Bare bones mayfly optimization algorithm

摘要

Although individuals in the mayfly optimization (MO) swarms would have a chance to dance around the current positions, most of them would still perform exploration according to their best historical trajectories and the global best candidates, which was quite similar to the behavior performed by individuals in the particle swarm optimization (PSO) algorithm. Therefore, there would also exist the stochastic Gauss distribution among the exploration, exploitation for individuals in MO swarms. In this paper, the bare bones MO algorithm was proposed and simulation experiments were carried out. Results verified that the bare bones MO algorithm would perform better than before.