An Improved Differential Evolution Algorithm with Novel Mutation Strategy
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Xin Shen, Dexuan Zou, Xin Zhang
Aiming at the defects of differential evolution, such as premature convergence, low accuracy and other shortcomings, an improved differential evolution algorithm with novel mutation strategy(NMSIDE) is presented. The novel mutation strategy is used to avoid being trapped into local minima for NMSIDE. If the evolution of the individual stagnates, the individual will rely on the current best individual to move closer to the global best individual. The mutation rate varies dynamically within the range of values, and the crossover rate is dynamically varied based on the number of iterations. NMSIDE is tested on 11 standard functions and compared with the other state-of-the-art algorithms. The experimental results show that NMSIDE has higher convergence precision, faster convergence speed and better robustness.
Differential evolution algorithm, fitness value, trapped solutions, dynamic adjustment.