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CCEO: cultural cognitive evolution optimization algorithm

  • Yongquan ZhouEmail author
  • Shaoling Zhang
  • Qifang Luo
  • Mohamed Abdel-Baset
Methodologies and Application
  • 12 Downloads

Abstract

Cognitive behavior is an indispensable factor in the history of human evolution; through cognitive behavior, human development has evolved from the Stone Age to the present high-tech information era. By simulating the process of cultural evolution, a cultural algorithm can reflect the evolutionary process of society accurately, which can speed up the process of solving problems. In this paper, cognitive behavior evolution and a cultural algorithm are combined to form the cultural cognitive evolution optimization (CCEO) algorithm. A cultural algorithm with the double-layer structure of the belief space and population space is transformed into a three-layer evolution mechanism in a CCEO so that the belief space improves on the next layer of careful guidance of the evolutionary process. Twenty-three benchmark test functions and two engineering problems were used to test the CCEO algorithm, and the results demonstrate that the culture cognitive evolution algorithm has a high convergence speed and calculation precision.

Keywords

Cognitive behavior Cultural evolution Cultural cognitive evolution algorithm Benchmark functions Engineering optimization 

Notes

Acknowledgements

This work was supported by the National Science Foundation of China under Grant Nos. 61563008 and 61463007 and the Project of Guangxi Nationalities Science Foundation under Grant No. 2018GXNSFAA138146. We thank Maxine Garcia, PhD, from Liwen Bianji, Edanz Group China (www.liwenbianji.cn/ac), for editing the English text of a draft of this manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Yongquan Zhou
    • 1
    • 2
    Email author
  • Shaoling Zhang
    • 1
  • Qifang Luo
    • 1
    • 2
  • Mohamed Abdel-Baset
    • 3
  1. 1.College of Information Science and EngineeringGuangxi University for NationalitiesNanningChina
  2. 2.Key Laboratory of Guangxi High Schools Complex System and Computational IntelligenceNanningChina
  3. 3.Faculty of Computers and InformaticsZagazig UniversityZagazigEgypt

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