Adaptive Caprine-Inspired Optimization: A Superior Metaheuristic to Particle Swarm Optimization in Complex Search Spaces - Evuiroro, Edirin Judith; Apanapudor, Joshua Sarduana (Faculty of Natural and Applied Sciences Journal of Applied and Physical Sciences, 2025)
This paper presents the Adaptive Caprine-Inspired Optimization (ACIO) algorithm, a novel bioinspired metaheuristic grounded in the behavioural ecology of goats. Unlike earlier caprine-based models, ACIO integrates four distinct strategies: stochastic exploration, directional refinement, probabilistic jumping, and diversity preservation, into a unified framework to enhance convergence speed and maintain population diversity. Implemented in MATLAB and tested against Particle Swarm Optimization (PSO) on five benchmark functions, ACIO achieves a 50% faster convergence rate and a 78% reduction in mean fitness on the Rastrigin function. Statistical validation using the Wilcoxon rank-sum test (p < 0.05) confirms the algorithm's robustness.