An adaptive stochastic ranking-based tournament selection method for differential evolution

The selection method of individuals is a crucial step in the mutation operator of differential evolution (DE). Typically, methods that select individuals with better fitness values are used to increase the exploitation ability of the algorithm. However, some researches have shown that incorporating distribution information of target space to measure diversity can improve the exploration ability of the algorithm. With this concept in mind, this paper presents an innovative approach called the adaptive stochastically ranking-based tournament selection method (ASR-TS). ASR-TS initially uses tournament selection and subsequently stochastically ranks selected individuals based on fitness and diversity measurements, leading to the determination of the tournament's winner. Furthermore, the stochastic ranking parameter is adaptively set based on the success rate of the previous generation to strike a balance between the exploitation and exploration abilities of the algorithm. The proposed ASR-TS method was tested on CEC 2013 benchmark functions in several original and improved DEs. To further validate the effectiveness of this method, the ASR-TS method was also tested on CEC 2022 benchmark functions as well as real-world problems. The experimental results demonstrate that the proposed ASR-TS method outperformed various other methods by a significant margin, which proves its efficiency and effectiveness in balancing exploration and exploitation.