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Universal Generalized Net Model for Description of Metaheuristic Algorithms: Verification with the Bat Algorithm

  • Olympia RoevaEmail author
  • Vassia Atanassova
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 643)

Abstract

In the present paper, the apparatus of generalized nets is used to describe the metaheuristic technique Bat algorithm. Generalized nets are considered an effective and appropriate tool for description of the logics of different optimization techniques. As a result, the developed generalized net model executes the Bat algorithm procedures, conducting basic steps and performing optimal search. The paper elaborates on the already proposed Universal generalized net model for description of the population-based metaheuristic algorithms, which was used so far to model the Cuckoo search, Firefly algorithm and Artificial bee colony optimization, and is used here for modelling of Bat algorithm. It is shown that the Bat algorithm can be described in terms of Universal generalized net model by only varying the characteristic functions of the tokens. Thus, verification of the Universal generalized net model is performed.

Keywords

Generalized nets Modelling Metaheuristic Bat algorithm 

Notes

Acknowledgements

This work has been partially supported by Grant DFNP-124, Programme for career development of young scientists of the Bulgarian Academy of Sciences, and by Grant DN02/10 “New Instruments for Knowledge Discovery from Data, and their Modelling”, Bulgarian National Science Fund.

References

  1. 1.
    Glover, F.: Tabu search - wellsprings and challenges. Eur. J. Oper. Res. 106, 221–225 (1998)CrossRefzbMATHGoogle Scholar
  2. 2.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley Longman, London (2006)Google Scholar
  3. 3.
    Beyer, H.-G., Schwefel, H.-P.: Evolution strategies: a comprehensive introduction. J. Natural Comput. 1(1), 3–52 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)zbMATHGoogle Scholar
  5. 5.
    Umarani, R., Selvi, V.: Particle swarm optimization: evolution, overview and applications. Int. J. Eng. Sci. Technol. 2(7), 2802–2806 (2010)Google Scholar
  6. 6.
    Yusof, M.K., Stapa, M.A.: Achieving of tabu search algorithm for scheduling technique in grid computing using gridsim simulation tool: multiple jobs on limited resource. Int. J. Grid Distrib. Comput. 3(4), 19–31 (2010)Google Scholar
  7. 7.
    Topal, A.O., Altun, O.: A novel meta-heuristic algorithm: dynamic virtual bats algorithm. Inf. Sci. 354, 222–235 (2016)CrossRefGoogle Scholar
  8. 8.
    Kuo, R.J., Zulvia, F.E.: The gradient evolution algorithm: a new metaheuristic. Inf. Sci. 316, 246–265 (2015)CrossRefGoogle Scholar
  9. 9.
    Özkış, A., Babalık, A.: A novel metaheuristic for multi-objective optimization problems: the multi-objective vortex search algorithm. Inf. Sci. 402, 124–148 (2017)CrossRefGoogle Scholar
  10. 10.
    Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983). New SeriesMathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Yang, X.-S., Deb, S.: Cuckoo search via Lévy flights. In: Proceedings of World Congress on Nature, Biologically Inspired Computing (NaBIC 2009), USA, pp. 210–214. IEEE Publications (2009)Google Scholar
  12. 12.
    Yang, X.-S.: Firefly algorithm for multimodal optimization. In: Lecture Notes in Computing Sciences, vol. 5792, pp. 169–178 (2009)Google Scholar
  13. 13.
    Karaboga, D.: An idea based on honeybee swarm for numerical optimization, Technical report TR06. Erciyes University, Engineering Faculty, Computer Engineering Department (2005)Google Scholar
  14. 14.
    Ceberio, J., Irurozki, E., Mendiburu, A., Lozano, J.A.: A review on estimation of distribution algorithms in permutation-based combinatorial optimization problems. Prog. Artif. Intell. 1, 103–117 (2012)CrossRefzbMATHGoogle Scholar
  15. 15.
    Glover, F.: A template for scatter search and path relinking. In: Lecture Notes in Computing Sciences, vol. 1363, pp. 13–54 (1997)Google Scholar
  16. 16.
    Feo, T.A., Resende, M.G.C.: Greedy randomized adaptive search procedures. J. Global Optim. 6, 109–133 (1995)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Colmenar, J.M., Greistorfer, P., Martí, R., Duarte, A.: Advanced greedy randomized adaptive search procedure for the obnoxious p-median problem. Eur. J. Oper. Res. 252(2), 432–442 (2016)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Mladenovic, N., Hansen, P.: Variable neighborhood search. Comput. Oper. Res. 24, 1097–1100 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Stützle, T.: Local search algorithms for combinatorial problems: analysis, improvements, and new applications, Ph.D. thesis, Darmstadt University of Technology (1998)Google Scholar
  20. 20.
    Brownlee, J.: Clever Algorithms, Nature-Inspired Programming Recipes. LuLu, Raleigh (2011)Google Scholar
  21. 21.
    Atanassov, K.T.: Generalized Nets and Systems Theory. Academic Publishing House “Prof. M. Drinov”, Sofia (1997)zbMATHGoogle Scholar
  22. 22.
    Atanassov, K.T.: Generalized Nets in Artificial Intelligence. Generalized Nets and Expert Systems, vol. 1. Academic Publishing House “Prof. M. Drinov”, Sofia (1998)zbMATHGoogle Scholar
  23. 23.
    Atanassov, K.T.: Generalized Nets. World Scientific, Singapore (1991)CrossRefzbMATHGoogle Scholar
  24. 24.
    Atanassov, K.T.: On Generalized Nets Theory. Academic Publishing House “Prof. M. Drinov”, Sofia (2007)zbMATHGoogle Scholar
  25. 25.
    Choy, E., Krawczak, M., Shannon, A., Szmidt, E. (eds.) A Survey of Generalized Nets, Raffles KvB Monograph No. 10, Australia (2007)Google Scholar
  26. 26.
    Georgieva, V., Sotirova, E.: Generalized net model of biological treatment of wastewater. In: Issues in Intuitionistic Fuzzy Sets and Generalized Nets, vol. 11, pp. 63–72 (2014)Google Scholar
  27. 27.
    Georgieva, V., Angelova, N., Roeva, O., Pencheva, T.: Simulation of parallel processes in wastewater treatment plant using generalized net integrated development environment. Comptes rendus de l’Academie bulgare des Sci. 69(11), 1493–1502 (2016)Google Scholar
  28. 28.
    Kosev, K., Melo-Pinto, P., Roeva, O.: Generalized net model of the lac operon in bacterium E. coli. In: Proceedings of the 6th IEEE International Conference on Intelligent Systems, pp. 237–241 (2012)Google Scholar
  29. 29.
    Pencheva, T., Roeva, O., Bentes, I., Barroso, J.: Generalized nets model for fixed-bed bioreactors. In: Proceedings of the 10th ISPE International Conference on Concurrent Engineering – Advanced Design, Production and Management Systems, Madeira, Portugal, 26–30 July 2003, pp. 1025–1028 (2013)Google Scholar
  30. 30.
    Ribagin, S., Roeva, O., Pencheva, T.: Generalized net model of asymptomatic osteoporosis diagnosing. In: Proceedings of the IEEE 8th International Conference on Intelligent Systems, pp. 604–608 (2016)Google Scholar
  31. 31.
    Ribagin, S., Chakarov, V., Atanassov, K.: Generalized net model of the scapulohumeral rhythm. In: Studies in Computational Intelligence, vol. 657, pp. 229–247 (2017)Google Scholar
  32. 32.
    Dimitrov, D., Roeva, O.: Development of generalized net for testing of different mathematical models of E. coli cultivation process. In: Advances in Intelligent Systems and Computing, vol. 322, pp. 657–668 (2015)Google Scholar
  33. 33.
    Roeva, O., Atanassova, V.: Generalized net model of cuckoo search algorithm. In: Proceedings of the IEEE 8th International Conference on Intelligent Systems, pp. 589–592 (2016)Google Scholar
  34. 34.
    Krawczak, M., Sotirov, S., Sotirova, E.: Generalized net model for parallel optimization of multilayer neural network with time limit. In: Proceedings of the 6th IEEE International Conference on Intelligent Systems, pp. 173–177 (2012)Google Scholar
  35. 35.
    Sotirov, S., Orozova, D., Sotirova, E.: Generalized net model of the process of the prognosis with feedforward neural network. In: Proceedings of the 16th International Symposium on Electrical Apparatus and Technologies, vol. 1, pp. 272–278 (2009)Google Scholar
  36. 36.
    Chountas, P., Atanassov, K., Sotirova, E., Bureva, V.: Generalized net model of an expert system dealing with temporal hypothesis. In: Advances in Intelligent Systems and Computing, vol. 400, pp. 473–481 (2016)Google Scholar
  37. 37.
    Peneva, D., Tasseva, V., Kodogiannis, V., Sotirova, E., Atanassov, K.: Generalized nets as an instrument for description of the process of expert system construction. In: Proceedings of the 3rd IEEE International Conference on Intelligent Systems, pp. 755–759 (2006)Google Scholar
  38. 38.
    Shannon, A.G., Riečan, B., Sotirova, E., Atanassov, K., Krawczak, M., Melo-Pinto, P., Parvathi, R., Kim, T.: Generalized net models of academic promotion and doctoral candidature. In: Studies in Computational Intelligence, vol. 657, pp. 263–277 (2017)Google Scholar
  39. 39.
    Shannon, A.G., Riečan, B., Orozova, D., Sotirova, E., Atanassov, K., Krawczak, M., Melo-Pinto, P., Parvathi, R., Kim, T.: Generalized net model of the process of selection and usage of an intelligent e-learning system. In: Proceedings of the 6th IEEE International Conference on Intelligent Systems, pp. 233–236 (2012)Google Scholar
  40. 40.
    Sotirova, E., Bureva, V., Sotirov, S.: A generalized net model for evaluation process using intercriteria analysis method in the university. In: Studies in Fuzziness and Soft Computing, vol. 332, pp. 389–399 (2016)Google Scholar
  41. 41.
    Atanassov, K., Gluhchev, G., Hadjitodorov, S., Kacprzyk, J., Shannon, A., Szmidt, E., Vassilev, V.: Generalized Nets Decision Making and Pattern Recognition. Warsaw School of Information Technology, Warszawa (2006)Google Scholar
  42. 42.
    Pencheva, T., Roeva, O., Shannon, A.: Generalized net models of basic genetic algorithm operators. In: Studies in Fuzziness and Soft Computing, vol. 332, pp. 305–325 (2016)Google Scholar
  43. 43.
    Roeva, O., Melo-Pinto, P.: Generalized net model of firefly algorithm. In: Proceedings of the 14th International Workshop on Generalized Nets, Burgas, 29 November 2013, pp. 22–27 (2013)Google Scholar
  44. 44.
    Fidanova, S., Atanassov, K., Marinov, P.: Generalized Nets in Artificial Intelligence. Generalized Nets and Ant Colony Optimization, vol. 5. Academic Publishing House “Prof. Marin Drinov”, Sofia (2011)Google Scholar
  45. 45.
    Roeva, O., Shannon, A., Pencheva, T.: Description of simple genetic algorithm modifications using generalized nets. In: Proceedings of 6th IEEE International Conference on Intelligent Systems, Sofia, Bulgaria, Vol. 2, pp. 178–183 (2012)Google Scholar
  46. 46.
    Roeva, O., Atanassova, V.: Cuckoo search algorithm, firefly algorithm and artificial bee colony optimization in terms of generalized net theory. In: Computational Intelligence series. Springer (in press)Google Scholar
  47. 47.
    Altringham, J.D.: Bats: Biology and Behaviour. Oxford Univesity Press, Oxford (1996)Google Scholar
  48. 48.
    Yang, X.-S., Gandomi, A.H.: Bat algorithm: a novel approach for global engineering optimization. Eng. Comput. 29(5), 464–483 (2012)CrossRefGoogle Scholar
  49. 49.
    Yang, X.-S.: A new metaheuristic bat-inspired algorithm. In: Nature-Inspired Cooperative Strategies for Optimization. In: Studies in Computational Intelligence, vol. 284, pp. 65–74 (2010)Google Scholar
  50. 50.
    Gandomi, A.H., Yang, X.-S., Alavi, A.H., Talatahari, S.: Bat algorithm for constrained optimization tasks. Neural Comput. Appl. 22(6), 1239–1255 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Bioinformatics and Mathematical Modelling Department, Institute of Biophysics and Biomedical EngineeringBulgarian Academy of SciencesSofiaBulgaria

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