改进型耗散粒子群优化算法

摘要

通过对标准粒子群优化算法中惯性权重的分析和对耗散理论的研究,提出了一种惯性权重正弦调整的耗散粒子群优化算法(S-DPSO),并对该算法进行了深入的分析和研究.通过对4个典型函数的仿真测试,试验结果表明S-DPSO在收敛速度和全局收敛性方面都比标准粒子群优化算法、随机惯性权重粒子群优化算法、惯性权重正弦调整粒子群优化算法、耗散粒子群优化算法和随机惯性权重耗散粒子群优化算法有明显改进,理论分析和仿真试验验证了S-DPSO的正确性和有效性.%Based on the analyzing inertia weight of the standard particle swarm optimization (PSO) algorithm and studying the dissipative structure theory, a new dissipative PSO(DPSO) algorithm with sinusoidal changing inertia weight (S-DPSO) is presented. S-DPSO algorithm is conducted in-depth analysis and research. By the experiments of four benchmark function, the results show the performance of S-DPSO improve more clearly than the standard PSO, random inertia weight PSO(R-PSO), S-PSO, DPSO and R-DPSO. Theoretical analysis and simulation experiments show that the S-DPSO is efficient and feasible.