一种深度扩展记忆的仿人粒子群算法仿真分析

从仿生学和心理学角度出发,提出一种深度扩展记忆的仿人粒子群算法,以解决标准粒子群及其主流改进算法易陷入局部最优等问题.算法对粒子认知进行群体共享,并采用深度扩展记忆积累粒子认知,通过仿人遗忘函数配置不同时期认知对当前决策的影响权重.仿真分析表明,所提出算法对遗忘函数和遗忘因子高度敏感,算法寻优多维多极值函数时,在收敛精度、成功率和优化成本等方面较标准粒子群及其改进算法有显著提升.

Simulation analysis of human simulated PSO based on deep extended memory

To solve the problem that particle swarm optimization and most of its improved algorithms are easy to fall into local convergence, standing on bionics and psychological point of view, a human simulated particle swarm optimization(HSPSO) algorithm based on deep extended memory is presented. The knowledge of each particle is shared by introducing the knowledge sharing part, which is accumulated by the deep extended memory, and the human simulated forgetting function is used to set up the weight of knowledge of different period. The simulation analysis shows that HSPSO algorithm is highly sensitive to the forgetting function and the forgetting factor, and has a better performance in convergence precision, success ratio and reducing the cost of algorithm compared to the standard PSO and its improved algorithm when applied to the optimization of multi-dimensional and multi-extreme value functions.