计算机工程 ›› 2011, Vol. 37 ›› Issue (18): 1-3.doi: 10.3969/j.issn.1000-3428.2011.18.001

• 博士论文 •    下一篇

一种改进的小生境多目标粒子群优化算法

黄 平,于金杨,元泳泉   

  1. (华南理工大学理学院,广州 510640)
  • 收稿日期:2011-03-11 出版日期:2011-09-20 发布日期:2011-09-20
  • 作者简介:黄 平(1963-),男,高级工程师、博士研究生,主研方向:进化计算;于金杨、元泳泉,学士
  • 基金项目:
    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)

Improved Niching Multi-objective Particle Swarm Optimization Algorithm

HUANG Ping, YU Jin-yang, YUAN Yong-quan   

  1. (School of Science, South China University of Technology, Guangzhou 510640, China)
  • Received:2011-03-11 Online:2011-09-20 Published:2011-09-20

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。

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

Abstract: 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.

Key words: multi-objective optimization, Particle Swarm Optimization(PSO) algorithm, niching technique, non-dominated sorting, crowding degree, dynamic weight method

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