.png){kind=icon}
返回
Guiding Evolutionary Multiobjective Optimization With Generic Front Modeling
.png){kind=icon}
OA
AI
DOI：10.1109/TCYB.2018.2883914.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In evolutionary multiobjective optimization, the Pareto front (PF) is approximated by using a set of representative candidate solutions with good convergence and diversity. However, most existing multiobjective evolutionary algorithms (MOEAs) have general difficulty in the approximation of PFs with complicated geometries. To address this issue, we propose a generic front modeling method for evolutionary multiobjective optimization, where the shape of the nondominated front is estimated by training a generalized simplex model. On the basis of the estimated front, we further develop an MOEA, where both the mating selection and environmental selection are driven by the approximate nondominated fronts modeled during the optimization process. For performance assessment, the proposed algorithm is compared with several state-of-the-art evolutionary algorithms on a wide range of benchmark problems with various types of PFs and different numbers of objectives. Experimental results demonstrate that the proposed algorithm performs consistently on a variety of multiobjective optimization problems.
Keyword:
Optimization
Shape
Training
Computer science
Sociology
Statistics
Evolutionary computation
Evolutionary algorithm
fitness function
front modeling
multiobjective and many-objective optimization
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:
10.5
论文数:
1.1W
被引数:
5.0W
机构
引用论文
Refining Estimates of Bird Collision and Electrocution Mortality at Power Lines in the United States.png)
PLoS ONEOAAI
PLoS ONEIF0
OAAI分享收藏PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective OptimizationPlatEMO: 用于进化多目标优化的MATLAB平台OAAI
OAAI分享收藏
分享收藏
查看更多内容