SRDE: An Improved Differential Evolution Based on Stochastic Ranking

In this paper, we propose a methodology to improve the performance of the standard Differential Evolution (DE) in constraint optimization applications, in terms of accelerating its search speed, and improving the success rate. One critical mechanism embedded in the approach is applying Stochastic Ranking (SR) to rank the whole population of individuals with both objective value and constraint violation to be compared. The ranked population is then in a better shape to provide useful information e.g. direction to guide the search process. The performance of the proposed approach, which we call SRDE (Stochastic Ranking based Differential Evolution) is investigated and compared with standard DE with two variants of mutation strategies. The experimental results show that SRDE outperforms, or at least is comparable with standard DE in both variants in all the tested benchmark functions.