.png){kind=icon}
返回
A reference direction and entropy based evolutionary algorithm for many-objective optimization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2018.05.011.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Evolutionary algorithms have been successfully applied in dealing with multi-objective optimization problems with two or three objectives. However, when solving the problem with more than 3 objectives (also called as many-objective optimizing problem), most multi-objective evolutionary algorithms perform poorly due to the ineffectiveness of Pareto dominance relationship in a high-dimensional space, and the diversity maintenance mechanism usually leads the population to be far from the true Pareto front. In this paper, a novel approach is proposed to handle the challenges in the many-objective optimization problem. Firstly, a grid-based approach is adopted to eliminate dominance resistant solutions which are non-dominated solutions with excellent diversity while incur the dominance resistance and lead the population far from the true Pareto front. Secondly, a new diversity maintenance mechanism based on reference directions is proposed, which not only enhances the diversity but also takes the convergence into consideration. For a domination- relationship based MOEA hardly has enough convergence capability for a high-dimension optimizing problem, our approach embeds convergence capability into the diversity maintenance process, and balances the convergence and diversity capability according to evolutionary states and Pareto entropy. The proposed algorithm is evaluated on a number of standard benchmark functions, i.e., DTLZ1-7 and WFG1-9 with 3-, 4-, 5-, 8-, 10-objective and compared with 5 state-of-the-art Many-Objective Evolutionary Algorithms (MaOEAs). Experimental results demonstrate the proposed algorithm's competitiveness in both convergence and diversity in solving Many-Objective Optimization Problems. (C) 2018 Elsevier B.V. All rights reserved.
Keyword:
Reference direction
Pareto entropy
Many-objective optimization
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:
6.6
论文数:
1.4W
被引数:
4.8W
机构
引用论文
.png){kind=icon}
Adaptive Multiobjective Particle Swarm Optimization Based on Parallel Cell Coordinate System基于并行单元坐标系的自适应多目标粒子群优化算法
PREAI
分享收藏分享收藏
查看更多内容