Computer Science ›› 2016, Vol. 43 ›› Issue (Z11): 117-122.doi: 10.11896/j.issn.1002-137X.2016.11A.025

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Multi-objective Evolutionary Algorithm Based Weight Vectors Generation Method of MOEA/D

MA Qing   

  • Online:2018-12-01 Published:2018-12-01

Abstract: In the evolutionary multi-objective optimization (EMO) community,multi-objective optimization refers to simultaneous optimization of multi-objective problems with more than one objective,which has gained more and more attention in recent years.After the raise of MOEA/D,the aggregation-based multi-objective evolutionary algorithms have obtained more and more research,and there have been many achievements with regard to the improvement of MOEA/D.While,there has been little research about the generation method of weight vectors for MOEA/D.This paper proposed a method to generate any number of well-distributed weight vectors using MOEAs.And the generated weight vectors are applied to MOEA/D,MSOPS and NSGA-III.Then,the three aggregation-based multi-objective evolutionary algorithms are comprehensively compared through testing on DTLZ test suit,multi-objective TSP and rectangle test problem in order to study their optimization abilities on continuous and combinatorial problems and a visual observation in the decision space,respectively.The experimental results show that,none of the algorithms is able to solve problems with all different properties.While,the performance of MOEA/D_Tchebycheff and MOEA/D_PBI is better than MSOPS and NSGA-III in most cases.

Key words: Evolutionary multi-objective optimization(EMO),Multi-objective evolutionary algorithm(MOEA),Multi-objective optimization problems,Performance indicator,Solution visualization

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