Adaptive Dynamic Disturbance Strategy for Differential Evolution Algorithm
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. First...
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doaj-00ab4a4bd5234bb1b9560399530e3bc22020-11-25T02:34:27ZengMDPI AGApplied Sciences2076-34172020-03-01106197210.3390/app10061972app10061972Adaptive Dynamic Disturbance Strategy for Differential Evolution AlgorithmTiejun Wang0Kaijun Wu1Tiaotiao Du2Xiaochun Cheng3School of Mathematics and Computer Science Institute, Northwest Minzu University, LanZhou 730030, ChinaSchool of Electronic and Information Engineering, LanZhou Jiao Tong University, LanZhou 730070, ChinaSchool of Electronic and Information Engineering, LanZhou Jiao Tong University, LanZhou 730070, ChinaDepartment of Computer Science, Middlesex University, London NW4 4BT, UKTo 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.https://www.mdpi.com/2076-3417/10/6/1972differential evolution algorithmadaptive dynamic disturbance strategygauss perturbationbenchmark functions |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Tiejun Wang Kaijun Wu Tiaotiao Du Xiaochun Cheng |
spellingShingle |
Tiejun Wang Kaijun Wu Tiaotiao Du Xiaochun Cheng Adaptive Dynamic Disturbance Strategy for Differential Evolution Algorithm Applied Sciences differential evolution algorithm adaptive dynamic disturbance strategy gauss perturbation benchmark functions |
author_facet |
Tiejun Wang Kaijun Wu Tiaotiao Du Xiaochun Cheng |
author_sort |
Tiejun Wang |
title |
Adaptive Dynamic Disturbance Strategy for Differential Evolution Algorithm |
title_short |
Adaptive Dynamic Disturbance Strategy for Differential Evolution Algorithm |
title_full |
Adaptive Dynamic Disturbance Strategy for Differential Evolution Algorithm |
title_fullStr |
Adaptive Dynamic Disturbance Strategy for Differential Evolution Algorithm |
title_full_unstemmed |
Adaptive Dynamic Disturbance Strategy for Differential Evolution Algorithm |
title_sort |
adaptive dynamic disturbance strategy for differential evolution algorithm |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2020-03-01 |
description |
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. |
topic |
differential evolution algorithm adaptive dynamic disturbance strategy gauss perturbation benchmark functions |
url |
https://www.mdpi.com/2076-3417/10/6/1972 |
work_keys_str_mv |
AT tiejunwang adaptivedynamicdisturbancestrategyfordifferentialevolutionalgorithm AT kaijunwu adaptivedynamicdisturbancestrategyfordifferentialevolutionalgorithm AT tiaotiaodu adaptivedynamicdisturbancestrategyfordifferentialevolutionalgorithm AT xiaochuncheng adaptivedynamicdisturbancestrategyfordifferentialevolutionalgorithm |
_version_ |
1724808828256518144 |