Abstract: Quantum-Behaved Particle Swarm Optimization is a new stochastic global optimization method.In this paper,Particle Swarm Optimization, Quantum-Behaved Particle Swarm Optimization， repulsion technique is introduced and presented based on the analysis of Quantum-Behaved Particle Swarm Optimization .New algorithm apply to nash equilibrium in game theory and in order to solves the solution of nash equilibrium. Two benchmark are tested and showed that the new algorithm is better than the PSO Algorithm Based on Replusion Technique with both a better value found and a steady convergence.

Key words: quantum-behaved particle swarm optimization, nash equilibrium, repulsion technique, game

摘要： 基于量子行为的粒子群优化算法（QPSO）是一种随机的全局优化搜索新方法.文章系统的介绍了PSO算法、QPSO算法和” replusion”技术.在对QPSO算法和基于“replusion”技术的PSO算法分析的基础上,提出了基于“replusion”技术的QPSO算法.将该算法用于求解混合纳什均衡.通过实验表明,新算法在解的收敛性和稳定性等方面优于QPSO算法

关键词: 具有量子行为的粒子群算法, 纳什均衡, 排斥技术, 博弈