Asynchronous differential evolution with selfadaptive parameter control for global numerical optimization

Abstract

In this paper, we propose an extended self-adaptive differential evolution algorithm, called A-jDE. A-jDE algorithm is based on jDE algorithm with the asynchronous method. jDE algorithm is one of the popular DE variants, which shows robust optimization performance on various problems. However, jDE algorithm uses a slow mutation strategy so that its convergence speed is slow compared to several state-of-the-art DE algorithms. The asynchronous method is one of the recently investigated approaches that if it finds a better solution, the solution is included in the current population immediately so it can be served as a donor individual. Therefore, it can improve the convergence speed significantly. We evaluated the optimization performance of A-jDE algorithm in 13 scalable benchmark problems on 30 and 100 dimensions. Our experiments prove that incorporating jDE algorithm with the asynchronous method can improve the optimization performance in not only a unimodal benchmark problem but also multimodal benchmark problem significantly.