An Improved Differential Evolution with Efficient Parameters Adjustment

Due to the real-world optimization problems have grown ever more complex. Solution solving capability and efficiency of optimization algorithms are confronted with serious challenge. In this paper, an Efficient Parameters Adjustment is proposed for Differential Evolution to solve optimization problems. Also, a novel mutation strategy and linear parameter control are involved to enhance search ability of the algorithm. In the experiments, 14 benchmarks of CEC 2005 are adopted as comparison basis. Several recent DE works are taken into comparison with the proposed method. According to experimental results, it can be observed that the proposed method provides efficiency and robustness and solution solving capability, and superior in performance over other DE methods.