Goat Optimization Algorithm: A Novel Bio-Inspired Metaheuristic for Global Optimization

This paper introduces the Goat Optimization Algorithm (GOA), a novel bio-inspired metaheuristic inspired by the adaptive behaviors of goats. Drawing from their foraging strategies, movement patterns, and ability to evade parasites, GOA is designed to balance exploration and exploitation effectively. The algorithm incorporates three key mechanisms: an adaptive foraging strategy for global search, a directed movement approach for refining solutions, and a jump mechanism to escape local optima. Additionally, a solution filtering process enhances robustness by maintaining diversity within the population. GOA's performance is evaluated against well-established metaheuristics, including Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), Whale Optimization Algorithm (WOA), and Artificial Bee Colony (ABC). Comparative results demonstrate GOA’s superior convergence speed, enhanced global search efficiency, and improved solution accuracy. The statistical significance of these improvements is validated through the Wilcoxon rank-sum test. Despite its effectiveness, GOA has some challenges, including computational complexity and sensitivity to parameter settings, which leave room for further optimization. Future research will explore adaptive parameter tuning, hybridization with other metaheuristics, and real-world applications in supply chain management, bioinformatics, and energy optimization. The findings indicate that GOA offers a promising advancement in bio-inspired optimization techniques.