一种改进的小生境多目标粒子群优化算法  

Improved Niching Multi-objective Particle Swarm Optimization Algorithm

作　　者：黄平[1] 于金杨[1] 元泳泉[1] 

机构地区：[1]华南理工大学理学院,广州510640

出　　处：《计算机工程》2011年第18期1-3,共3页Computer Engineering

基　　金：国家"973"计划基金资助项目(20091072);国家自然科学基金资助项目(11071081)

摘　　要：提出一种小生境多目标粒子群优化算法。使用环邻域拓扑且无需任何小生境参数,克服常规小生境技术中需确定小生境参数的困难。采用NSGA-Ⅱ的非支配排序策略和动态加权方法选择最优粒子。基于拥挤度的变异操作引导粒子跳出局部最优,增强算法的全局搜索能力。通过对ZDT1~ZDT4和ZDT6的测试结果表明,与经典的多目标进化算法NSGA-Ⅱ、PESA-Ⅱ和MOPSO相比,该算法在最优解集的收敛度与多样性方面具有明显的优势。This paper describes a niching multi-objective Particle Swarm Optimization（PSO） algorithm.The algorithm applies ring neighborhood topology,which does not require any niching parameters.Hence,it can resolve the problem of traditional parameters setting.Non-dominated sorting and dynamic weight method are used to select the best particles.To enhance the global exploratory capability,a mutation operation is to operate when the crowding-distance decreases to the required precision.The proposed algorithm is tested by five well-known benchmark test functions ZDT1~ZDT4 and ZDT6.Simulation results prove that this algorithm performs better than those classical algorithms do in convergence and diversity.

关 键 词：多目标优化 粒子群优化算法 小生境技术 非支配排序 拥挤度 动态加权方法 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]