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Neural Computing and Applications
Published in: Neural Computing and Applications 12/2019

22-10-2019 | Original Article

Adaptive differential search algorithm with multi-strategies for global optimization problems

Authors: Xianghua Chu, Da Gao, Jiansheng Chen, Jianshuang Cui, Can Cui, Su Xiu Xu, Quande Qin

Published in: Neural Computing and Applications | Issue 12/2019

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Abstract

Differential search (DSA) is a recently proposed evolutionary algorithm mimicking the Brownian motion-like random movement existing in living beings. Though it has displayed promise for global optimization, the original DSA suffers from relatively poor search capability, especially for exploitation. In this study, an augmented DSA (ADSA) is proposed by integrating memetic framework with multiple strategies. In ADSA, a sub-gradient strategy is combined to improve local exploitation, and the dynamic Lévy flight technique is developed to strengthen the global exploration. Moreover, a mutation operator based on differential search is used to increase swarm diversity. An intelligent selection method is implemented to adaptively adjust the strategies based on historical performance. To fully benchmark the proposed algorithm, 35 test functions of various properties in 30-D and 100-D are adopted in numerical experiments. Seven canonical optimization algorithms are involved for experimental comparison. In addition, two real-world problems are also tested to verify ADSA’s practical applicability. Numerical results reveal the efficiency and effectiveness of ADSA.
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Appendix
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Metadata
Title
Adaptive differential search algorithm with multi-strategies for global optimization problems
Authors
Xianghua Chu
Da Gao
Jiansheng Chen
Jianshuang Cui
Can Cui
Su Xiu Xu
Quande Qin
Publication date
22-10-2019
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 12/2019
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-019-04538-6

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