Soft Computing

, Volume 23, Issue 21, pp 11077–11105 | Cite as

Memetic frog leaping algorithm for global optimization

  • Deyu TangEmail author
  • Zhen Liu
  • Jin Yang
  • Jie Zhao
Methodologies and Application


Developing an effective memetic algorithm that integrates leaning units and achieves the synergistic coordination between exploration and exploitation is a difficult task. In this paper, we propose a memetic algorithm based on the shuffled frog leaping algorithm, which is fulfilled by three units: memetic diffusion component, memetic evolutionary component and memetic learning component. Memetic diffusion component enhances the diversity of population by the shuffled process. Memetic evolutionary component accomplishes the exploitation task by integrating the frog leaping rule, geometric center, Newton’s gravitational force-based gravitational center and Lévy flight operator. Memetic learning component improves the exploration by an adaptive learning rule based on the individual selection and the dimension selection. In order to evaluate the effectiveness of the proposed algorithm, 30 benchmark functions and a real-world optimization problem are used to compare our algorithm against 13 well-known heuristic methods. The experimental results demonstrate that the performance of our algorithm is better than others for the continuous optimization problems.


Memetic algorithm Shuffled frog leaping algorithm Gravity search algorithm Lévy flight Continuous optimization 



The authors would like to thank the reviewers and editor for their very useful and constructive comments that helped to improve the quality of the paper. This work is supported by the Guang Dong Provincial Natural fund project, Drug-target interaction prediction method based on collaborative intelligent optimization (2016A030310300); the Natural Science Foundation of China under Grant (No. 61501128); NSFC, Research on reasoning of behavior trust for resisting collusive reputation attack (71401045); Guangdong province precise medicine and big data engineering technology research center for traditional Chinese medicine, Guang Dong Provincial Natural fund (2014A030313585, 2015A030310267, 2015A030310483). Major scientific research projects of Guangdong, Research of Behavioral Trust resisting collusion reputation attack based on implicit and explicit big behavior data analysis (2017WTSCX021). Philosophy and Social Sciences of Guangzhou ‘13th Five-Year’ program (2018GZGJ48).

Compliance with ethical standards

Conflict of interest

The author declares that there is no conflict of interest.

Ethical approval

The work of this article does not involve use of human participants or animals.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Medical Information and EngineeringGuangdong Pharmaceutical UniversityGuangzhouChina
  2. 2.School of Computer Science and EngineeringSouth China University of TechnologyGuangzhouChina
  3. 3.Department of Information Management Engineering, School of ManagementGuangdong University of TechnologyGuangzhouChina
  4. 4.Department of Computer ScienceAmerican UniversityWashingtonUSA

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