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

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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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Acknowledgements

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).

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Authors and Affiliations

  1. Department of Management Science, College of Management, Shenzhen University, Shenzhen, China

    Xianghua Chu, Da Gao, Jiansheng Chen & Quande Qin

  2. Institute of Big Data Intelligent Management and Decision, Shenzhen University, Shenzhen, China

    Xianghua Chu

  3. School of Economics and Management, University of Science and Technology Beijing, Beijing, China

    Jianshuang Cui

  4. School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, USA

    Can Cui

  5. Institute of Physical Internet, School of Electrical and Information Engineering, Jinan University, Guangzhou, China

    Su Xiu Xu

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  1. Xianghua Chu
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Correspondence to Quande Qin.

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Appendices

Appendix 1: Benchmark functions

See Table 11.

Table 11 Features of benchmark functions
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Appendix 2: Algorithm pseudo-code of DSA

figure a

Appendix 3: Algorithm pseudo-code of ADSA

figure b

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Chu, X., Gao, D., Chen, J. et al. Adaptive differential search algorithm with multi-strategies for global optimization problems. Neural Comput & Applic 31, 8423–8440 (2019). https://doi.org/10.1007/s00521-019-04538-6

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