DocumentCode :
467731
Title :
Solving Multi-Objective Optimization Problems by a Bi-Objective Evolutionary Algorithm
Author :
Wang, Yu-Ping
Author_Institution :
Xidian Univ., Xi´´an
Volume :
2
fYear :
2007
fDate :
19-22 Aug. 2007
Firstpage :
1018
Lastpage :
1024
Abstract :
In this paper a novel model for multiobjective optimization problem is proposed first, in which the multiobjective optimization problem is transformed into a bi-objective optimization problem. In this bi-objective problem one objective is responsible for optimizing the quality of the solutions, and the other is to improve the distribution of the obtained nondominated solution set. Then a new crossover operator and selection scheme are designed. Based on these, a specific-designed evolutionary algorithm is presented. The simulations on five widely used benchmark problems are made and the results indicate that the proposed algorithm is efficient and outperforms the compared algorithms.
Keywords :
evolutionary computation; optimisation; set theory; biobjective evolutionary algorithm; crossover operator; multiobjective optimization problems; nondominated solution set; Computer science; Cybernetics; Distributed computing; Electronic mail; Evolutionary computation; Genetic programming; Machine learning; Mathematical model; Pareto optimization; Stochastic processes;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics, 2007 International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-0973-0
Electronic_ISBN :
978-1-4244-0973-0
Type :
conf
DOI :
10.1109/ICMLC.2007.4370292
Filename :
4370292
Link To Document :
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