Baboon optimization algorithm: A novel nature-inspired metaheuristic algorithm for optimization problems

This paper proposes the Baboon Optimization Algorithm (BOA), a novel metaheuristic inspired by the hierarchical social structure, foraging strategies, and stress response mechanisms of baboon populations. BOA divides the population into leader, adult, and juvenile layers, enabling a dynamic balance between global exploration and local exploitation through adaptive foraging and stress-induced perturbation mechanisms. The algorithm is evaluated on 23 classical test functions, CEC-2020, and CEC-2022 test suites. On unimodal functions, BOA consistently achieves strong exploitation capability. It achieves the best results on five out of ten CEC-2020 functions and nine out of twelve CEC-2022 functions. Statistical analyses confirm that BOA significantly outperforms ten compared algorithms on most test functions. However, BOA exhibits limitations in computational efficiency, with total running time exceeding that of five algorithms. Additionally, BOA does not achieve the best results on all functions, consistent with the No Free Lunch theorem. Applications to four engineering design problems and traveling salesman problems further demonstrate its practical utility. The source code of BOA is publicly available at https://drive.mathworks.com/sharing/38ce7c40-ffef-4278-8685-1172842b986b . • A novel metaheuristic algorithm named BOA is proposed. • BOA and 10 algorithms are compared. • BOA is evaluated using test functions and four engineering design problems.