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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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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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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