A Novel Differential Evolution with Uniform Design for Continuous Global Optimization

Abstract

Differential Evolution (DE) is a simple and efficient optimizer, especially for continuous global optimization. Over the last few decades, DE has often been employed for solving various engineering problems. At the same time, the DE structure has some limitations in the complicated problems. This fact has inspired many researchers to improve on DE by proposing modifications to the original algorithm. Population initialization is very important to the performance of differential evolution. A good initialization method can help in finding better solutions and improving convergence rate. In this paper, a uniform-differential evolution algorithm (UDE) is proposed. It incorporates uniform design initialization method into differential evolution to accelerate its convergence speed and improve the stability. UDE is compared with other four algorithms of Standard Differential Evolution (SDE), Orthogonal Differential Evolution (ODE), Opposition Based Differential Evolution(OBDE) and Chaos Differential Evolution(CDE). Experiments have been conducted on 23 benchmark problems of diverse complexities. The results indicate that our approach has the stronger ability and higher calculation accuracy to find better solutions than other four algorithms.