Geodesic
A new hypervolume-based differential evolution algorithm for many-objective optimization
Liu, Chao1, 2 ; Zhao, Qi1, 2  ; Yan, Bai3 ; Gao, Yang1, 2
1 College of Economics and Management, Beijing University of Technology, Beijing 100124, China.
2 Research Base of Beijing Modern Manufacturing Development, Beijing 100124, China
3 Institute of Laser Engineering, Beijing University of Technology, Beijing 100124, China
RAIRO - Operations Research - Recherche Opérationnelle, Tome 51 (2017) no. 4, pp. 1301-1315

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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.

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Accepté le :
DOI : 10.1051/ro/2017014
Classification : 65K10, 68T20
Keywords: Differential Evolution, Hypervolume indicator, Many-objective optimization, Many-objective evolutionary algorithm

Liu, Chao 1, 2 ; Zhao, Qi 1, 2 ; Yan, Bai 3 ; Gao, Yang 1, 2

1 College of Economics and Management, Beijing University of Technology, Beijing 100124, China.
2 Research Base of Beijing Modern Manufacturing Development, Beijing 100124, China
3 Institute of Laser Engineering, Beijing University of Technology, Beijing 100124, China 
@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://geodesic.mathdoc.fr/articles/10.1051/ro/2017014/}
}
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AU  - Yan, Bai
AU  - Gao, Yang
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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

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