Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization

Due to the novelty of the Grey Wolf Optimizer (GWO), there is no study in the literature to design a multi-objective version of this algorithm. This paper proposes a Multi-Objective Grey Wolf Optimizer (MOGWO) in order to optimize problems with multiple objectives for the first time. A fixed-sized e...

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Bibliographic Details
Published in:Expert systems with applications Vol. 47; pp. 106 - 119
Main Authors: Mirjalili, Seyedali, Saremi, Shahrzad, Mirjalili, Seyed Mohammad, Coelho, Leandro dos S.
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.04.2016
Subjects:
Algorithms
Archives
Computer simulation
Engineering optimization
Evolutionary algorithm
Grey wolf optimizer
Heuristic algorithm
Hunting
Meta-heuristic
Multi-criterion optimization
Multi-objective optimization
Optimization
Searching
Source code
Wolves
Multi-criterion optimization
Heuristic algorithm
Evolutionary algorithm
Engineering optimization
Multi-objective optimization
Meta-heuristic
Grey wolf optimizer
ISSN:0957-4174, 1873-6793
Online Access:Get full text
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Summary:Due to the novelty of the Grey Wolf Optimizer (GWO), there is no study in the literature to design a multi-objective version of this algorithm. This paper proposes a Multi-Objective Grey Wolf Optimizer (MOGWO) in order to optimize problems with multiple objectives for the first time. A fixed-sized external archive is integrated to the GWO for saving and retrieving the Pareto optimal solutions. This archive is then employed to define the social hierarchy and simulate the hunting behavior of grey wolves in multi-objective search spaces. The proposed method is tested on 10 multi-objective benchmark problems and compared with two well-known meta-heuristics: Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D) and Multi-Objective Particle Swarm Optimization (MOPSO). The qualitative and quantitative results show that the proposed algorithm is able to provide very competitive results and outperforms other algorithms. Note that the source codes of MOGWO are publicly available at http://www.alimirjalili.com/GWO.html. •A novel multi-objective algorithm called Multi-objective Grey Wolf Optimizer is proposed.•MOGWO is benchmarked on 10 challenging multi-objective test problems.•The quantitative results show the superior convergence and coverage of MOGWO.•The coverage ability of MOGWO is confirmed by the qualitative results as well.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2015.10.039