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Open AccessArticle

Adaptive Dynamic Disturbance Strategy for Differential Evolution Algorithm

1
School of Mathematics and Computer Science Institute, Northwest Minzu University, LanZhou 730030, China
2
School of Electronic and Information Engineering, LanZhou Jiao Tong University, LanZhou 730070, China
3
Department of Computer Science, Middlesex University, London NW4 4BT, UK
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2020, 10(6), 1972; https://doi.org/10.3390/app10061972
Received: 22 February 2020 / Revised: 7 March 2020 / Accepted: 9 March 2020 / Published: 13 March 2020
To overcome the problems of slow convergence speed, premature convergence leading to local optimization and parameter constraints when solving high-dimensional multi-modal optimization problems, an adaptive dynamic disturbance strategy for differential evolution algorithm (ADDSDE) is proposed. Firstly, this entails using the chaos mapping strategy to initialize the population to increase population diversity, and secondly, a new weighted mutation operator is designed to weigh and combinemutation strategies of the standard differential evolution (DE). The scaling factor and crossover probability are adaptively adjusted to dynamically balance the global search ability and local exploration ability. Finally, a Gauss perturbation operator is introduced to generate a random disturbance variation, and to accelerate premature individuals to jump out of local optimization. The algorithm runs independently on five benchmark functions 20 times, and the results show that the ADDSDE algorithm has better global optimization search ability, faster convergence speed and higher accuracy and stability compared with other optimization algorithms, which provide assistance insolving high-dimensionaland complex problems in engineering and information science. View Full-Text
Keywords: differential evolution algorithm; adaptive dynamic disturbance strategy; Gauss perturbation; benchmark functions differential evolution algorithm; adaptive dynamic disturbance strategy; Gauss perturbation; benchmark functions
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MDPI and ACS Style

Wang, T.; Wu, K.; Du, T.; Cheng, X. Adaptive Dynamic Disturbance Strategy for Differential Evolution Algorithm. Appl. Sci. 2020, 10, 1972. https://doi.org/10.3390/app10061972

AMA Style

Wang T, Wu K, Du T, Cheng X. Adaptive Dynamic Disturbance Strategy for Differential Evolution Algorithm. Applied Sciences. 2020; 10(6):1972. https://doi.org/10.3390/app10061972

Chicago/Turabian Style

Wang, Tiejun; Wu, Kaijun; Du, Tiaotiao; Cheng, Xiaochun. 2020. "Adaptive Dynamic Disturbance Strategy for Differential Evolution Algorithm" Appl. Sci. 10, no. 6: 1972. https://doi.org/10.3390/app10061972

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