Abstract
A novel metaheuristic algorithm named the Blue-Eared Hedgehog Optimization (BEHO), inspired by the unique foraging and defensive behaviour of the blue-eared hedgehog is introduced in this study.Unlike conventional optimization methods, BEHO simulates the species' natural strategies-nocturnal cautious exploration, gradual environmental mapping, and protective retreat-into computational operators that effectively balance exploration and exploitation.The algorithm initializes a diverse population of candidate solutions, simulates hedgehog-inspired gradual movements for exploration, and employs defensive-inspired refinement for exploitation, ensuring robust convergence and preservation of high-quality solutions.BEHO's performance has been rigorously evaluated on 23 standard benchmark functions, including unimodal, high-dimensional multimodal, and fixed-dimensional multimodal problems, and compared with nine state-of-the-art metaheuristics, including MOA, WaOA, AOA, GWO, LSA, SWO, TLBO, BaOA, and WSO.Experimental results demonstrate that BEHO consistently achieves superior accuracy, stability, and convergence speed across all function categories.Its hedgehog-inspired mechanisms allow the algorithm to escape local optima, maintain population diversity, and achieve precise global solutions in complex and high-dimensional landscapes.The findings highlight BEHO as a highly effective and versatile optimization tool, providing a biologically grounded and computationally efficient framework for solving diverse complex problems.