Introduction: The Pelican Optimization Algorithm (POA) is a meta-heuristic optimization algorithm, distinguished by its excellent ability to balance global exploration and local exploitation. This unique advantage enables it to effectively tackle complex multi-objective and multi-constraint optimization problems in mechanical design, where traditional algorithms often struggle with precision and efficiency. Thus, exploring POA’s application value in this field is of great significance for advancing mechanical design optimization.

Methods: This study applies the POA to mechanical design parameter optimization, taking advantage of its bionic mechanism that simulates pelicans’ natural hunting behavior. To verify its performance, POA is employed to solve three mechanical design optimization tasks: minimizing the self-weight of tension/compression springs, reducing the volume of rolling bearings, and lowering the manufacturing cost of reducers, with parameter optimization conducted by leveraging POA’s balanced exploration–exploitation capability.

Results: Comparative experimental tests show that the mechanical design solutions obtained by POA outperform those generated by other conventional meta-heuristic algorithms. Specifically, in terms of core objective function values, including the self-weight of springs, volume of rolling bearings, and production cost of reducers, POA achieves more optimal results, demonstrating its superior optimization performance.

Conclusions: The application of POA effectively enhances the precision and efficiency of mechanical design parameter optimization. This study confirms the feasibility, superiority, and practical applicability of POA in solving engineering optimization problems, providing a reliable new optimization tool for related mechanical design scenarios.