.png){kind=icon}
返回
Multi-Objective Optimization by Using Evolutionary Algorithms: The p-Optimality Criteria
.png){kind=icon}
PRE
AI
DOI：10.1109/TEVC.2013.2243455.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, a novel general class of optimality criteria is defined and proposed to solve multi-objective optimization problems by using evolutionary algorithms. These criteria, named p-optimality criteria, allow us to value (assess) the relative importance of those solutions with outstanding performance in very few objectives and poor performance in all others, regarding those solutions with an equilibrium (balance) among all the objectives. The optimality criteria avoid interrelating the relative values of the different objectives, respecting the integrity of each one in a rational way. As an example, a simple multi-objective approach based on the p-optimality criteria and genetic algorithms is designed, where solutions used to generate new solutions are selected according to the proposed optimality criteria. It is implemented and applied on several benchmark test problems, and its performance is compared to that of the nondominated sort genetic algorithm-II method, in order to analyze the contribution and potential of these new optimality criteria.
Keyword:
Evolutionary algorithms
genetic algorithms
multi-objective optimization
optimality criteria
optimality criterion
期刊
IF:
12
论文数:
1.8K
被引数:
2.4W
机构
暂无机构信息
引用论文
分享收藏
Strong territoriality and weak density‐dependent reproduction in Lesser Spotted Eagles Clanga pomarina.png)
Ibis
IbisIF0
PREAI
分享收藏分享收藏
查看更多内容