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山东大学学报 (理学版) ›› 2018, Vol. 53 ›› Issue (11): 56-66.doi: 10.6040/j.issn.1671-9352.0.2018.002

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一种解多目标优化问题的基于分解的人工蜂群算法

万鹏飞,高兴宝*   

  1. 陕西师范大学数学与信息科学学院, 陕西 西安 710119
  • 发布日期:2018-11-14
  • 作者简介:万鹏飞(1993— ), 男, 硕士研究生, 研究方向为智能优化算法. E-mail:wanpf@snnu.edu.cn*通信作者简介:高兴宝(1966— ), 男, 博士, 教授, 研究方向为智能优化方法、最优化理论与算法. E-mail:xinbaog@snnu.edu.cn
  • 基金资助:
    国家自然科学基金资助项目(61273311);中央高校基本科研业务费专项资金资助项目(GK201603002,2017TS002)

Novel artificial bee colony algorithm based on objective space decomposition for solving multi-objective optimization problems

WAN Peng-fei, GAO Xing-bao*   

  1. School of Mathematics and Information Science, Shannxi Normal University, Xian 710119, Shaanxi, China
  • Published:2018-11-14
  • About author:国家自然科学基金资助项目(61273311);中央高校基本科研业务费专项资金资助项目(GK201603002,2017TS002)
  • Supported by:
    国家自然科学基金资助项目(61273311);中央高校基本科研业务费专项资金资助项目(GK201603002,2017TS002)

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摘要： 在处理多目标优化问题时,如何平衡所得解集的分布性与收敛性是一个困难又重要的工作。为此,提出了解决该问题的一种基于目标空间分解的人工蜂群算法(MOABC/D)。首先采用一组方向向量将目标空间分解成一系列的子区域,并在每一个子区域至少保留一个解来保持解的分布性,其次提出一个基于分解的选择策略和2个基于信息交换的搜索策略来提高人工蜂群算法的搜索能力,并采用一个基于高斯分布的搜索策略来增强人工蜂群算法的搜索效率。为验证所提算法的性能,与8种同类算法在10个测试问题上进行比较。结果表明,本文所提算法得到的解集具有更好的收敛性能和分布性能。

关键词: 多目标优化问题, 目标空间分解, 人工蜂群算法, 搜索策略

Abstract: When solving multi-objective optimization problems, how to keep balance between convergence and distribution of solutions is a task which extremely important, but it is not easy. In this paper, we develop a novel artificial bee colony algorithm based on objective space decomposition for solving these issues. First, we divide the objective space into a series of sub-regions by a set of direction vectors, and one solution is at least chosen at each sub-region to maintain the diversity of the obtained solutions. To improve the convergence performance, we propose a search strategy based on information exchanging and two selection strategies based on decomposition respectively to enhance the search capacity of artificial bee colony algorithm. Moreover, a search strategy based on Gaussian distribution is employed to improve the effectiveness. The proposed algorithm is empirically compared with eight state-of-the-art multi-objective evolutionary algorithms on 10 benchmark problems. The comparative results demonstrate that the convergence and distribution performance of the proposed algorithm are superior to the compared algorithms.

Key words: multi-objective optimization problems, objective space decomposition, artificial bee colony algorithm, search strategy

中图分类号: 

  • TP301
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