基于反吸引速度更新机制的改进蜉蝣算法

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

针对蜉蝣算法(MA)前期收敛速度较慢、后期寻优精度不高等问题,提出一种基于反吸引速度更新机制的改进蜉蝣算法(MMOA)。采用改进型Tent混沌序列初始化蜉蝣种群,使蜉蝣分布更加均匀,提升了种群的多样性;结合MA的特点,引入反吸引速度更新机制指导蜉蝣速度更新,平衡算法的全局搜索和局部寻优能力,进而提升算法的收敛性能;对全局最优蜉蝣进行逐维的重心反向学习变异,降低各维度间的干扰,帮助算法跳出局部最优并加速收敛。基于12个标准测试函数和部分CEC2017测试函数进行对比仿真实验,结果表明:MMOA较灰狼优化(GWO)算法、MA等算法在收敛速度、寻优精度和稳定性等方面都具有明显优势。

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.