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模式识别与人工智能  2017, Vol. 30 Issue (12): 1069-1082    DOI: 10.16451/j.cnki.issn1003-6059.201712002
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基于差异化邻域策略的分解多目标进化算法*
王丽萍1,2,吴峰1,2,张梦紫1,2,邱飞岳3
1.浙江工业大学 经贸管理学院 杭州 310023
2.浙江工业大学 信息智能与决策优化研究所 杭州 310023
3.浙江工业大学 教育科学与技术学院 杭州 310023
Decomposition Multi-objective Evolutionary Algorithm Based on Differentiated Neighborhood Strategy
WANG Liping1,2, WU Feng1,2, ZHANG Mengzi1,2, QIU Feiyue3
1.College of Economics and Management, Zhejiang University of Technology, Hangzhou 310023
2.Institute of Information Intelligence and Decision Optimization, Zhejiang University of Technology, Hangzhou 310014
3.College of Educational Science and Technology, Zhejiang University of Technology, Hangzhou 310023

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摘要 子问题邻域对基于分解的多目标进化算法性能影响较大.当邻域过大时,种群繁殖产生的新解偏离Pareto解集,在更新子问题时,新解与邻域内旧解的比较次数增多,算法的计算复杂度增加;当邻域过小时,算法容易陷入局部最优.为了解决上述问题,文中提出基于差异化邻域策略的分解多目标进化算法(MOEA/D-DN),通过分析不同大小的邻域对算法性能的影响,选择合适的参数.并根据每个子问题的权重向量与中心向量的偏角,为各子问题设置不同大小的邻域,合理分配算法资源,提高算法搜索全局最优解的速率.在2维ZDT系列和3维、5维DTLZ系列测试函数上的实验表明,MOEA/D-DN 的收敛速度与收敛性能均有明显提高,算法的计算资源分配更合理,所获解集整体质量更优.
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王丽萍
吴峰
张梦紫
邱飞岳
关键词 多目标优化不同子问题差异化邻域资源分配    
Abstract:The performance of the decomposition-based multi-objective evolutionary algorithm is easily affected by the neighborhood of a subproblem. When the neighborhood is too large, the new solutions generated by the population propagation deviate from the Pareto set and the frequency of comparison between new solutions and old solutions in the neighborhood is increased for the updating subproblems. Consequently, the computational complexity of the algorithm is increased. If the neighborhood is too small, the algorithm easily falls into the local optimum. To solve this problem, a decomposed multi-objective evolutionary algorithm based on differentiated neighborhood strategy(MOEA/D-DN) is proposed. The suitable parameters are selected by analyzing the influence of different neighborhood sizes on the algorithm performance and different sizes of neighborhood for each subproblem are set according to the angle between their weight vectors and the central vector. Thus, the resource of algorithm is allocated more rationally and the velocity of searching for the optimal solution is also improved. Finally, the experimental results on the test functions of 2 dimensional ZDT and 3-5 dimensional DTLZ show that the convergence rate and the performance of MOEA/D-DN algorithm are improved obviously and the computational resource allocation of the algorithm is more reasonable. The overall solution quality is better.
Key wordsMulti-objective Optimization    Different Subproblem    Differentiated Neighborhood    Resource Allocation   
收稿日期: 2017-08-21     
ZTFLH: TP 18  
基金资助:国家自然科学基金项目(No.61472366,61379077)、浙江省自然科学基金项目(No.LY17F020022)资助
作者简介: 王丽萍,女,1964年生,博士,教授,主要研究方向为决策优化、商务智能.E-mail:wlp@zjut.edu.cn.
吴 峰,男,1992年生,硕士研究生,主要研究方向为智能进化算法、决策优化.E-mail:1095847476@qq.com.
张梦紫,女,1993年生,硕士研究生,主要研究方向为智能进化算法、决策优化.E-mail:zmzjdjh@qq.com.
邱飞岳(通讯作者),男,1965年生,博士,教授,主要研究方向为智能计算、深度学习.E-mail:qfy@zjut.edu.cn.
引用本文:   
王丽萍,吴峰,张梦紫,邱飞岳. 基于差异化邻域策略的分解多目标进化算法*[J]. 模式识别与人工智能, 2017, 30(12): 1069-1082. WANG Liping, WU Feng, ZHANG Mengzi, QIU Feiyue. Decomposition Multi-objective Evolutionary Algorithm Based on Differentiated Neighborhood Strategy. , 2017, 30(12): 1069-1082.
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