基于博弈模型的合作式粒子群优化算法

粒子群算法作为一种新兴的进化优化方法，能够大大减轻复杂的大规模优化问题的计算负担. 根据博弈论的思想，在传统粒子群基础上提出了一种基于博弈模型的合作式粒子群优化算法，算法基于重复博弈模型，在重复博弈中利用一个博弈序列，使得每次博弈都能够产生最大效益，并得到了相应博弈过程的纳什均衡. 通过典型基准测试函数对算法的性能进行对比实验，实验结果表明算法是可行的、有效的，对拓展粒子群算法研究具有重要的理论意义与实际意义.

Improved Particle Swarm Optimization Algorithm Based on Game Model

Particle Swarm algorithm as a new evolutionary optimization method can greatly reduce the computational burden of complex, large-scale optimization problems. This article is based on game theory. On the basis of the particle swarm it proposeda non-cooperative game model based on particle swarm optimization algorithm, it wses a game sequence repeated game model. And in repeated games, each game all hope to produce maximum benefits. Nash equilibrium of the corresponding game process. Function through multiple benchmarks, comparing with the performance of the algorithm experimental results show that the algorithm is feasible and effective. The study has important theoretical significance and practical significance onexpand swarm intelligence algorithm.