摘要： 结合社会行为学、心理学相关理论改进粒子群算法，以提高个体粒子的智能特性。构造基于个体行为激励理论的粒子群算法，用标准测试函数进行测试，分析改进方案对算法寻优和收敛能力的影响。用社会学的理论及其客观现象，解释算法性能出现这些变化的理论基础和现实意义。实验表明，该改进算法具有较好的寻优性能。

关键词: 粒子群算法, 社会行为, 个体激励

Abstract: Some relevant theories of social behavioral and psychology are integrated in particle swarm optimization’s improvement, in order to make each particles have more intelligence characteristics. An individual inspiration Particle Swarm Optimization(PSO) is proposed and applied to benchmark functions for testing, and modified approach’s impact on the algorithm’s searching and convergent ability is analyzed. Those changes are explained by sociological theory and phenomenon. Experimental results show that the modified algorithm has better searching and convergence performances.

Key words: Particle Swarm Optimization(PSO), social behavior, individual inspiration

中图分类号: 

• TP181