自适应信息选择PSO算法及其特性分析

Adaptive partly informed PSO algorithm and its characteristics analysis

针对粒子群优化过程中容易出现早熟收敛或停滞的问题,在全信息粒子群优化(FIPSO)算法的基础上结合社会心理学原理提出了一种新的粒子群优化算法——自适应信息选择粒子群优化算法(API-PSO)。在API-PSO算法中,粒子根据其邻域粒子不同表现,自适应地选择群体共享经验。实验表明,新的优化算法具有较好的收敛精度和收敛速度。分别对API-PSO算法的种群多样性和收敛性进行了数学分析,分析结果为合理选择算法参数,解决算法种群多样性匮乏,促进种群进化发展,改善算法性能提供了理论依据。

To avoid the premature convergence or stagnation during particle swarm optimization process, on the basis of Fully- Informed Particle Swarm Optimization （FIPSO） in conjunction with social psychological principles, the novel Adaptive Partly Informed Particle Swarm Optimization （API-PSO） algorithm is proposed. In API-PSO algorithm, a particle adaptively selects swarm-shared experience according to performances of its neighboring particles. Experiment results show that API-PSO offers satisfactory convergence accuracy and speed. Probabilistic analysis on conducted which serves as the theoretic basis to select the algorithm evolution development and improve algorithm performance. swarm diversity and convergence analysis of API-PSO is parameters, solve swarm diversity lack, facilitate swarm