论文部分内容阅读
提出一种新的双子群粒子群优化(PSO)算法。充分利用搜索域内的有效信息,通过2组搜索方向相反的主、辅子群之间的相互协同,扩大搜索范围。在不增加粒子群规模的前提下,提高解高维最优化问题的精度,降低粒子群优化算法陷入局部最优点的风险。3种典型函数的仿真结果及与2种经典PSO算法的比较结果验证了该算法的有效性。
A new dual subgroup swarm optimization (PSO) algorithm is proposed. Make full use of the effective information in the search domain to expand the search scope through the mutual synergy between the two groups of main and auxiliary sub-groups whose search directions are opposite. Without increasing the size of the particle swarm optimization, the accuracy of solving the high-dimensional optimization problem is improved, and the risk of particle swarm optimization algorithm falling into the local optimum is reduced. The simulation results of three typical functions and comparison with two classical PSO algorithms verify the effectiveness of the proposed algorithm.