Evolutionary algorithms are successfully used for many-objective optimization. However, solutions are prone to become nondominated from each other with the increase in the number of objectives, which reduces the efficiency of Pareto dominance-based algorithms. In this paper, a new hypervolume-based differential evolution algorithm (MODEhv) is proposed for many-objective optimization problems (MaOPs). In MODEhv, a modified differential evolution paradigm with automatic parameter configuration strategy is used to balance exploration and exploitation of the algorithm. Besides, the hypervolume indicator is incorporated for the selection of solutions to be varied and solutions to be kept in archive respectively. Finally, a threshold technique is employed to improve diversity of solutions in archive. MODEhv is investigated on a set of widely used benchmark problems and compared with five state-of-the-art algorithms. The experimental results show the efficiency of MODEhv for solving MaOPs.
Accepté le :
DOI : 10.1051/ro/2017014
Mots-clés : Differential Evolution, Hypervolume indicator, Many-objective optimization, Many-objective evolutionary algorithm
@article{RO_2017__51_4_1301_0,
author = {Liu, Chao and Zhao, Qi and Yan, Bai and Gao, Yang},
title = {A new hypervolume-based differential evolution algorithm for many-objective optimization},
journal = {RAIRO - Operations Research - Recherche Op\'erationnelle},
pages = {1301--1315},
publisher = {EDP-Sciences},
volume = {51},
number = {4},
year = {2017},
doi = {10.1051/ro/2017014},
mrnumber = {3783946},
zbl = {1398.65143},
language = {en},
url = {http://archive.numdam.org/articles/10.1051/ro/2017014/}
}
TY - JOUR AU - Liu, Chao AU - Zhao, Qi AU - Yan, Bai AU - Gao, Yang TI - A new hypervolume-based differential evolution algorithm for many-objective optimization JO - RAIRO - Operations Research - Recherche Opérationnelle PY - 2017 SP - 1301 EP - 1315 VL - 51 IS - 4 PB - EDP-Sciences UR - http://archive.numdam.org/articles/10.1051/ro/2017014/ DO - 10.1051/ro/2017014 LA - en ID - RO_2017__51_4_1301_0 ER -
%0 Journal Article %A Liu, Chao %A Zhao, Qi %A Yan, Bai %A Gao, Yang %T A new hypervolume-based differential evolution algorithm for many-objective optimization %J RAIRO - Operations Research - Recherche Opérationnelle %D 2017 %P 1301-1315 %V 51 %N 4 %I EDP-Sciences %U http://archive.numdam.org/articles/10.1051/ro/2017014/ %R 10.1051/ro/2017014 %G en %F RO_2017__51_4_1301_0
Liu, Chao; Zhao, Qi; Yan, Bai; Gao, Yang. A new hypervolume-based differential evolution algorithm for many-objective optimization. RAIRO - Operations Research - Recherche Opérationnelle, Tome 51 (2017) no. 4, pp. 1301-1315. doi : 10.1051/ro/2017014. http://archive.numdam.org/articles/10.1051/ro/2017014/
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