Abstract：To get better solution of the differential evolution (DE) algorithm, the mutation strategy of DE is proposed and divided into two parts to reflect the changes of the target population trends and their random variation. Fractal mutation factor differential evolution (FMDE) algorithm is put forward and it consists of an additional mutation factor simulated by a different Hurst index fractal Brownian motion. FMDE is tested on 25 benchmark functions presented at 2005 IEEE congress on evolutionary computation. The optimization results of at least 10 benchmark functions are better than the results obtained by other differential evolution algorithms, and the rest of the test results are approximate. Experimental results show that FMDE significantly improves the accuracy and adaptability of the optimization.
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2015, Vol. 28 
