Performance of multi-strategy optimized mayfly optimization algorithm for location selection of university reimbursement form submission terminals

The Mayfly Optimization Algorithm(MOA) suffers from inherent drawbacks, such as susceptibility to local optima and slow convergence speed. In this study, an improved mayfly algorithm integrated with multiple strategies is proposed. Firstly, a Logistic chaotic map is adopted to generate a uniformly distributed initial population, which replaces the traditional random initialization method and enhances population diversity. Secondly, a nonlinear adaptive weight factor is introduced to improve the global search capability. Thirdly, a Lévy flight strategy is incorporated to assist the algorithm in escaping local optima and further augment population diversity. Finally, mechanisms derived from Particle Swarm Optimization (PSO) are integrated into the algorithm. This integration strengthens the directionality of the algorithm, balances global exploration and local exploitation, accelerates convergence, and directly mitigates the core flaw of premature convergence in the original MOA. The performance of the improved algorithm was evaluated using 12 benchmark functions. Comparative experiments with three other state-of-the-art algorithms demonstrate that the proposed algorithm achieves higher optimization accuracy, faster convergence rate, and enhanced ability to escape local optima. Furthermore, the applicability of the proposed algorithm to practical engineering problems was validated through a case study on the optimal placement of university reimbursement form submission terminals.