• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

J4 ›› 2013, Vol. 35 ›› Issue (8): 77-88.

• 论文 • 上一篇    下一篇

多目标模拟退火算法及其应用研究进展

李金忠,夏洁武,曾小荟,曾劲涛,刘新明,冷明,孙凌宇   

  1. (井冈山大学电子与信息工程学院,江西 吉安 343009)
  • 收稿日期:2012-07-02 修回日期:2012-10-22 出版日期:2013-08-25 发布日期:2013-08-25
  • 基金资助:

    国家自然科学基金资助项目(61163062,61063007);江西省教育厅科技项目(GJJ12487);江西省自然科学基金资助项目(20122BAB201038);江西省科技厅科技支撑项目(20122BBG70161);吉安市科技局2011年度指导性科技计划项目(19)

Survey of multiobjective simulated
annealing algorithm and its applications    

LI Jinzhong,XIA Jiewu,ZENG Xiaohui,ZENG Jintao,LIU Xinming,LENG Ming,SUN Lingyu   

  1. (School of Electronic and Information Engineering,Jinggangshan University,Ji’an 343009,China)
  • Received:2012-07-02 Revised:2012-10-22 Online:2013-08-25 Published:2013-08-25

摘要:

作为一种简单有效的多目标智能优化算法,多目标模拟退火(MOSA)算法已经引起了广泛研究并在许多领域得到应用。针对近二十年来MOSA算法及其应用的进展进行了系统的综述和评论。首先描述了MOSA算法的基本框架;接着讨论了几种典型的MOSA算法,重点探讨了这些算法的接受概率函数的计算方法,并对这些算法进行归类性分析;然后介绍了MOSA算法的应用进展;最后,根据当前MOSA算法的研究状况,展望了该算法若干值得进一步研究的方向和所面临的挑战。可为今后对MOSA算法的改进以及在实际工程应用中的研究提供综合参考。

关键词: 多目标优化, 多目标模拟退火, 算法, 应用

Abstract:

 Multi-Objective Simulated Annealing (MOSA) algorithm has been widely studied and applied to various fields successfully as a simple and effective multi-objective intelligence optimization algorithm. A systematic survey and discussion of the development of MOSA algorithm and its application in the recent twenty years are introduced. Firstly, the generic framework of MOSA algorithm is briefly described. Secondly, several typical MOSA algorithms are discussed, calculation methods of acceptance probability functions for those algorithms are emphatically addressed, and these algorithms are classified and analyzed. Thirdly, some typical applications of MOSA algorithms are introduced. Finally, some promising directions and challenges for future research in the area of MOSA algorithm are proposed according to the present studies. This paper can provide a comprehensive reference for future study of MOSA in algorithm improvement and its practical applications.

Key words: multi-objective optimization;multi-objective simulated annealing;algorithm;application