.png){kind=icon}
返回
A new decomposition based evolutionary algorithm with uniform designs for many-objective optimization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2015.01.062.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
For many-objective optimization problems, how to get a set of solutions with good convergence and diversity is a difficult and challenging work. In this paper, a new decomposition based evolutionary algorithm with uniform designs is proposed to achieve the goal. The proposed algorithm adopts the uniform design method to set the weight vectors which are uniformly distributed over the design space, and the size of the weight vectors neither increases nonlinearly with the number of objectives nor considers a formulaic setting. A crossover operator based on the uniform design method is constructed to enhance the search capacity of the proposed algorithm. Moreover, in order to improve the convergence performance of the algorithm, a sub-population strategy is used to optimize each sub-problem. Comparing with some efficient state-of-the-art algorithms, e.g., NSGAII-CE, MOEA/D and HypE, on six benchmark functions, the proposed algorithm is able to find a set of solutions with better diversity and convergence. (C) 2015 Elsevier B.V. All rights reserved.
Keyword:
Multi-objective optimization
Decomposition
Uniform design
Weight vector
Many-objective optimization problems
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:
6.6
论文数:
1.4W
被引数:
4.8W
机构
引用论文
.png){kind=icon}
分享收藏分享收藏
Behavior of Multiobjective Evolutionary Algorithms on Many-Objective Knapsack Problems多目标进化算法在多目标背包问题上的行为
OAAI
OAAI分享收藏
查看更多内容