Multistrategy improved zebra optimization algorithm

With the rapid development of Industry 4.0 and smart factory systems, increasingly complex optimization problems have placed higher demands on the convergence speed and global search capabilities of optimization algorithms. To overcome the limitations of the original Zebra Optimization Algorithm (ZOA), such as low solution accuracy and slow convergence, this study proposes a multi-strategy improved ZOA, termed MIZOA. The traditional ZOA suffers from sluggish convergence in the early stages and a tendency to become trapped in local optima in later iterations, limiting its effectiveness in complex industrial optimization scenarios. To address these issues, MIZOA integrates multiple population search strategies, genetic algorithm-based mutation operations, the Metropolis acceptance criterion, and selective aggregation strategies into the ZOA framework. These enhancements significantly improve the algorithm’s global exploration and local exploitation capabilities. By maintaining population diversity, MIZOA greatl y enhances search efficiency in complex multimodal optimization problems, thereby improving both robustness and solution precision. Experimental results show that MIZOA outperforms seven widely adopted swarm intelligence algorithms across eight benchmark functions, achieving average improvements of 97.7% in convergence accuracy and 89.1% in stability compared with the original ZOA. This study introduces novel and efficient optimization methodologies for Industry 4.0 and smart factory systems within the context of intelligent manufacturing, showcasing strong potential for engineering applications and considerable value for practical implementation and promotion.