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Journal of Software:2017.28(10):2704-2721

自适应偏好半径划分区域的多目标进化方法
王帅发,郑金华,胡建杰,邹娟,喻果
(智能计算与信息处理教育部重点实验室(湘潭大学), 湖南 湘潭 411105;智能计算与信息处理教育部重点实验室(湘潭大学), 湖南 湘潭 411105;智能信息处理与应用湖南省重点实验室(衡阳师范学院), 湖南 衡阳 421002;Computer Sciences, University of Surrey, Guildford, Surrey GU2 7XH, UK)
Multi-Objective Evolutionary Algorithm for Adaptive Preference Radius to Divide Region
WANG Shuai-Fa,ZHENG Jin-Hua,HU Jian-Jie,ZOU Juan,YU Guo
(Key Laboratory of Intelligent Computing and Information Processing, Ministry of Education (Xiangtan University), Xiangtan 411105, China;Key Laboratory of Intelligent Computing and Information Processing, Ministry of Education (Xiangtan University), Xiangtan 411105, China;Hu'nan Provincial Key Laboratory of Intelligent Information Processing and Application (Hengyang Normal University), Hengyang 421002, China;Computer Sciences, University of Surrey, Guildford, Surrey GU2 7XH, UK)
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Received:January 08, 2017    Revised:April 05, 2017
> 中文摘要: 偏好多目标进化算法是一类帮助决策者找到感兴趣的Pareto最优解的算法.目前,在以参考点位置作为偏好信息载体的偏好多目标进化算法中,不合适的参考点位置往往会严重影响算法的收敛性能,偏好区域的大小难以控制,在高维问题上效果较差.针对以上问题,通过计算基于种群的自适应偏好半径,利用自适应偏好半径构造一种新的偏好关系模型,通过对偏好区域进行划分,提出基于偏好区域划分的偏好多目标进化算法.将所提算法与4种常用的以参考点为偏好信息载体的多目标进化算法g-NSGA-II、r-NSGA-II、角度偏好算法、MOEA/D-PRE进行对比实验,结果表明,所提算法具有较好的收敛性能和分布性能,决策者可以控制偏好区域大小,在高维问题上也具有较好的收敛效果.
Abstract:The preference-based multi-objective evolutionary algorithms are the sort of evolutionary algorithms to assist the decision maker (DM) in finding interesting Pareto optimal solutions.At present, the inappropriate locations of the reference points sometimes seriously impact the convergence performance of the algorithms when the locations of the reference points are used as the preference information during the optimization.Moreover, the size of the preferred region is difficult to control.And the comprehensive performance of the algorithms will degrade in dealing with many-objective problems.To address the above issues, in this paper, the self-adjustable preference-based radius is calculated to build a new preference relation model, and by dividing region of interest (ROI), a preference-based multi-objective evolutionary algorithm based on the division of RoI is proposed.The proposed algorithm is compared with four reference point based multi-objective evolutionary algorithms (g-NSGA-II, r-NSGA-II, angle-based preference algorithm and MOEA/D-PRE).The results show that the proposed algorithm has good convergence and diversity, and at meantime allows the DM control the size of the preferred region.In addition it has a good convergence in addressing the many-objective problems.
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基金项目:国家自然科学基金(61502408,61673331,61379062,61403326);湖南省教育厅重点项目(17A212);赛尔网络创新项目(NGII20150302);湖南省自然科学基金(14JJ2072,2017JJ4001);湖南省科技计划(2016TP1020) 国家自然科学基金(61502408,61673331,61379062,61403326);湖南省教育厅重点项目(17A212);赛尔网络创新项目(NGII20150302);湖南省自然科学基金(14JJ2072,2017JJ4001);湖南省科技计划(2016TP1020)
Foundation items:National Natural Science Foundation of China (61502408, 61673331, 61379062, 61403326); Key Project of Hu’nan Provincial Education Department (17A212); CERNET Innovation Project (NGII20150302); Natural Science Foundation of Hu’nan Province of China (14JJ2072, 2017JJ4001); The Science and Technology Plan Project of Hu’nan Province of China (2016TP1020)
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王帅发,郑金华,胡建杰,邹娟,喻果.自适应偏好半径划分区域的多目标进化方法.软件学报,2017,28(10):2704-2721

WANG Shuai-Fa,ZHENG Jin-Hua,HU Jian-Jie,ZOU Juan,YU Guo.Multi-Objective Evolutionary Algorithm for Adaptive Preference Radius to Divide Region.Journal of Software,2017,28(10):2704-2721