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

Seyedali Mirjalili, Shahrzad Saremi, Seyed Mohammad Mirjalili, Leandro Dos S. Coelho

Research output: Contribution to journalArticle

306 Citations (Scopus)

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.html.

Original languageEnglish
Pages (from-to)106-119
Number of pages14
JournalExpert Systems with Applications
Volume47
DOIs
Publication statusPublished - 1 Apr 2016
Externally publishedYes

Keywords

  • Engineering optimization
  • Evolutionary algorithm
  • Grey wolf optimizer
  • Heuristic algorithm
  • Meta-heuristic
  • Multi-criterion optimization
  • Multi-objective optimization

Fingerprint Dive into the research topics of 'Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization'. Together they form a unique fingerprint.

  • Cite this