The improved mayfly optimization algorithm with opposition based learning rules

Abstract

Literal researches proved that not only the best candidates or the best historical trajectories would perform well in guiding the individuals in swarms to find the best solution, the worst and the worst historical trajectories would also work well in doing so. Such situations could be directly treated as pairs of oppositions, and satisfied the ancient Chinese Yin-Yang philosophy, where the opposition based learning (OBL) rule was directly derived from. In this paper, the improved mayfly optimization (MO) algorithm with OBL rules were proposed, simulation experiments were carried out and results showed that the improved MO algorithm with OBL rules would perform better than usual.