Paper Info
Title
A many-objective evolutionary algorithm with epsilon-indicator direction vector.
Abstract
The major difficulty in multi-objective optimization evolutionary algorithms (MOEAs) is how to find an appropriate solution which is able to converge towards the true Pareto Front with high diversity. In order to strengthen the selection pressure of the algorithms, indicator-based algorithms have been proposed to handle many-objective optimization problems (MaOPs), among which binary addition quality indicator Iε+ is superior to other indicators in terms of low computational complexity. However it often has edge effects which degrade the performance of MOEA. In this work, we devise a new MOEA approach, which is able to combine binary addition quality indicator Iε+ with direction vector (EDV), to address MaOPs. At the same time, an efficient resource allocation strategy is developed to improve the diversity distribution of solutions. Simulation results are presented to show that EDV outperforms state-of-the-art approaches in all problems considered in this paper, and takes a great advantage in solving the black box problem.
Year
DOI
Venue
2019
10.1016/j.asoc.2018.11.041
Applied Soft Computing
Keywords
Field
DocType
Many-objective evolutionary algorithm,Many-objective test problems,Binary quality indicator,Uniform direction vector
Mathematical optimization,Evolutionary algorithm,Direction vector,Multi-objective optimization,Resource allocation,Black box,Optimization problem,Binary addition,Mathematics,Computational complexity theory
Journal
Volume
ISSN
Citations 
76
1568-4946
0
PageRank 
References 
Authors
0.34
19
4
Name
Order
Citations
PageRank
Yun Yang1769.80
Jianping Luo21076.99
Lei Huang356568.88
Qiqi Liu4384.99