摘要：Differential evolution (DE) is a simple yet powerful evolutionary algorithm for global numerical optimization. In this paper, we propose a novel hybrid DE variant to accelerate the convergence rate of the classical DE algorithm. The proposed algorithm is hybridized with a convex mutation. The convex mutation is able to utilize the information of the parents, and hence, provides faster convergence speed. Our proposal is referred to as Convex-DE. In order to verify our expectation, we test our approach on 13 widely used benchmark functions. The results indicate that our approach is better than the classical DE algorithm in terms of the convergence speed and the quality of final solution. Furthermore, the potential of our approach for real-world applications is evaluated on three real-world problems.
