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A Memetic Version of the Bacterial Evolutionary Algorithm for Discrete Optimization Problems

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Information Technology, Systems Research, and Computational Physics (ITSRCP 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 945))

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Abstract

In this paper we present our test results with our memetic algorithm, the Discrete Bacterial Memetic Evolutionary Algorithm (DBMEA). The algorithm combines the Bacterial Evolutionary Algorithm with discrete local search techniques (2-opt and 3-opt).

The algorithm has been tested on four discrete NP-hard optimization problems so far, on the Traveling Salesman Problem, and on its three variants (the Traveling Salesman Problem with Time Windows, the Traveling Repairman Problem, and the Time Dependent Traveling Salesman Problem). The DBMEA proved to be efficient for all problems: it found optimal or close-optimal solutions. For the Traveling Repairman Problem the DBMEA outperformed even the state-of-the-art methods.

The preliminary version of this paper was presented at the 3rd Conference on Information Technology, Systems Research and Computational Physics, 2–5 July 2018, Cracow, Poland [1].

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References

  1. Tüű-Szabó, B., Földesi, P., Kóczy, L.T.: A Memetic version of the Bacterial Evolutionary Algorithm for discrete optimization problem. In: Kulczycki, P., Kowalski, P.A., Łukasik, S. (eds.) Contemporary Computational Science, p. 75. AGH-UST Press, Cracow (2018)

    Google Scholar 

  2. Applegate, D.L., Bixby, R.E., Chvátal, V., Cook, W.J., Espinoza, D., Goycoolea, M., Helsgaun, K.: Certification of an optimal tour through 85,900 cities. Oper. Res. Lett. 37(1), 11–15 (2009)

    Article  MathSciNet  Google Scholar 

  3. Botzheim, J., Toda, Y., Kubota, N.: Bacterial memetic algorithm for offline path planning of mobile robots. Memetic Comput. 4, 73–86 (2012)

    Article  Google Scholar 

  4. da Silva, R.F., Urrutia, S.: A General VNS heuristic for the traveling salesman problem with time windows. Discrete Optim. 7(4), 203–211 (2010)

    Article  MathSciNet  Google Scholar 

  5. Das, S., Chowdhury, A., Abraham, A.: A bacterial evolutionary algorithm for automatic data clustering. In: IEEE Congress on Evolutionary Computation, CEC 2009, pp. 2403–2410 (2009)

    Google Scholar 

  6. Dányádi, Zs., Földesi, P., Kóczy, L.T.: A bacterial evolutionary solution for three dimensional bin packing problems using fuzzy fitness evaluation. Aus. J. Intell. Inf. Process. Syst. 13(1), 7 (2011). Paper 1181

    Google Scholar 

  7. Dányádi, Zs., Földesi, P., Kóczy, L.T.: Solution of a fuzzy resource allocation problem by various evolutionary approaches. In: IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS) 2013 Joint, pp. 807–812 (2013)

    Google Scholar 

  8. Farkas, M., Földesi, P., Botzheim, J., Kóczy, L.T.: Approximation of a modified traveling salesman problem using bacterial memetic algorithms. In: Towards Intelligent Engineering and Information Technology (Studies in Computational Intelligence, vol. 243), pp. 607–625. Springer, Berlin (2009)

    Google Scholar 

  9. Földesi, P., Botzheim, J.: Modeling of loss aversion in solving fuzzy road transport traveling salesman problem using eugenic bacterial memetic algorithm. Memetic Comput. 2(4), 259–271 (2010)

    Article  Google Scholar 

  10. Földesi, P., Kóczy, L.T., Botzheim, J., Farkas, M.: Eugenic bacterial memetic algorithm for fuzzy road transport traveling salesman problem. In: The 6th International Symposium on Management Engineering (ISME 2009), August 5–7, Dalian, China (2009)

    Google Scholar 

  11. Földesi, P., Botzheim, J., Kóczy, L.T.: Eugenic bacterial memetic algorithm for fuzzy road transport traveling salesman problem. Int. J. Innovative Comput. 7(5(B)), 2775–2798 (2011)

    Google Scholar 

  12. Helsgaun, K.: An effective implementation of the Lin-Kernighan traveling salesman heuristic. Eur. J. Oper. Res. 126, 106–130 (2000)

    Article  MathSciNet  Google Scholar 

  13. Hoos, H.H., Stutzle, T.: Stochastic Local Search: Foundations and Applications. Morgan Kaufmann, San Francisco (2005)

    MATH  Google Scholar 

  14. Hsieh, T.-J.: Hierarchical redundancy allocation for multi-level reliability systems employing a bacterial-inspired evolutionary algorithm. Inf. Sci. 288, 174–193 (2014)

    Article  Google Scholar 

  15. Inoue, T., Furuhashi, T., Maeda, H., Takaba, M.: A study on interactive nurse scheduling support system using bacterial evolutionary algorithm engine. Trans. Inst. Electron. Inf. Syst. 122-C, 1803–1811 (2002)

    Google Scholar 

  16. Kóczy, L.T., Földesi, P., Tüű-Szabó, B.: An effective discrete bacterial memetic evolutionary algorithm for the traveling salesman problem. Int. J. Intell. Syst. 32(8), 862–876 (2017)

    Article  Google Scholar 

  17. Kóczy, L.T., Földesi, P., Tüű-Szabó, B.: Enhanced discrete bacterial memetic evolutionary algorithm-an efficacious metaheuristic for the traveling salesman optimization. Inf. Sci. 460, 389–400 (2017)

    MathSciNet  Google Scholar 

  18. Li, F., Golden, B., Wasil, E.: Solving the time dependent traveling salesman problem. In: Golden, B., Raghavan, S., Wasil, E. (eds.) The Next Wave in Computing, Optimization, and Decision Technologies. Operations Research/Computer Science Interfaces Series, vol. 29. Springer, Boston (2005)

    Google Scholar 

  19. Moscato, P.: On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts -Towards Memetic Algorithms, Technical Report Caltech Concurrent Computation Program, Report. 826, California Institute of Technology, Pasadena, USA (1989)

    Google Scholar 

  20. Nawa, N.E., Furuhashi, T.: Fuzzy system parameters discovery by bacterial evolutionary algorithm. IEEE Trans. Fuzzy Syst. 7, 608–616 (1999)

    Article  Google Scholar 

  21. Ohlmann, J.W., Thomas, B.W.: A compressed-annealing heuristic for the traveling salesman problem with time windows. INFORMS J. Comput. 19(1), 80–90 (2007)

    Article  MathSciNet  Google Scholar 

  22. Salehipour, A., Sörensen, K., Goos, P., Bräysy, O.: Efficient GRASP+VND and GRASP+VNS metaheuristics for the traveling repairman problem. 4OR Q. J. Oper. Res. 9(2), 189–209 (2011)

    Article  MathSciNet  Google Scholar 

  23. Schneider, J.: The time-dependent traveling salesman problem. Phys. A 314, 151–155 (2002)

    Article  MathSciNet  Google Scholar 

  24. Silva, M.M., Subramanian, A., Vidal, T., Ochi, L.S.: A simple and effective metaheuristic for the Minimum Latency Problem. Eur. J. Oper. Res. 221(3), 513–520 (2012)

    Article  Google Scholar 

  25. Tüű-Szabó, B., Földesi, P., Kóczy, L.T.: A population based metaheuristic for the Minimum Latency Problem. In: ESCIM 2017, Faro, Portugal (2017)

    Google Scholar 

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Acknowledgement

The authors would like to thank to EFOP-3.6.1-16-2016-00017 1 ‘Internationalisation, initiatives to establish a new source of researchers and graduates, and development of knowledge and technological transfer as instruments of intelligent specialisations at Széchenyi István University’ for the support of the research. This work was supported by National Research, Development and Innovation Office (NKFIH) K124055. Supported by the ÚNKP-18-3 New National Excellence Program of the Ministry of Human Capacities.

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Authors and Affiliations

  1. Department of Information Technology, Széchenyi István University, Győr, Hungary

    Boldizsár Tüű-Szabó & László T. Kóczy

  2. Department of Logistics, Széchenyi István University, Győr, Hungary

    Péter Földesi

  3. Department of Telecommunications and Media Informatics, Budapest University of Technology and Economics, Budapest, Hungary

    László T. Kóczy

Authors
  1. Boldizsár Tüű-Szabó
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  2. Péter Földesi
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  3. László T. Kóczy
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Corresponding author

Correspondence to Boldizsár Tüű-Szabó .

Editor information

Editors and Affiliations

  1. Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, Krakow, Poland

    Piotr Kulczycki

  2. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

  3. Faculty of Engineering Sciences, Széchenyi István University, Győr, Hungary

    László T. Kóczy

  4. Faculty of Civil Engineering, Slovak University of Technology, Bratislava, Slovakia

    Radko Mesiar

  5. Department of Electronic Systems, Aalborg University, Aalborg, Denmark

    Rafal Wisniewski

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Tüű-Szabó, B., Földesi, P., Kóczy, L.T. (2020). A Memetic Version of the Bacterial Evolutionary Algorithm for Discrete Optimization Problems. In: Kulczycki, P., Kacprzyk, J., Kóczy, L., Mesiar, R., Wisniewski, R. (eds) Information Technology, Systems Research, and Computational Physics. ITSRCP 2018. Advances in Intelligent Systems and Computing, vol 945. Springer, Cham. https://doi.org/10.1007/978-3-030-18058-4_4

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