Neural Computing and Applications

, Volume 31, Issue 12, pp 8423–8440 | Cite as

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

  • Xianghua Chu
  • Da Gao
  • Jiansheng Chen
  • Jianshuang Cui
  • Can Cui
  • Su Xiu Xu
  • Quande QinEmail author
Original Article


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.


Swarm intelligence Differential search algorithm Gradient search Lévy flight 



This work was partially supported by the National Natural Science Foundation of China (Grant No. 71971142 and 71871146), the Major Research plan of the National Natural Science Foundation of China (No. 91846301), the Major Project for National Natural Science Foundation of China (Grant No. 71790615), and the Natural Science Foundation of Guangdong Province (Grant No. 2016A030310067).

Compliance with ethical standards

Conflict of interest

No conflict of interest exists in the submission of this manuscript. I would like to declare on behalf of my co-authors that this manuscript is the authors’ original work and has not been published nor has it been submitted simultaneously elsewhere.


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Management Science, College of ManagementShenzhen UniversityShenzhenChina
  2. 2.Institute of Big Data Intelligent Management and DecisionShenzhen UniversityShenzhenChina
  3. 3.School of Economics and ManagementUniversity of Science and Technology BeijingBeijingChina
  4. 4.School of Computing, Informatics, and Decision Systems EngineeringArizona State UniversityTempeUSA
  5. 5.Institute of Physical Internet, School of Electrical and Information EngineeringJinan UniversityGuangzhouChina

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