Journal article
Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization
Expert Systems with Applications, Vol.47, pp.106-119
2016
Abstract
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.
Details
- Title
- Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization
- Authors
- Seyedali Mirjalili (Corresponding Author) - Griffith UniversityShahrzad Saremi - Griffith UniversitySeyed Mohammad MirjaliliLeandro dos S. Coelho - Pontifícia Universidade Católica do Paraná
- Publication details
- Expert Systems with Applications, Vol.47, pp.106-119
- Publisher
- Elsevier Ltd
- Date published
- 2016
- DOI
- 10.DOI: 10.1016/j.eswa.2015.10.039
- ISSN
- 1873-6793
- Organisation Unit
- School of Science, Technology and Engineering
- Language
- English
- Record Identifier
- 991136504902621
- Output Type
- Journal article
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