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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。
Aiming at the particle aggregation and slow convergence speed in particle swarm optimization algorithm, an improved particle swarm optimization algorithm is proposed. The algorithm considers both the success rate of particle evolution and the degree of diversity on the performance of the algorithm. When the degree of particle agglomeration is high, the inertia weight is increased and the global search ability of the algorithm is improved. In order to balance the global and local optimization ability of the algorithm, when the evolution speed is fast, the local search ability of the algorithm is improved, so as to avoid missing a better position. In the speed update, the introduction of poorer particles, to avoid the algorithm to search again for these poor location, reduce the search efficiency of the algorithm. The algorithm is used to optimize six classical test functions. Experiments show that this algorithm can not only balance the local and global search capabilities, but also improve the search efficiency and accuracy of the algorithm.