Enhancing Adaptive Differential Evolution Algorithms with Rank-Based Mutation Adaptation

Differential evolution has many mutation strategies which are problem dependent. Some Adaptive Differential Evolution techniques have been proposed tackling this problem. But therein all individuals are treated equally without taking into account how good these solutions are. In this paper, a new method called Ranked-based Mutation Adaptation (RAM) is proposed, which takes into consideration the ranking of an individual in the whole population. This method will assign different probabilities of choosing different mutation strategies to different groups in which the population is divided. RAM has been integrated into several well-known adaptive differential evolution algorithms and its performance has been tested on the benchmark suit proposed in CEC2014. The experimental results shows the use of RAM can produce generally better quality solutions than the original adaptive algorithms.