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

2025-03-04

AI Summary

To address the imbalance between exploration and exploitation and the tendency to converge prematurely to local optima in metaheuristic algorithms, this paper proposes the Goat Optimization Algorithm (GOA). Inspired by goats’ foraging behavior, parasite-avoidance strategies, and collective coordinated movement, GOA integrates three key mechanisms: adaptive foraging, elite-guided optimal-directional movement, and random jump-based escape. It further introduces a novel solution-filtering mechanism incorporating parasite avoidance to dynamically preserve population diversity and balance exploration versus exploitation. Evaluated on 23 standard benchmark functions, GOA demonstrates statistically significant improvements (Wilcoxon signed-rank test, *p* < 0.05) over PSO, GWO, GA, WOA, and ABC in convergence speed, global search capability, and solution accuracy. Results confirm GOA’s robustness in escaping local optima and its superior performance with statistical significance.

Technology Category

• Search and Optimization: Metareasoning and Metaheuristics
• Constraint Satisfaction and Optimization: Constraint Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

This paper presents the Goat Optimization Algorithm (GOA), a novel bio-inspired metaheuristic optimization technique inspired by goats' adaptive foraging, strategic movement, and parasite avoidance behaviors.GOA is designed to balance exploration and exploitation effectively by incorporating three key mechanisms, adaptive foraging for global search, movement toward the best solution for local refinement, and a jump strategy to escape local optima.A solution filtering mechanism is introduced to enhance robustness and maintain population diversity. The algorithm's performance is evaluated on standard unimodal and multimodal benchmark functions, demonstrating significant improvements over existing metaheuristics, including Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), Whale Optimization Algorithm (WOA), and Artificial Bee Colony (ABC). Comparative analysis highlights GOA's superior convergence rate, enhanced global search capability, and higher solution accuracy.A Wilcoxon rank-sum test confirms the statistical significance of GOA's exceptional performance. Despite its efficiency, computational complexity and parameter sensitivity remain areas for further optimization. Future research will focus on adaptive parameter tuning, hybridization with other metaheuristics, and real-world applications in supply chain management, bioinformatics, and energy optimization. The findings suggest that GOA is a promising advancement in bio-inspired optimization techniques.

Problem

• Develops Goat Optimization Algorithm for global optimization.
• Balances exploration and exploitation using adaptive foraging.
• Enhances robustness with solution filtering mechanism.

Innovation

• Bio-inspired Goat Optimization Algorithm (GOA)
• Balances exploration and exploitation effectively
• Introduces solution filtering for population diversity