On the improvement of Differential Evolution for global optimization

Differential Evolution (DE) is a population-based search algorithm, which has shown good search abilities in many optimization problems. In this paper, we propose a novel DE algorithm, called IDE, to improve the performance of DE. In order to verify the performance of the proposed approach, we test IDE on eight well-known benchmark problems. The comparison results among IDE and two other improved versions of DE show that IDE outperforms them on majority of test problems.