Mayfly Optimization Algorithm: A Nature-Inspired Approach with Advanced Termination for Efficient Minimization

The Mayfly Optimization Algorithm (MO), inspired by the swarming and mating behavior of adult mayflies, offers a promising approach for tackling minimization problems. However, traditional MO can suffer from unnecessary iterations and exploration of suboptimal regions in the search space. This paper addresses these limitations by introducing two key advancements: greedy selection and auto-termination. Greedy selection ensures the algorithm prioritizes solutions with lower fitness values during position updates, guiding the search towards the minimum more effectively. Auto-termination monitors fitness function changes over a defined window and terminates the algorithm if improvement stagnates, reducing computation time. We evaluate the performance of the enhanced MO algorithm on various benchmark minimization functions. The results demonstrate that the incorporation of greedy selection and auto-termination significantly improves the convergence speed and efficiency of MO compared to the traditional approach. This paves the way for MO to be a more competitive and efficient tool for tackling various real-world minimization problems.