Full-informed differential evolution particle swarm optimization algorithm
全信息差异进化粒子群优化算法

As the performance of the particle swarm optimization was affected by the population diversity, this paper developed the particle swarm optimization based on the differential evolution. The new algorithm used the multi-ecological subgroups structure and presented a new full-informed particle to link the subgroups, the population decline monitor guided the ecological subgroups to differential fusion dynamically. The new algorithm was beneficial to obtain better particle, it also could increase the otherness between the particles, the whole quality and the convergence performance. Finally, it applied the new algorithm to eight test functions and compared with six extended particle swarm optimization. The result shows that the new algorithm has a potent affect on population diversity, improves the convergence accuracy with fast speed, the theory and experiment both support that the new algorithm has strong global searching ability.