Genetic symbiosis algorithm for multiobjective optimization problem

被引：11

作者：

Mao, JM ^{[1
]}

Hirasawa, K ^{[1
]}

Hu, JL ^{[1
]}

Murata, J ^{[1
]}

机构：

[1] Kyushu Univ, Dept Elect & Elect Syst Engn, Higashi Ku, Fukuoka 8128581, Japan

来源：

IEEE RO-MAN 2000: 9TH IEEE INTERNATIONAL WORKSHOP ON ROBOT AND HUMAN INTERACTIVE COMMUNICATION, PROCEEDINGS | 2000年

关键词：

D O I：

10.1109/ROMAN.2000.892484

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Evolutionary Algorithms are often well-suited for optimization problems. Since the mid-1980's, interest in multiobjective problems has been expanding rapidly. Various evolutionary algorithms have been developed which are capable of searching for multiple solutions concurrently in a single run. In this paper, we proposed a genetic symbiosis algorithm (GSA) for multi-object optimization problems (MOP) based on the symbiotic concept found widely in ecosystem. In the proposed CSA for MOP, a set of symbiotic parameters are introduced to modify the fitness of individuals used for reproduction so as to obtain a variety of Pareto solutions corresponding to user's demands. The symbiotic parameters are trained by minimizing a user defined criterion function. Several numerical simulations are carried out to demonstrate the effectiveness of proposed GSA.

引用

页码：137 / 142

页数：6