中文摘要：

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。

英文摘要：

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.