Chaotic hunger games search optimization algorithm for global optimization and engineering problems

Abstract

Chaotic maps have the characteristics of ergodicity and non-repeatability. Owing to these properties, they provide fast convergence by effectively scanning the search space in a metaheuristic optimization algorithm. The Hunger Games Search (HGS) is a metaheuristic algorithm modeled on the foraging and hunger instincts of animals. In this study, ten chaotic maps have been applied to the classical HGS method. The control of two random values in the HGS algorithm has been carried out with chaotic maps in three alternative scenarios. Accordingly, it has been observed that Scenario 2 exhibits a more stable and faster convergence than other scenarios. The performance of the proposed chaotic HGS has been evaluated on CEC2017 and 23 classical benchmark problems. The proposed algorithm has been applied to real engineering problems for cantilever beam design, tension/compression, and speed reducer, and the results have been compared with classical HGS and state-of-art algorithms in the literature. It can be seen that chaotic HGS yields promising results compared to other studies in the literature. (C) 2021 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.