Chaotic spotted hyena optimizer for numerical problems

Abstract

AbstractSpotted hyena optimizer (SHO) is a new metaheuristic algorithm that replicates spotted hyenas' hunting and social behaviour. This article proposes novel SHO algorithm that utilizes chaotic maps for fine‐tuning of control parameters. The chaotic maps help SHO to enhance the searching behaviour and preclude the solution to get trapped in local optima. The authors suggest 10 novel chaotic versions of SHO. The algorithms' performance is evaluated using 29 standardized test functions. The finding reveal that some of the presented algorithms outperform the standard SHO in terms of search capability and solution quality. In addition, five competitive approaches are compared with the suggested algorithms. It is observed from the results that chaos‐based spotted hyena optimizer (CSHO) achieved approximately 3% improvement over SHO in terms of fitness value. CSHO is also tested using five engineering design problems. CSHO achieved a 3%–5% improvement over the existing metaheuristic algorithms in terms of optimal design cost. Experimental results reveal that CSHO outperforms the existing metaheuristic algorithms.