AN IMPROVED DIFFERENTIAL EVOLUTION OPTIMIZATION ALGORITHM

The most important challenge in some optimization problems is CPU time. The performance and convergence speed of optimization algorithms have the most important effect on CPU time. In this work, the original differential evolution algorithm has been modified in order to increase the convergence speed of the optimization algorithm. These modifications can increase the probability of selection of evolved individuals and consequently can increase the performance of differential evolution algorithm. The performance characteristics of the modified differential evolution algorithm have been compared with those of the original differential evolution algorithm. Results obtained show that the modified differential evolution algorithm is able to converge and find the optimum point faster compared to the original algorithm. These modifications have been applied not only to different versions of differential evolution algorithm but also to adaptive differential evolution algorithm in order to investigate the convergence speed of these modified methods. According to the results obtained, the proposed modifications were able to improve the performance of the different versions of differential evolution and even adaptive differential evolution algorithms. Finally, the proposed modifications have been tested on several optimization problems to evaluate the effect of these modifications on the convergence speed of the algorithm in different applications. In all cases, the modified differential evolution algorithm is faster than the original algorithm. The results of the proposed algorithm have been verified with analytical results.

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