CCEO: cultural cognitive evolution optimization algorithm

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


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.


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



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 (, 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.


  1. Akay B, Karaboga D (2012) Artifcial bee colony algorithm for large-scale problems and engineering design optimization. J Intell Manuf 23(4):1001–1014CrossRefGoogle Scholar
  2. Ali MZ, Awad NH, Reynolds RG, Suganthan PN (2018) A balanced fuzzy cultural algorithm with a modified Levy flight search for real parameter optimization. Inf Sci 447:12–35CrossRefGoogle Scholar
  3. Awad NH, Ali MZ, Suganthan PN, Reynolds RG (2017) CADE: a hybridization of cultural algorithm and differential evolution for numerical optimization. Inf Sci 378:215–241CrossRefGoogle Scholar
  4. Basturk B, Karaboga D (2006) An artificial bee colony (ABC) algorithm for numeric function optimization. In: IEEE swarm intelligence symposium, IndianaGoogle Scholar
  5. Cai Y, Zhao H, Li M, Huang H (2018) 3D real-time path planning based on cognitive behavior optimization algorithm for UAV with TLP model. Clust Comput. Google Scholar
  6. Cheng M-Y, Prayogo D (2014) Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput Struct 139:98–112CrossRefGoogle Scholar
  7. Coello CAC (2000) Use of a self-adaptive penalty approach for engineering optimization problems. Comput Ind 41:113–127CrossRefGoogle Scholar
  8. Derrac J, Garcia S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1:3–18CrossRefGoogle Scholar
  9. Gandomi AH, Yang X-S, Alavi AH (2013a) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29(1):17–35CrossRefGoogle Scholar
  10. Gandomi A, Yang XS, Alavi A, Talatahari S (2013b) Bat algorithm for constrained optimization tasks. Neural Comput Appl 22(6):1239–1255CrossRefGoogle Scholar
  11. Gandomi AH, Yang X-S, Alavi AH (2013c) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29:17–35CrossRefGoogle Scholar
  12. Holland J (1992) Adaptation in natural and artificial systems. MIT Press, CambridgeCrossRefGoogle Scholar
  13. Li Z, Zhou Y et al (2016) Lévy-flight moth-flame algorithm for function optimization and engineering design problems. Math Probl Eng, vol 2016, Article ID 1423930, 22 pagesGoogle Scholar
  14. Li M, Zhao H, Weng X, Han T (2016) Cognitive behavior optimization algorithm for solving optimization problems. Appl Soft Comput 39:199–222CrossRefGoogle Scholar
  15. Long W, Zhang W-Z, Huang Y-F, Chen Y-X (2014) A hybrid cuckoo search algorithm with feasibility-based rule for constrained structural optimization. J Cent South Univ 21(8):3197–3204CrossRefGoogle Scholar
  16. Mezura-Montes E, Coello CAC (2005) Useful infeasible solutions in engineering optimization with evolutionary algorithms. In: MICAI 2005: Lecture notes in artificial intelligence, vol 3789, pp 652–662Google Scholar
  17. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61CrossRefGoogle Scholar
  18. Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27(14–15):495–513CrossRefGoogle Scholar
  19. Omran MGH (2016) A novel cultural algorithm for real-parameter optimization. Int J Comput Math 93:1541–1563MathSciNetCrossRefzbMATHGoogle Scholar
  20. Price KV, Storn RM, Lampinen JA (2005) Differential evolution: a practical approach to global optimization. Springer, BerlinzbMATHGoogle Scholar
  21. Ray T, Liew KM (2003) Society and civilization: an optimization algorithm based on the simulation of social behavior. IEEE Trans Evol Comput 7(4):386–396CrossRefGoogle Scholar
  22. Reynolds RG (1978) On modeling the evolution of hunter-gatherer decision-making systems. Geogr Anal 10(1):31–46CrossRefGoogle Scholar
  23. Reynolds RG (1979) An adaptive computer model of the evolution of agriculture for hunter-gatherers in the valley of Oaxaca, Mexico, Doctoral dissertation. University of Michigan, Ann ArborGoogle Scholar
  24. Reynolds RG (1994) An introduction to cultural algorithms. In: Proceedings of the third annual conference on evolutionary programming, San Diego, pp 131–139Google Scholar
  25. Sadollah A, Bahreininejad A et al (2013) Mine blast algorithm: a new population based algorithm for solving constrained engineering optimization problems. Appl Soft Comput 13:2592–2612CrossRefGoogle Scholar
  26. Salmeron JL, Mansouri T, Moghadam MRS, Mardani A (2018) Learning fuzzy cognitive maps with modified asexual reproduction optimisation algorithm. Knowl Based Syst. Google Scholar
  27. Shi Y, Eberhart RA (1998) Modified particle swarm optimizer. In: IEEE international conference on evolutionary computation, Anchorage, pp 4–9Google Scholar
  28. Yan X, Song T, Wu Q (2017a) An improved cultural algorithm and its application in image matching. Multimed Tools Appl 76(13):14951–14968CrossRefGoogle Scholar
  29. Yan X, Gong W, Wu Q (2017b) Contaminant source identification of water distribution networks using cultural algorithm. Concurr Comput Pract Exp. Google Scholar
  30. Yang XS (2012) Flower pollination algorithm for global optimization. In: Unconventional computation and natural computation. Lecture notes in computer science, vol 7445, pp 240–249Google Scholar
  31. Yang XS, Deb S (2009) Cuckoo search via Levy flights. In: World congress on nature and biologically inspired computing (NaBIC 2009). IEEE Publication, pp 210–214Google Scholar
  32. Zhang S, Zhou Y, Luo Q (2018) Elite opposition-based cognitive behavior optimization algorithm for global optimization. J Intell Syst. Google Scholar
  33. Zhou Y, Su K, Shao L (2018) A new chaotic hybrid cognitive optimization algorithm. Cognit Syst Res 52:537–542CrossRefGoogle Scholar

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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