Improved mayfly optimization algorithm based on anti-attraction velocity update mechanism

To address the problem that the mayfly optimization algorithm (MA) has a slow convergence speed in the early stage and not high accuracy in the later stage of the search, a modified mayfly optimization algorithm (MMOA) based on the anti-attraction speed update mechanism is proposed. Firstly, an improved Tent chaotic sequence is used to initialize the mayfly population, which makes the mayfly distribution more uniform and improves the diversity of the population. Secondly, in order to enhance the algorithm’s convergence performance, an anti-attraction speed update mechanism is presented to direct the mayfly speed update depending on the properties of the MA. Finally, the dimension-by-dimension centroid opposition-based learning strategy is performed on the global best mayfly, which reduces the interference between dimensions, helps the algorithm jump out of the local optimum and accelerates the convergence. Based on a comparison of simulation experiments using 12 conventional test functions and a few CEC2017 test functions, the findings indicate that MMOA clearly outperforms algorithms such as grey wolf optimizer (GWO) and MA in terms of convergence speed, stability, and optimization accuracy. © 2024 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.