Chaotic Ant Lion Optimization Algorithm

Abstract

The Ant Lion Optimization (ALO) algorithm is a relative recent metaheuristic, designed on the concept of predator ant lions. The basic concepts revolve around hunting prey such as the random walk of ants, building traps, entrapment of ants in traps, catching preys, and re-building traps. The proposed algorithm focus on the stochasticity of the algorithm, with the embedding of chaos maps as pseudo-random number generation. The uniqueness of this approach is to evaluate the behavior of ALO, which employs minimal tuning parameters, and to observe the effectiveness of chaotic systems in mostly self-tuning algorithms. The experimentations was conducted on standard benchmark unimodal and multimodal problems and the results compared with the canonical version of ALO and other published algorithms. Based on the results comparisons, the Chaotic Ant Lion Optimization (CALO) performed significantly better than ALO and most compared algorithms.