Multi-strategy fusion improved adaptive mayfly algorithm

This paper proposes the multi-strategy fusion improved adaptive mayfly algorithm (MIMA), which addresses the shortcomings of the improved mayfly algorithm, including its low adaptive ability, minimal population diversity, and poor global search performance. Firstly, Sin chaos mapping was used to initialize the mayfly population so that the population could be uniformly distributed in the solution space, which improved the initial population quality and enhanced the global search ability. Second, in order to improve the local optimal escape ability, control population density, and boost population diversity, individuals in the population were exposed to Gaussian variation and Tent chaos mapping. Then, the incomplete gamma function was introduced to reconstruct the adaptive dynamic adjustment of gravity coefficients to establish a better balance between global search and local exploitation ability, which in turn improved the convergence accuracy of the algorithm and facilitated the potential of global search to find the optimal solution. Finally, the random opposition-based learning (ROBL) strategy was adopted to enhance the global search ability, improve the convergence speed and enhance the stability. To demonstrate the efficacy and dependability of the four improvement measures, the algorithms were compared using the classical test function set and their optimization effect was examined using the Wilcoxon rank sum test. The experimental results show that compared with other algorithms, the MIMA has better searching accuracy, convergence speed, and stability.