A Survey of Recent Variants and Applications of Antlion Optimizer

A Survey of Recent Variants and Applications of Antlion Optimizer

Shail Dinkar, Kusum Deep
Copyright: © 2021 |Pages: 26
DOI: 10.4018/IJEOE.2021040103
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This work proposes a review of a recently developed swarm intelligence-based metaheuristic algorithm called Antlion Optimizer (ALO), its variants, and applications. The suitable blending of a random walk with an adaptive shrinking of hypersphere radius makes this algorithm more effective and impressive over other recent optimization algorithms. This paper elaborates on the recent variants of ALO by reviewing the concerned publications. It also summarized the applications of ALO for solving real-world complex optimization problems of a wide variety of areas. So, this paper comprises of summarized review of various recently published ALO papers. Firstly, the natural phenomena of ALO and the working principle of its various operators are described. Then the recently developed variants of ALO are described in detail depicting in various categories. The real-world applications using ALO and its variants are also described under global optimization, power and system engineering, electronics and communication engineering, machine learning, environmental engineering, and networking.
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1. Introduction

Solving real-world complex problems is becoming a big challenge due to increased complexities in terms of dependencies among variables, nonlinear constraints, large data and enhanced search space (Gogna and Tayal,2013). So, there is a necessity to develop such optimization algorithms that provide a simple and easy way to employ computational intelligence so that these techniques promise to solve complex problems in real-time (Engelbrecht,2007). Nature-inspired optimization techniques are categorized as algorithms for intelligent computing and optimization (Pandian et al.,2019 and Pandian et al.,2012). Most of them are population-based algorithms and follow iterative process during the evolution and put under evolutionary algorithms (EA). These are motivated by natural evolution and selection of any biological species (Faris et al.,2017). Starting with initial random populations, it is evolved iteratively by using a set of evolutionary operators to make an algorithm capable of searching the maximum region of the search space to find the global optima.

Based on the natural behaviour of species, the EAs can be classified in various categories. Inheritance and survival of the fittest are two principles of Darwin’s theory which are employed in Genetic algorithm (Holland,1975) and Differential evolution (Storn & Price,1997; Das & Suganthan,2011). The other algorithms lie in the same category such as Genetic programming (Koza,1992), Evolutionary strategies (Hansen and Kern, 2004), Biogeography-based optimization (Simon,2008) and population-based incremental learning (Höhfeld and Rudolph,1997).

Swarm intelligence (SI) is another category of optimization technique which is inspired with the coordination of individuals from natural and artificial systems using self-organization and decentralized control. These individuals or swarms communicate intelligently within the search space to fulfil their natural needs such as food hunting. The algorithms based on swarm intelligence imitate the natural communities and their behaviours such as flocking of birds, colonies of ants, bacterial growth and animal herds (Faris et al.,2017. Most of these algorithms are inspired by the life cycle and their food hunting behaviour in nature. These algorithms include Particle swarm optimization which is based on flocking of birds(Kennedy and Eberhart,1995), Ant colony optimization which imitates the movement of ants and their social behaviour(Dorigo and Di Caro,1999) and Artificial bee colony, conceptualized on the nature of bees and their searching of food sources(Karaboga,2005).Grey wolf optimizer(Mirjalili et al.,2014) and Whale optimization (Mirjalili and Lewis,2016) are recently proposed swarm intelligence based algorithms which imitate the unique hunting behaviour of swarms. Glowworm swarm optimization (Krishnanand and Ghose, 2006) is another such swarm-based optimization algorithm. Some other algorithms are Krill herd optimization (Gandomi and Alavi,2012), Firefly algorithm (Yang,2009), Multi-verse optimizer (Mirjalili et al.,2016), Sine cosine algorithm (Mirjalili,2016), Dragonfly algorithm (Mirjalili,2016), Moth flame optimization algorithm (Mirjalili,2015).

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