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Recent studies on optimisation method of Grey Wolf Optimiser (GWO): a review (2014–2017)

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Abstract

Today, finding a viable solution for any real world problem focusing on combinatorial of problems is a crucial task. However, using optimisation techniques, a viable best solution for a specific problem can be obtained, developed and solved despite the existing limitations of the implemented technique. Furthermore, population based optimisation techniques are now a current interest and has spawned many new and improved techniques to rectify many engineering problems. One of these methods is the Grey Wolf Optimiser (GWO), which resembles the grey wolf’s leadership hierarchy and its hunting behavior in nature. The GWO adopts the hierarchical nature of grey wolfs and lists the best solution as alpha, followed by beta and delta in descending order. Additionally, its hunting technique of tracking, encircling and attacking are also modeled mathematically to find the best optimised solution. This paper presents the results from an extensive study of 83 published papers from previous studies related to GWO in various applications such as parameter tuning, economy dispatch problem, and cost estimating to name a few. A discussion on the properties of GWO algorithm and how it minimises the different problems in the different applications is presented, as well as an analysis on the research trend of GWO optimisation technique in various applications from year 2014 to 2017. Based on the literatures, it was observed that GWO has the ability to solve single and multi-objective problems efficiently due to its good local search criteria that performs exceptionally well for different problems and solutions.

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Acknowledgements

Special appreciative to the reviewer(s) for useful advice and comments. The authors greatly acknowledge the Research Management Centre, (UTM) for financial support through the Research University Grant (GUP) Vot No. Q.J130000.2528.16H81.

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  1. Applied Industrial Analytics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia

    N. M. Hatta, Azlan Mohd Zain, Roselina Sallehuddin & Yusliza Yusoff

  2. School of Manufacturing Engineering, Universiti Malaysia Perlis, 02600, Arau, Malaysia

    Z. Shayfull

  3. Green Design and Manufacture Research Group, Center of Excellence Geopolymer and Green Technology (CEGeoGTech), Universiti Malaysia Perlis, 0100, Kangar, Malaysia

    Z. Shayfull

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  1. N. M. Hatta
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Hatta, N.M., Zain, A.M., Sallehuddin, R. et al. Recent studies on optimisation method of Grey Wolf Optimiser (GWO): a review (2014–2017). Artif Intell Rev 52, 2651–2683 (2019). https://doi.org/10.1007/s10462-018-9634-2

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