Evolutionary multiobjective optimization using a cultural algorithm

被引：65

作者：

Coello, CAC ^{[1
]}

Becerra, RL ^{[1
]}

机构：

[1] IPN, CINVESTAV, Colutionary Computat Grp, Dept Ing Elect,Secc Computac, Mexico City 07300, DF, Mexico

来源：

PROCEEDINGS OF THE 2003 IEEE SWARM INTELLIGENCE SYMPOSIUM (SIS 03) | 2003年

关键词：

cultural algorithms; multiobjective optimization; evolutionary programming;

D O I：

10.1109/SIS.2003.1202240

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we present the first proposal to use a cultural algorithm to solve multiobjective optimization problems. Our proposal uses, evolutionary programming, Pareto ranking and elitism (i.e., an external population). The approach proposed is validated using several examples taken from the specialized literature. Our results are compared with respect to the NSCA-II, which is an algorithm representative of the state-of-the-art in evolutionary multiobjective optimization. The performance of our approach indicates that cultural algorithms are a viable alternative for multiobjective optimization.

引用

页码：6 / 13

页数：8