带扩展记忆的粒子群优化算法仿真分析

Simulation analysis of particle swarm optimization algorithm with extended memory

从心理学的角度提出带扩展记忆的粒子群优化算法(PSOEM),以克服标准粒子群优化算法(PSO)在优化多维函数过程中粒子搜索方向性差、目的性弱的缺陷.采用扩展记忆存储粒子的历史信息,并引入参数表征扩展记忆的重要性.利用经典离散控制理论分析其定值算法的稳定范围.此算法与标准算法是同源异构的,可以与已改进的PSO算法结合使用.基准测试函数的仿真结果验证了所提出算法的有效性.

Standing on a psychological point of view,a particle swarm optimization algorithm with extended memory （PSOEM）is presented for the problem that particles often lost their way when applying the standard particle swarm optimization（PSO）algorithm to optimization multidimensional functions.The extended memory is introduced to store each particle＇s historical information and a parameter is employed to describe the importance of extended memory as well. Stability region of its deterministic version in a dynamic environment is analyzed by means of the classic discrete control theory.Because PSO with extended memory（PSOEM）and PSO are homologous but heterogeneous in structure,the specialty of PSOEM is that it can integrate with numerous existing improved PSO algorithms and combine respective advantages. Results of simulation on benchmark functions show the effectiveness of the proposed algorithm.