电子学报 ›› 2019, Vol. 47 ›› Issue (11): 2359-2367.DOI: 10.3969/j.issn.0372-2112.2019.11.018

所属专题: 多目标优化

• 学术论文 • 上一篇    下一篇

一种多策略协同的多目标萤火虫算法

谢承旺1,2, 张飞龙2, 陆建波1, 肖驰2, 龙广林1   

  1. 1. 南宁师范大学计算机与信息工程学院, 广西南宁 530299;
    2. 华东交通大学软件学院, 江西南昌 330013
  • 收稿日期:2018-10-17 修回日期:2019-03-28 出版日期:2019-11-25 发布日期:2019-11-25
  • 通讯作者: 陆建波
  • 作者简介:谢承旺 男,1974年10月生于湖北武汉.计算机博士,数学博士后,CCF高级会员,南宁师范大学计算机与信息工程学院教授,硕士生导师,主要研究方向为智能计算与多目标优化等.E-mail:chengwangxie@163.com;张飞龙 男,1992年6月生于河南周口.硕士研究生,主要研究领域为智能计算与多目标优化.E-mail:862677277@qq.com
  • 基金资助:
    国家自然科学基金(No.61763010);广西八桂学者项目(No.桂科政字[2016]127号);广西创新驱动重大专项(No.AA18118047);广西科技基地和人才专项(No.桂科AD18126015);广西自然科学基金(No.2018GXNSFAA138056);广西研究生教育创新计划资助(No.YCSW2019182)

Multi-Objective Firefly Algorithm Based on Multiply Cooperative Strategies

XIE Cheng-wang1,2, ZHANG Fei-long2, LU Jian-bo1, XIAO Chi2, LONG Guang-lin1   

  1. 1. School of Computer and Information Engineering, Nanning Normal University, Nanning, Guangxi 530299, China;
    2. School of Software, East China Jiaotong University, Nanchang, Jiangxi 330013, China
  • Received:2018-10-17 Revised:2019-03-28 Online:2019-11-25 Published:2019-11-25

摘要: 现实中的多目标优化问题不断增多且日益复杂,需要不断发展新型启发式算法应对挑战.提出一种多策略协同的多目标萤火虫算法MOFA-MCS.该算法采用均匀化与随机化相结合的方法产生初始种群;利用档案集中的精英解个体指导萤火虫移动;并在移动的过程施加Lévy flights随机扰动;最后,利用ε-三点最短路径策略维护档案解群的多样性.MOFA-MCS算法与其他6种经典的多目标进化算法一同在12个基准的多目标测试问题上进行实验,结果表明所提算法在收敛性、多样性方面总体上具有显著的性能优势.

关键词: 多目标优化问题, 萤火虫算法, 多目标萤火虫算法, 多策略协同

Abstract: More and more complex multi-objective optimization problems have emerged in the real world,and the novel heuristic algorithms need to be developed to meet the challenge.A multi-objective firefly algorithm based on multiply cooperative strategies (MOFA-MCS) is proposed in the paper.MOFA-MCS uses the method of homogenization and randomization to generate the initial population,adopts the elite solutions in the external archive to lead the firefly to move,exerts Lévy flights to add random disturbance in the moving process,and finally,the ε-three-point shortest path strategy is also applied to maintain the diversity of the archive solutions.MOFA-MCS is compared with other six representative multi-objective evolutionary algorithms on 12 benchmark multi-objective test problems,and the experimental results show that MOFA-MCS has significant performance advantages in terms of convergence and diversity.

Key words: multi-objective optimization problem, firefly algorithm, multi-objective firefly algorithm, multiply cooperative strategies

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