An improved differential evolution algorithm with novel mutation strategy

As a modern Evolutionary Algorithm, Differential Evolution (DE) is usually criticized for its slow convergence when compared to Particle Swarm Optimization (PSO) on the PSO´s benchmark functions. In this paper, by combing the merits of PSO and DE, we first present a new hybrid DE algorithm to accelerate its convergence speed. Then a novel mutation strategy with local and global search operators is proposed for balancing the exploration ability and the convergence rate of the improved DE. The new algorithm is applied to a set of benchmark test problems and compared with basic PSO and DE algorithms and their variants. The experimental results show the new algorithm shows better achievements on most test problems.