Enhanced Differential Evolution using center-based sampling

The classical Differential Evolution (DE) has showed to perform efficiently in solving both benchmark functions and real-world problems. However, DE, similar to other evolutionary algorithms deteriorate in performance during solving high-dimensional problems. Opposition-based Differential Evolution (ODE) was introduced and, in general, has shown better performance comparing to classical DE for solving large-scale problems. In this paper, we propose an enhancement to ODE in order to improve its ability to solve large-scale problems more effectively. The proposed modified version of ODE is called Center-Based Differential Evolution (CDE) which utilizes the exact algorithm of ODE except replacing of opposite points with center-based individuals. This paper compares DE and ODE with the proposed algorithm, CDE. Furthermore, CDE with dynamic range (CDE_d) will be compared to CDE with fixed range (CDE_f). Experimental verifications are conducted on seven well-known shifted large-scale benchmark functions for dimensions of 100 and 500, including detailed parameter analysis for CDE. The shifted version of the functions ensures there is no bias towards the center of search space, in favor of CDE algorithm. The results clearly show that the CDE outperforms DE and ODE during solving large-scale problems, and also clarifies the superiority of CDE_d to CDE_f.