计算机应用 ›› 2012, Vol. 32 ›› Issue (05): 1320-1324.

• 人工智能 • 上一篇    下一篇

改进的约束多目标粒子群算法

凌海风1,2,周献中2,江勋林1   

  1. 1. 解放军理工大学 工程兵工程学院,南京 210007
    2. 南京大学 工程管理学院,南京 210093
  • 收稿日期:2011-10-08 修回日期:2011-12-02 发布日期:2012-05-01 出版日期:2012-05-01
  • 通讯作者: 凌海风
  • 作者简介:凌海风(1972-),女,浙江长兴人,副教授,博士,主要研究方向:智能信息处理与智能系统;周献中(1962-),男,江苏泰兴人,教授,博士生导师,主要研究方向:复杂系统建模与仿真、智能信息处理;江勋林(1984-),男,江西九江人,硕士研究生,主要研究方向:智能信息处理与智能决策;萧毅鸿(1983-),男,安徽黄山人,博士研究生,讲师,主要研究方向:管理信息系统、决策支持系统、知识管理、项目管理。
  • 基金资助:

    国家自然科学基金资助项目(90718036)

Improved constrained multi-objective particle swarm optimization algorithm

LING Hai-feng1,2, 3,JIANG Xun-lin1   

  1. 1. Engineering Institute Corps of Engineers, PLA University of Science and Technology,Nanjing Jiangsu 210007, China
    2. School of Management and Engineering, Nanjing University, Nanjing Jiangsu 210093, China
    3. School of Management and Engineering, Nanjing
  • Received:2011-10-08 Revised:2011-12-02 Online:2012-05-01 Published:2012-05-01
  • Contact: LING Hai-feng

摘要: 在约束优化问题搜索空间分析的基础上提出了一种改进的约束多目标粒子群算法(CMOPSO)。提出一种动态ε不可行度许可约束支配关系作为主要约束的处理方法,提高了算法的边缘搜索能力和跨越非联通可行区域的能力。设计了一种新的密集距离度量方法用于外部档案维护,提高了算法的效率;提出了新的全局向导选取策略,使算法获得了更好的收敛性和多样性。数值仿真实验结果表明约束多目标粒子群算法算法可得到分布性、均匀性及逼近性都较好的Pareto最优解。

关键词: 多目标优化, 多目标粒子群, 距离量度, 档案维护, 全局向导选取

Abstract: An improved Multiple Objective Particle Swarm Optimization(MOPSO) algorithm for solving constrained multi-objective optimization problems (CMOPSO) was proposed based on the analysis of the characteristics of the multi-objective search space. A processing method taking dynamic ε unfeasible degree allowable constraint dominance relation as the main constraint was brought forward in this paper, which aimed to improve the algorithm's ability of edge searching and crossing unconnected feasible regions. A simple density measuring method was put forward for external archive maintenance, which intended to improve the efficiency of the algorithm. A new global guide selection strategy was put forward, which brought better convergence and diversity to the algorithm. The computer simulation results show that the CMOPSO algorithm can find a sufficient number of Pareto optimal solutions that have better distribution, uniformity, and approachability.

Key words: multi-objective optimization, Multi-Objective Particle Swarm Optimization (MOPSO), distance measurement, archive maintenance, global guide selection strategy

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