.png){kind=icon}
返回
An improved MOEA/D design for many-objective optimization problems
.png){kind=icon}
PRE
AI
DOI：10.1007/s10489-018-1183-5.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
MOEA/D is one of the most popular multi-objective evolutionary algorithms. To extend the effective application scope of MOEA/D for high-dimensional objectives, an improved MOEA/D design for many-objective optimization problems, named I-MOEA/D, is proposed in this paper. Comparing with the original MOEA/D, we redesigned the weight vectors used in the subproblems, making the distribution of the weight vectors broader and more effective to ensure the diversity and convergence of solutions in the objective space. Moreover, a new decomposition approach, called the weighted mixture-style method, which combines the advantages of the weighted sum decomposition and the Tchebycheff decomposition approaches, is adopted in I-MOEA/D to improve the effectiveness of the algorithm. A three-part experimental comparison using DTLZ1-DTLZ4, with the number of objectives ranging from three to fifteen, is performed. Experimental results verify the effectiveness of each strategy and reveal that the proposed I-MOEA/D method achieves better performance than the other related state-of-the-art algorithms in solving this type of many-objective optimization problems.
Keyword:
Many-objective optimization problems
Weight vector design
Decomposition
Evolutionary algorithm
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.5
论文数:
7.6K
被引数:
1.7W
机构
引用论文
Refining Estimates of Bird Collision and Electrocution Mortality at Power Lines in the United States.png)
PLoS ONE
OAAI
PLoS ONEIF0
OAAI分享收藏
分享收藏
查看更多内容