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Abstract

The paper provides an insight into the improved novel metaheuristics of the Firefly Algorithm for constrained continuous optimization tasks. The presented technique is inspired by social behavior of fireflies and the phenomenon of bioluminescent communication. The first part of the paper is devoted to the detailed description of the existing algorithm. Then some suggestions for extending the simple scheme of the technique under consideration are presented. Subsequent sections concentrate on the performed experimental parameter studies and a comparison with existing Particle Swarm Optimization strategy based on existing benchmark instances. Finally some concluding remarks on possible algorithm extensions are given, as well as some properties of the presented approach and comments on its performance in the constrained continuous optimization tasks.

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References

  1. Encyclopædia Britannica: Firefly. In: Encyclopædia Britannica. Ultimate Reference Suite. Encyclopædia Britannica, Chicago (2009)

    Google Scholar 

  2. Babu, B.G., Kannan, M.: Lightning bugs. Resonance 7(9), 49–55 (2002)

    Article  Google Scholar 

  3. Fraga, H.: Firefly luminescence: A historical perspective and recent developments. Journal of Photochemical & Photobiological Sciences 7, 146–158 (2008)

    Article  Google Scholar 

  4. Lewis, S., Cratsley, C.: Flash signal evolution, mate choice, and predation in fireflies. Annual Review of Entomology 53, 293–321 (2008)

    Article  Google Scholar 

  5. Leidenfrost, R., Elmenreich, W.: Establishing wireless time-triggered communication using a firefly clock synchronization approach. In: Proceedings of the 2008 International Workshop on Intelligent Solutions in Embedded Systems, pp. 1–18 (2008)

    Google Scholar 

  6. Jumadinova, J., Dasgupta, P.: Firefly-inspired synchronization for improved dynamic pricing in online markets. In: Proceedings of the 2008 Second IEEE International Conference on Self-Adaptive and Self-Organizing Systems, pp. 403–412 (2008)

    Google Scholar 

  7. Krishnanand, K., Ghose, D.: Glowworm swarm based optimization algorithm for multimodal functions with collective robotics applications. Multiagent and Grid Systems 2(3), 209–222 (2006)

    Article  MATH  Google Scholar 

  8. Yang, X.S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press (2008)

    Google Scholar 

  9. Eberhart, R.C., Shi, Y.: Computational Intelligence: Concepts to Implementations. Morgan Kaufmann, San Francisco (2007)

    Book  MATH  Google Scholar 

  10. Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization 39(3), 459–471 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  11. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, 1995. Proceedings, vol. 4, pp. 1942–1948 (1995)

    Google Scholar 

  12. Schutte, J.F., Groenwold, A.A.: A study of global optimization using particle swarms. Journal of Global Optimization 31(1), 93–108 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  13. Ingber, L.: Adaptive simulated annealing (ASA): lessons learned. Control & Cybernetics 25(1), 33–55 (1996)

    MATH  Google Scholar 

  14. Himmelblau, D.M.: Applied Nonlinear Programming. McGraw-Hill, New York (1972)

    MATH  Google Scholar 

  15. Schwefel, H.P.: Numerical Optimization of Computer Models. John Wiley & Sons, Inc., Chichester (1981)

    MATH  Google Scholar 

  16. Easom, E.: A survey of global optimization techniques. Master’s thesis, University of Louisville (1990)

    Google Scholar 

  17. Mühlenbein, H., Schomisch, D., Born, J.: The Parallel Genetic Algorithm as Function Optimizer. Parallel Computing 17(6-7), 619–632 (1991)

    Article  MATH  Google Scholar 

  18. Griewank, A.: Generalized descent for global optimization. Journal of Optimization Theory and Applications 34, 11–39 (1981)

    Article  MathSciNet  MATH  Google Scholar 

  19. Rosenbrock, H.H.: State-Space and Multivariable Theory. Thomas Nelson & Sons Ltd. (1970)

    Google Scholar 

  20. Neumaier, A.: Permutation function, http://www.mat.univie.ac.at/~neum/glopt/my_problems.html

  21. Törn, A., Žilinskas, A.: Global Optimization. Springer, Heidelberg (1989)

    Book  MATH  Google Scholar 

  22. Shekel, J.: Test functions for multimodal search techniques. In: Proceedings of the 5th Princeton Conference on Infomration Science and Systems, pp. 354–359 (1971)

    Google Scholar 

  23. Jansson, C., Knüppel, O.: Numerical results for a self-validating global optimization method. Technical Report 94.1, Technical University of Hamburg-Harburg (1994)

    Google Scholar 

  24. Bilchev, G., Parmee, I.: Inductive search. In: Proceedings of IEEE International Conference on Evolutionary Computation, pp. 832–836 (1996)

    Google Scholar 

  25. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Heidelberg (1998)

    MATH  Google Scholar 

  26. Neumaier, A.: Powersum function, http://www.mat.univie.ac.at/~neum/glopt/my_problems.html

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© 2009 Springer-Verlag Berlin Heidelberg

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Łukasik, S., Żak, S. (2009). Firefly Algorithm for Continuous Constrained Optimization Tasks. In: Nguyen, N.T., Kowalczyk, R., Chen, SM. (eds) Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems. ICCCI 2009. Lecture Notes in Computer Science(), vol 5796. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04441-0_8

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  • DOI: https://doi.org/10.1007/978-3-642-04441-0_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04440-3

  • Online ISBN: 978-3-642-04441-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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