一种改进的粒子群优化算法

An Improved Particle Swarm Optimization Algorithm

针对粒子群优化算法中粒子容易聚集和收敛速度慢,提出一种改进的粒子群优化算法。该算法同时考虑到粒子进化的成功率和多样性程度对算法寻优性能的影响,当粒子集聚程度较高时,增大惯性权值,提高算法的全局搜索能力。为平衡算法全局和局部寻优能力,当进化速度较快时,提高算法局部搜索能力,以免错过较好的位置。在速度更新中,引入较差粒子,避免算法再次去搜索这些较差的位置,降低算法的搜索效率。将该算法用于优化6个经典测试函数,实验表明:该算法不仅可以平衡局部和全局的搜索能力,而且可以提高算法的搜索效率和精度。

To overcome the problem of loss of diversity and poor convergence, an improved particle swarm optimization algorithm isproposed in this paper. The effect of agglomeration degree and evolution velocity on the optimization ability of algorithm is consid-ered in the improved algorithm. To improve the global searching capacity of the presented algorithm, the inertia weight increaseswhen agglomeration of particles is high. In order to balance global and local optimization ability of algorithm, local optimization abil-ity should be increased when algorithm has higher evolution velocity, so as not to miss a good location. To avoid algorithm to searchthese poor location repeatedly and increase the search efficiency of algorithm, worst particle is introduced in velocity updating formu-la. To verify the validity of algorithm, six classical test functions are optimized by the improved algorithm proposed in this paper.The results show that the proposed algorithm can not only balance the global and local search ability, but also improve the search ef-ficiency and accuracy of the algorithm.