A Novel Two-Layer Hierarchical Differential Evolution Algorithm for Global Optimization

This paper proposes a novel Two-layer Hierarchical differential evolution (THDE) algorithm to improve the search ability of differential evolution (DE) algorithm. Individuals are separated into bottom layer and top layer. In the bottom layer, individuals are divided into several groups. Modified DE/current-best/1/bin strategy is conducted to produce offspring, where the best individual comes from top layer. In the top layer, modified DE/rand/1/bin strategy is used to update individuals. A set of famous benchmark functions has been used to test and evaluate the performance of the proposed THDE. The experimental results show that the proposed algorithm is better than DE/current-best/1/bin and DE/rand/1/bin and better than or at least comparable to the self-adaptive DE (JDE) and intersect mutation differential evolution algorithm (IMDE) for most functions.