An Improved Differential Evolution Algorithm with Novel Mutation Strategy
Download as PDF
DOI: 10.25236/icmit.2017.17
Author(s)
Xin Shen, Dexuan Zou, Xin Zhang
Corresponding Author
Xin Shen
Abstract
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.
Keywords
Differential evolution algorithm, fitness value, trapped solutions, dynamic adjustment.