|Table of Contents|

Differential Evolution Algorithm for Multi-Objective OptimizationBased on Adaptive ε-Dominance(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2015年01期
Page:
119-
Research Field:
计算机科学
Publishing date:

Info

Title:
Differential Evolution Algorithm for Multi-Objective OptimizationBased on Adaptive ε-Dominance
Author(s):
Xu Jin1; 2; Gu Qiong1; 3; Cai Zhihua2; Gong Wenyin2
(1.School of Mathematics and Computer Science,Hubei University of Arts and Science,Xiangyang 441053,China)(2.School of Computer Science,China University of Geosciences,Wuhan 430074,China)(3.Institute of Logic and Intelligence,Southwest University,Chongqing 400715,China)
Keywords:
multi-objective optimization; Pareto optimal solution; differential evolution; orthogonal design; adaptive ε-dominance
PACS:
TP301.6
DOI:
-
Abstract:
The purpose to solve multi-objective optimization is to get solutions closing to the true Pareto front as much as possible and having good diversity. To meet these two demands,an algorithm is proposed in this paper,which has these characteristics:firstly,it adopts the orthogonal design method with quantization technology to generate initial population whose individuals are scattered uniformly over the target search space. So the algorithm can use them sufficiently in the subsequent iterations. What’s more,it is based on an adaptive ε concept to obtain a good distribution along the true Pareto-optimal solutions. Finally,experiments on five benchmark problems with different features have shown that this algorithm does well not only in distribution,but also in convergence when compared to other evolution algorithms.

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Last Update: 2015-03-30