<![CDATA[Metaheuristic algorithms are widely applied to complex optimization problems, yet many suffer from premature convergence or slow search efficiency. To report these limitations, this paper proposes a new hybrid algorithm, Grey Wolf Optimizer–Zebra Optimization Algorithm (GWO–ZOA). The algorithm integrates the exploitation ability of the Grey Wolf Optimizer with the exploration capability of the Zebra Optimization Algorithm in a sequential framework, thereby enhancing both convergence accuracy and global search ability. The performance of GWO–ZOA is first evaluated on 23 standard benchmark functions, where it demonstrates competitive results in both unimodal and multimodal landscapes. Further validation is carried out on the CEC2017 and CEC2020 benchmark suites, confirming the hybrid’s robustness across higher-dimensional and more challenging composite problems. In all three benchmark categories, the Friedman statistical test ranks GWO–ZOA first among the compared algorithms, highlighting its superior overall performance. Finally, the algorithm is applied to two real-world engineering design problems, where it consistently achieves high-quality feasible solutions and demonstrates practical effectiveness. These results confirm that the proposed GWO–ZOA algorithm is both robust and reliable for solving diverse and complex optimization tasks.]]>
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