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A New Bat Algorithm with Fuzzy Logic for Dynamical Parameter Adaptation and Its Applicability to Fuzzy Control Design

  • Jonathan Pérez
  • Fevrier Valdez
  • Oscar Castillo
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 574)

Abstract

We describe in this paper the Bat Algorithm and a new approach is proposed using a fuzzy system to dynamically adapt its parameters. The original method is compared with the proposed method and also compared with genetic algorithms, providing a more complete analysis of the effectiveness of the bat algorithm. Simulation results on a set of mathematical functions with the fuzzy bat algorithm outperform the traditional bat algorithm and genetic algorithms and proposed to implement the method in a controller to analyze the effectiveness of the algorithm.

Keywords

Bat algorithm Genetic algorithm Fuzzy system 

Notes

Acknowledgments

We would like to express our gratitude to the CONACYT and Tijuana Institute of Technology for the facilities and resources granted for the development of this research.

References

  1. 1.
    Alemu, T., Mohd, F.: Use of Fuzzy Systems and Bat Algorithm for Exergy Modeling in a Gas Turbine Generator. TamiruAlemu Lemma, Department of Mechanical Engineering, Malaysia, (2011)Google Scholar
  2. 2.
    Biswal, S., Barisal, A.K., Behera, A., Prakash, T.: Optimal Power Dispatch Using Bat Algorithm, Department of Electrical Engineering., VSSUT, Burla, India, (2013)Google Scholar
  3. 3.
    Engelbrecht, A.: Fundamentals of Computational Swarm Intelligence. A. P. Engelbrecht University of Pretoria South Africa, Wiley. pp. 25–26 and 66–70, (2005)Google Scholar
  4. 4.
    Fierro, R., Castillo, O., Valdez, F., Cervantes, L.: Design of optimal membership functions for fuzzy controllers of the water tank and inverted pendulum with PSO variants. In: IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), 2013 JointGoogle Scholar
  5. 5.
    Gandomi, A., Yang, X.: Chaotic Bat Algorithm. The University of Akron, Department of Civil Engineering, USA (2013)Google Scholar
  6. 6.
    Goel, N., Gupta, D., Goel, S.: Performance of Firefly and Bat Algorithm for Unconstrained Optimization Problems, Department of Computer Science Maharaja Surajmal. Institute of Technology GGSIP university C-4 Janakpuri, New Delhi, India (2013)Google Scholar
  7. 7.
    Hasançebi, O., Carbas, S.: Bat inspired Algorithm For Discrete Size Optimization Of Steel Frames. Department of Civil Engineering, Middle East Technical University, 06800 Ankara, Turkey, (2013)Google Scholar
  8. 8.
    Hasançebi, O., Teke, T., Pekcan, O.: A Bat-Inspired Algorithm For Structural Optimization. Middle East Technical University Department of Civil Engineering, Ankara, Turkey (2013)Google Scholar
  9. 9.
    Kashi, S., Minuchehr, A., Poursalehi, N., Zolfaghari, A.: Bat Algorithm For The Fuel Arrangement Optimization of Reactor Core. ShahidBeheshti University, Nuclear Engineering Department, Tehran, Iran (2013)Google Scholar
  10. 10.
    Khan, K., Sahai, A. A.: Comparison of BA, GA, PSO, BP and LM for Training Feed forward Neural Networks in e-Learning Context. Department of Computing and Information Technology, University of the West Indies, St. Augustine, Trinidad And Tobago, (2012)Google Scholar
  11. 11.
    Kotteeswaran, R., Sivakumar, L.: Optimal Partial-Retuning of Decentralised PI Controller of Coal Gasifier Using Bat Algorithm. Swarm, Evolutionary, and Memetic Computing, Springer, pp. 750–761, (2013)Google Scholar
  12. 12.
    Melin, P., Olivas, F., Castillo, O., Valdez, F., Soria, J., Garcia, J.: Optimal design of fuzzy classification systems using PSO with dynamic parameter adaptation through fuzzy logic. Expert Syst. Appl. 40(8), 3196–3206 (2013)CrossRefGoogle Scholar
  13. 13.
    Mishra, S., Shaw, K., Mishra, D.: A New Meta-heuristic Bat Inspired Classification Approach for Microarray Data. Siksha O Anusandhan Deemed to be University, Institute of Technical Education and Research, Bhubaneswar, Odisha, India (2011)Google Scholar
  14. 14.
    Musikapun, P., Pongcharoen, P.: Solving Multi-Stage MultiMachine Multi-Product Scheduling Problem Using Bat Algorithm. Faculty of Engineering, Naresuan University, Department of Industrial Engineering, Thailand (2012)Google Scholar
  15. 15.
    Nakamura, R., Pereira, L., Costa, K., Rodrigues, D., Papa, J.: BBA: A Binary Bat Algorithm for Feature Selection. Department of Computing Sao Paulo State University Bauru, Brazil, (2012)Google Scholar
  16. 16.
    Neyoy, H., Castillo, O., Soria, J.: Dynamic fuzzy logic parameter tuning for ACO and its application in TSP problems. In: Recent Advances on Hybrid Intelligent Systems, pp. 259–271, (2013)Google Scholar
  17. 17.
    Rodrigues, D., Pereira, L., Nakamura, R., Costa, K., Yang, X., Souza, A., Papa, J. P.: A Wrapper Approach for Feature Selection Based on Bat Algorithm and Optimum-Path Forest. Department of Computing, Universidade Estadual Paulista, Bauru, Brazil, (2013)Google Scholar
  18. 18.
    Taherian, H., NazerKakhki, I., Aghaebrahimi, M.: Application of an Improved SVR Based Bat Algorithm for Short-Term Price Forecasting in the Iranian Pay-as-Bid Electricity Market. University of Birjand, Birjand, Department of Electrical and Computer Engineering, Iran (2013)Google Scholar
  19. 19.
    Valdez, F., Melin, P., Castillo, O.: Evolutionary method combining particle swarm optimization and genetic algorithms using fuzzy logic for decision making. In: Proceedings of the IEEE International Conference on Fuzzy Systems, pp. 2114–2119, (2009)Google Scholar
  20. 20.
    Valdez, F., Melin, P., Castillo, O.: Parallel Particle Swarm Optimization witch Parameters Adaptation Using Fuzzy Logic. In: Batyrshin, I., González Mendoza, M. (eds.): MICAI 2012, Part II, LNAI 7630, pp. 374–385, 2012. Springer Berlin Heidelberg (2012)Google Scholar
  21. 21.
    Yammani, C., Maheswarapu, S., Sailaja Kumari, M.: Optimal Placement and Sizing of DER’s with Load Models Using BAT Algorithm. Electrical Engineering Department, National Institute of Technology, Warangal, India (2013)Google Scholar
  22. 22.
    Yang, X.: A New Metaheuristic Bat-Inspired Algorithm. Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, UK, (2010)Google Scholar
  23. 23.
    Yang, X.: Bat Algorithm: Literature Review and Applications. School of Science and Technology, Middlesex University, The Burroughs, London NW4 4BT, United Kingdom, (2013)Google Scholar
  24. 24.
    Yang, X., Karamanoglu, M., Fong, S.: Bat algorithm for topology optimization in microelectronic applications. School of Science and Technology, Middlesex University, Hendon Campus, London NW4 4BT, UK, (2012)Google Scholar
  25. 25.
    Yuanbin, M., Xinquan, Z., Sujian, X.: Local Memory Search Bat Algorithm for Grey Economic Dynamic System. Statistics and Mathematics Institute, (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Jonathan Pérez
    • 1
  • Fevrier Valdez
    • 1
  • Oscar Castillo
    • 1
  1. 1.Tijuana Institute of TechnologyTijuanaMexico

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