An improved particle swarm optimization algorithm

Particles can remember some information in an optimization process. They learn by themselves and from other particles, so the next generation can inherit much information from their parents and finally find optimal solutions. But particles are also faced with two problems of stagnating in a local but not global optimum. Genetic algorithms have strong global search ability. Genetic algorithms are combined with particles swarm optimization and an improved particles swarm optimization algorithm is proposed in this paper. The better individuals obtained by improved genetic algorithms can be improved further by particles swarm optimization. The experiments show that the proposed algorithm is better than traditional genetic algorithm and particles swarm.