Multiscale Cooperative Differential Evolution Algorithm

A multiscale cooperative differential evolution algorithm is proposed to solve the problems of narrow search range at the early stage and slow convergence at the later stage in the performance of the traditional differential evolution algorithms. Firstly, the population structure of multipopulation...

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Bibliographic Details
Published in:Computational intelligence and neuroscience 2019, Vol.2019 (2019), p.1-17
Main Authors: Tang, Jianeng, Luo, Yanmin, Liu, Xiaofang, Fan, Yuling, Du, Yongzhao, Liu, Peizhong
Format: Article
Language:English
Subjects:
Adaptive systems
Algorithms
Convergence
Coordinates
Covariance
Evolution, Molecular
Evolutionary algorithms
Evolutionary computation
Genetic algorithms
Global optimization
Models, Genetic
Mutation
Optimization
Population
Population structure
Researchers
Surveys
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Summary:A multiscale cooperative differential evolution algorithm is proposed to solve the problems of narrow search range at the early stage and slow convergence at the later stage in the performance of the traditional differential evolution algorithms. Firstly, the population structure of multipopulation mechanism is adopted so that each subpopulation is combined with a corresponding mutation strategy to ensure the individual diversity during evolution. Then, the covariance learning among populations is developed to establish a suitable rotating coordinate system for cross operation. Meanwhile, an adaptive parameter adjustment strategy is introduced to balance the population survey and convergence. Finally, the proposed algorithm is tested on the CEC 2005 benchmark function and compared with other state-of-the-art evolutionary algorithms. The experiment results showed that the proposed algorithm has better performance in solving global optimization problems than other compared algorithms.
ISSN:1687-5265
1687-5273
DOI:10.1155/2019/5259129