Global optimization using a combination of differential evolution and modified Powell method

Optimization is one of the most important problems in engineering applications. Substantial algorithms have been proposed to tackle these problems, such as differential evolution(DE), which has excellent global search capability and robustness. However, like most metaheuristics, differential evolution is computationally expensive and difficult to converge to the exact global optimum. The modified Powell(MP) method is a derivative-free local optimization algorithm with fast convergence speed but is prone to fall into the local optimum trap. Combining the advantages of these two algorithms, we propose a hybrid optimization algorithm, which is called the DEMP algorithm. The DEMP algorithm is compared to other three well-known optimization algorithms by a set of benchmark functions. The results show that DEMP is not only notably better than the traditional DE Algorithm, but also highly competitive for other metaheuristic algorithms.