSO) and genetic algorithm (GA). Firstly, the population are evolved a certainty generations by PSO and the best M particles are retained while the other pop_size-M particles are removed. Secondly, generate pop_size-M new individuals by implementing selection, crossover and mutation operators of GA according to the remaining best M particles. Finally, put the pop_size-M new individuals into the remaining best M particles to form new population for next generation. The algorithm can exchange information several times during the evolvement process, so that the complement of two algorithms can be more fully exploited. The proposed method is used to deal with 5 functions optimization problems, and the results obtained are compared with existent bibliography, showing an improvement over the published methods. Furthermore, this paper studies the impact of M scale on the algorithm performance.