A comprehensive survey on the ant lion optimiser, variants and applications

ABSTRACT

The Ant Lion Optimiser (ALO) algorithm is one of the recent nature-inspired algorithms developed by Mirjalili in 2015. This article presents a comprehensive survey on the ALO algorithm that mimics the hunting mechanism of antlions in nature, besides the overview of ALO applications to solve optimisation problems in different fields. The ALO algorithm is simple to use, flexible and scalable and has an adequate balance of exploration and exploitation, making it suitable to find best solution and attaining good convergence. Therefore, the ALO algorithm has gained significant interest among researchers across various domains in a short span. Thus, an updated review of research of the ALO algorithm has been recapitulated involving recent variants, modification and hybridisation that further improved ALO algorithm performance. Initially, introduction of the ALO algorithm is presented covering inspiration and mathematical formulations based on the conceptual framework of antlion behaviour. Furthermore, the optimisation procedure is discussed including the use of all operators involved in five main steps of the algorithm. Finally, various application domains are examined for the ALO algorithm and statistical analysis is performed using Friedman and Wilcoxon rank sum tests. Based on the analysis and investigations performed on this algorithm, it has not only realised a great success in solving numerical optimisation problems but also been implemented in various application areas such as global optimisation, renewable energy, feature selection, power load balancing and image segmentation. Finally, this article ends with conclusion summary and providing several future directions and applications of the ALO algorithm.

KEYWORDS:

• Ant lion optimiser
• ALO
• optimisation
• review
• metaheuristics
• variants

Disclosure statement

No potential conflict of interest was reported by the author(s).

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