A Novel Weight Design in Multi-objective Evolutionary Algorithm

This paper presents a method to improve the performance of MOEA/D. The idea is to approximate the Pareto front(PF) by using a linear interpolation of the non-dominant solutions. It propose a novel weight design method for multi-objective evolutionary algorithm. Even when the PF is complex, we can ob...

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Bibliographic Details
Published in2010 International Conference on Computational Intelligence and Security pp. 137 - 141
Main Authors Fang-Qing Gu, Hai-Lin Liu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2010
Subjects
Algorithm design and analysis
Design methodology
Equations
Evolutionary algorithm
Evolutionary computation
Interpolation
linear interpolation
Multi-objective optimization
Optimization
uniformly distribution
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Summary:This paper presents a method to improve the performance of MOEA/D. The idea is to approximate the Pareto front(PF) by using a linear interpolation of the non-dominant solutions. It propose a novel weight design method for multi-objective evolutionary algorithm. Even when the PF is complex, we can obtain the Pareto optimal solutions which are distributed uniformly over the PF. Some test functions are constructed to compare the performance of the proposed algorithm with that of MOEA/D. The results indicate that the proposed algorithm could significantly outperform MOEA/D on these test instances.
ISBN:9781424491148
1424491142
DOI:10.1109/CIS.2010.37