A Novel Center-based Differential Evolution Algorithm

Differential Evolution (DE) algorithm has been shown notable performance in solving complex optimization problems. In recent years, some variants of the DE algorithm have been proposed based on the concept of center-based sampling strategy. To the best of our knowledge, the related papers employed center-based sampling for population initialization or as the base vector in mutation operator. In fact, they were operation-level approaches applied during the optimization process, and none of them was about proposing a population-level approach to utilize center-based sampling to accelerate convergence rate of algorithms. This paper proposes a novel center-based sampling scheme for the DE algorithm that utilizes center-based sampling as a member of the population. In our scheme, one candidate solution is the center of the best candidate solutions, while other individuals in the population behave similarly to the standard DE algorithm. The center-based candidate solution is not updated using standard operators and is set to the center in each iteration. To validate our scheme, we benchmark our algorithm on CEC-2017 benchmark functions with three dimensions of 30, 50, and 100. Also, we design some experiments to analyze the behavior of the proposed center-based scheme. Our experiments demonstrate a significant improvement of the proposed algorithm on the majority of benchmark functions.

I. Introduction

In recent years, many real-world problems have been reformulated as an optimization problem. In an optimization problem, an objective function should be maximized or minimized regarding x as a decision variable vector.