Parallel Differential Evolution Applied to Interleaving Generation with Precedence Evaluation of Tentative Solutions

This paper proposes a method to improve the CPU utilization of parallel differential evolution (PDE) by incorporating the interleaving generation mechanism. Previous research proposed the interleaving generation evolutionary algorithm (IGEA) and its improved variants (iIGEA). IGEA reduces the computation time by generating new offspring, which parents have been determined even when all individuals have not evaluated. However, the previous research only used a simple EA method, which is not suitable for practical use. For this issue, this paper explores the applicability of IGEA and iIGEA to practical EA methods. In particular, we choose differential evolution (DE), which is widely used in real-world applications, and propose IGDE and its improved variant, iIGDE. We conduct experiments to investigate the effectiveness of IGDE with several features of the evaluation time on a simulated parallel computing environment. The experimental results reveal that the IGDE variants have higher CPU utilization than a simple PDE and reduce the computation time required for optimization. Besides, iIGDE outperforms the original IGDE for all features of the evaluation time.