中文摘要：

粒子群优化（Particle Swarm Optimization,PSO）算法参数较少、搜索机制简单,故一直是智能优化算法研究和应用的重点。然而PSO有易早熟、搜索精度不高及搜索性能对参数依赖性强的缺陷。针对此特点,在基于仿真的优化框架下,基于多Agent对融合传统全局最佳和局部最佳的PSO算法人工生命模型进行了仿真,以混合优化算法为计算引擎,对PSO的参数选取进行了重点讨论。利用一系列benchmark函数为例,进行了仿真优化实验和分析,取得了较为满意的结果,从而说明了本思想方法的可行性与可信性。

英文摘要：

Particle Swarm Optimization（PSO） has the characteristics that the parameter number is few and the basic operation is simple,so it has been the hotspot of the intelligent optimization.However,PSO possesses the defects that it can be premature easily,its searching precision is not high and the performance is affected by the parameters deeply.Aiming at the deficiencies,within the framework of simulation based optimization,this paper models and simulates the artificial life model using multiagent that integrates the traditional global PSO with local PSO,and the hybrid intelligent optimization algorithms function is used as the optimization engine.The parameter selection in PSO is discussed with emphasis.A series of benchmark functions are tested and analyzed,and high performance is obtained,which is supposed to demonstrate the feasibility and creditability of the above methodology.