Blue-eared Hedgehog Optimization (BEHO): A Nature-inspired Metaheuristic for Robust and Efficient Global Optimization

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. © This article is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. License details: https://creativecommons.org/licenses/by-sa/4.0/