Skip to main content
Log in

Cuckoo search and firefly algorithms in terms of generalized net theory

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

In the presented paper, the functioning and the results of the work of two metaheuristic algorithms, namely cuckoo search algorithm (CS) and firefly algorithm (FA), are described using the apparatus of generalized nets (GNs), which is an appropriate and efficient tool for describing the essence of various optimization methods. The two developed GN-models mimic the optimization processes based on the nature of cuckoos and fireflies, respectively. The proposed GN-models execute the two considered metaheuristic algorithms conducting basic steps and performing optimal search. Building upon these two GN-models, a universal GN-model is constructed that can be used for describing and simulating both the CS and the FA by setting different characteristic functions of the GN-tokens. Moreover, the universal GN-model itself can be transformed to each of the herewith presented GN-models by applying appropriate hierarchical operators. In order to validate the proposed universal GN-model, numerical experiments are performed for the operating of the universal GN-model (CS and FA) on benchmark mathematical functions. The obtained results are compared with the results of the GN-model of CS, GN-model of FA, as well as the results of the standard CS and FA.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  • Angelova N, Todorova M, Atanassov K (2016) GN IDE: implementation, improvements and algorithms. C R Acad Bulg Sci 69(4):411–420

    MathSciNet  MATH  Google Scholar 

  • Atanassov KT (1987) Generalized index matrices. C R Acad Bulg Sci 40(11):15–18

    MathSciNet  MATH  Google Scholar 

  • Atanassov KT (1991) Generalized nets. World Scientific, Singapore

    Book  Google Scholar 

  • Atanassov KT (1998) Generalized nets in artificial intelligence: generalized nets and expert systems, vol 1. “Prof. M. Drinov” Academic Publishing House, Sofia

    MATH  Google Scholar 

  • Atanassov KT (2007) On generalized nets theory. “Prof. Marin Drinov” Academic Publishing House, Sofia

    MATH  Google Scholar 

  • Atanassov KT (2014) Index matrices: towards an augmented matrix calculus. Springer, Cham

    MATH  Google Scholar 

  • Atanassov KT (2016) Generalized nets as a tool for the modelling of data mining processes. In: Sgurev V, Yager R, Kacprzyk J, Jotsov V (eds) Innovative issues in intelligent systems. Springer, Cham, pp 161–215

    Chapter  Google Scholar 

  • Atanassov KT, Aladjov H (2000) Generalized nets in artificial intelligence: generalized nets and machine learning, vol 2. “Prof. Marin Drinov” Publishing House of the Bulgarian Academy of Sciences, Sofia

    MATH  Google Scholar 

  • Banerjee A, Ghosh D, Das S (2018) Modified firefly algorithm for area estimation and tracking of fast expanding oil spills. Appl Soft Comput 73:829–847

    Article  Google Scholar 

  • Chiroma H, Herawan T, Fister I Jr, Fister I, Abdulkareem S, Shuib L, Hamza MF, Saadi Y, Abubakar A (2017) Bio-inspired computation: recent development on the modifications of the cuckoo search algorithm. Appl Soft Comput 61:149–173

    Article  Google Scholar 

  • Choy E, Krawczak M, Shannon A, Szmidt E (eds) (2007) A survey of generalized nets, vol 10. Raffles KvB Monograph, North Sydney

    Google Scholar 

  • Fidanova S, Atanassov K, Marinov P (2011) Generalized nets in artificial intelligence: generalized nets and ant colony optimization, vol 5. “Prof. M. Drinov” Academic Publishing House, Sofia

    Google Scholar 

  • Fister I, Fister I Jr, Yang X-S, Brest J (2013) A comprehensive review of firefly algorithms. Swarm Evol Comput 13:34–46

    Article  Google Scholar 

  • Fister I Jr, Perc M, Kamal SM, Fister I (2015) A review of chaos-based firefly algorithms: perspectives and research challenges. Appl Math Comput 252:155–165

    MathSciNet  MATH  Google Scholar 

  • Georgieva V, Angelova N, Roeva O, Pencheva T (2016) Simulation of parallel processes in wastewater treatment plant using generalized net integrated development environment. C R Acad Bulg Sci 69(11):1493–1502

    Google Scholar 

  • González CI, Castro JR, Melin P, Castillo O (2015) Cuckoo search algorithm for the optimization of type-2 fuzzy image edge detection systems, CEC 2015, pp 449–455

  • Guerrero M, Castillo O, Valdez MG (2015a) Cuckoo search via Lévy flights and a comparison with genetic algorithms. In: Fuzzy logic augmentation of nature-inspired optimization metaheuristics. Springer, Germany, pp 91–103

    Google Scholar 

  • Guerrero M, Castillo O, García Valdez M (2015b) Study of parameter variations in the cuckoo search algorithm and the influence in its behavior. In: Melin P, Castillo O, Kacprzyk J (eds) Design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization. Springer, Germany, pp 199–210

    Chapter  Google Scholar 

  • Jamil M, Yang X-S (2013) A literature survey of benchmark functions for global optimization problems. Int J Math Model Num Opt 4(2):150–194. https://doi.org/10.1504/IJMMNO.2013.055204

    Article  MATH  Google Scholar 

  • Majumder A, Laha D (2016) A new cuckoo search algorithm for 2-machine robotic cell scheduling problem with sequence-dependent setup times. Swarm Evol Comput 28:131–143

    Article  Google Scholar 

  • Mlakar U, Fister I Jr, Fister I (2016) Hybrid self-adaptive cuckoo search for global optimization. Swarm Evol Comput. https://doi.org/10.1016/j.swevo.2016.03.001

    Article  Google Scholar 

  • Mohamad AB, Zain AM, Bazin NEN (2014) Cuckoo search algorithm for optimization problems: a literature review and its applications, applied artificial intelligence. Int J 28(5):419–448

    Google Scholar 

  • Nasiri B, Meybodi MR (2012) Speciation-based firefly algorithm for optimization in dynamic environments. Int J Artif Intell 8(S12):118–132

    Google Scholar 

  • Payne RB, Sorenson MD, Klitz K (2005) The cuckoos. Oxford University Press, Oxford

    Google Scholar 

  • Roeva O (2012) Optimization of E. coli cultivation model parameters using firefly algorithm. Int J Bioautom 16(1):23–32

    Google Scholar 

  • Roeva O, Atanassova V (2016) Cuckoo search algorithm for model parameter identification. Int J Bioautom 20(4):483–492

    Google Scholar 

  • Roeva O, Slavov TS (2012) Firefly algorithm tuning of PID controller for glucose concentration control during E. coli fed-batch cultivation process. In: Proceedings of the federated conference on computer science and information systems, WCO, Poland, pp 455–462

  • Roeva O, Shannon A, Pencheva T (2012) Description of simple genetic algorithm modifications using generalized nets. In: Proceedings of 6th IEEE international conference on intelligent systems, Bulgaria, pp 178–183

  • Roeva O, Pencheva T, Shannon A, Atanassov K (2013) Generalized nets in artificial intelligence: generalized nets and genetic algorithms, vol 7. “Prof. Marin Drinov” Publishing House of the Bulgarian Academy of Sciences, Sofia

    Google Scholar 

  • Shehab M, Khader AT, Al-Betar MA (2017) A survey on applications and variants of the cuckoo search algorithm. Appl Soft Comput 61:1041–1059

    Article  Google Scholar 

  • Siva Sathya S, Radhika MV (2013) Convergence of nomadic genetic algorithm on benchmark mathematical functions. Appl Soft Comput 13(5):2759–2766

    Article  Google Scholar 

  • Sotirov S, Atanassov KT (2013) Generalized nets in artificial intelligence: generalized nets and supervised neural networks, vol 6. “Prof. Marin Drinov” Publishing House of the Bulgarian Academy of Sciences, Sofia

    Google Scholar 

  • Tangherloni A, Spolaor S, Cazzaniga P et al (2019a) Biochemical parameter estimation vs. benchmark functions: a comparative study of optimization performance and representation design. Appl Soft Comput 81:105494. https://doi.org/10.1016/j.asoc.2019.105494

    Article  Google Scholar 

  • Tangherloni A, Spolaor S, Cazzaniga P, Besozzi D, Rundo L, Mauri G, Nobile MS (2019b) Biochemical parameter estimation vs. benchmark functions: a comparative study of optimization performance and representation design. Appl Soft Comput 81:105494

    Article  Google Scholar 

  • Tzanov V, Todorova L, Zoteva D, Dukovska L (2019) Generalized net model of processes of loading and transportation of raw materials of open construction sites. In: Atanassov KT, Kacprzyk J et al (eds) Uncertainty and imprecision in decision making and decision support: cross fertilization, new models and applications. Springer, Cham (in press)

    Google Scholar 

  • Yang XS (2008) Nature-inspired meta-heuristic algorithms. Luniver Press, Beckington

    Google Scholar 

  • Yang XS (2009) Firefly algorithm for multimodal optimization. Lecture notes in computing sciences, vol 5792. Springer, Berlin, pp 169–178

    Google Scholar 

  • Yang XS (2010a) Firefly algorithm, levy flights and global optimization, research and development in intelligent systems, vol XXVI. Springer, London, pp 209–218

    Google Scholar 

  • Yang XS (2010b) Firefly algorithm, stochastic test functions and design optimisatio. Int J Bio-inspired Comput 2(2):78–84

    Article  Google Scholar 

  • Yang X-S (2014) Nature-inspired optimization algorithms. Elsevier, London

    MATH  Google Scholar 

  • Yang X-S, Deb S (2009) Cuckoo search via Lévy flights. In: Proceedings of world congress on nature and biologically inspired computing (NaBIC 2009). IEEE Publications, pp 210–214

  • Yang B, Miao J, Fan Z, Long J, Liu X (2018) Modified cuckoo search algorithm for the optimal placement of actuators problem. Appl Soft Comput 67:48–60

    Article  Google Scholar 

  • Yelghi A, Köse C (2018) A modified firefly algorithm for global minimum optimization. Appl Soft Comput 62:29–44

    Article  Google Scholar 

  • Yousif A, Abdullah AH, Nor SM, Abdelaziz AA (2011) Scheduling jobs on grid computing using firefly algorithm. J Theor Appl Inf Technol 33(2):155–164

    Google Scholar 

Download references

Acknowledgements

The work presented here is partially supported by the National Scientific Fund of Bulgaria under Grant DN02/10 “New Instruments for Knowledge Discovery from Data, and their Modelling” (D. Zoteva and K. Atanassov) and by the Program for career development of young scientists, BAS, Grant DFNP-17-136/2017 (O. Roeva and V. Atanassova). This research work did not receive funding.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Oscar Castillo.

Ethics declarations

Conflict of interest

All the authors in the paper have no conflict of interest.

Ethical approval

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

Additional information

Communicated by V. Loia.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Roeva, O., Zoteva, D., Atanassova, V. et al. Cuckoo search and firefly algorithms in terms of generalized net theory. Soft Comput 24, 4877–4898 (2020). https://doi.org/10.1007/s00500-019-04241-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-019-04241-7

Keywords

Navigation