### 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].

Original language | English |
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Title of host publication | Information Technology, Systems Research, and Computational Physics |

Editors | László T. Kóczy, Radko Mesiar, László T. Kóczy, Piotr Kulczycki, Piotr Kulczycki, Janusz Kacprzyk, Rafal Wisniewski |

Publisher | Springer Verlag |

Pages | 44-55 |

Number of pages | 12 |

ISBN (Print) | 9783030180577 |

DOIs | |

Publication status | Published - Jan 1 2020 |

Event | 3rd Conference on Information Technology, Systems Research and Computational Physics, ITSRCP 2018 - Krakow, Poland Duration: Jul 2 2018 → Jul 5 2018 |

### Publication series

Name | Advances in Intelligent Systems and Computing |
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Volume | 945 |

ISSN (Print) | 2194-5357 |

### Conference

Conference | 3rd Conference on Information Technology, Systems Research and Computational Physics, ITSRCP 2018 |
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Country | Poland |

City | Krakow |

Period | 7/2/18 → 7/5/18 |

### Fingerprint

### Keywords

- Time dependent
- Time windows
- Traveling Repairman Problem
- Traveling Salesman Problem

### ASJC Scopus subject areas

- Control and Systems Engineering
- Computer Science(all)

### Cite this

*Information Technology, Systems Research, and Computational Physics*(pp. 44-55). (Advances in Intelligent Systems and Computing; Vol. 945). Springer Verlag. https://doi.org/10.1007/978-3-030-18058-4_4

**A memetic version of the bacterial evolutionary algorithm for discrete optimization problems.** / Tüű-Szabó, Boldizsár; Földesi, Péter; Kóczy, L.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Information Technology, Systems Research, and Computational Physics.*Advances in Intelligent Systems and Computing, vol. 945, Springer Verlag, pp. 44-55, 3rd Conference on Information Technology, Systems Research and Computational Physics, ITSRCP 2018, Krakow, Poland, 7/2/18. https://doi.org/10.1007/978-3-030-18058-4_4

}

TY - GEN

T1 - A memetic version of the bacterial evolutionary algorithm for discrete optimization problems

AU - Tüű-Szabó, Boldizsár

AU - Földesi, Péter

AU - Kóczy, L.

PY - 2020/1/1

Y1 - 2020/1/1

N2 - 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].

AB - 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].

KW - Time dependent

KW - Time windows

KW - Traveling Repairman Problem

KW - Traveling Salesman Problem

UR - http://www.scopus.com/inward/record.url?scp=85065389887&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85065389887&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-18058-4_4

DO - 10.1007/978-3-030-18058-4_4

M3 - Conference contribution

SN - 9783030180577

T3 - Advances in Intelligent Systems and Computing

SP - 44

EP - 55

BT - Information Technology, Systems Research, and Computational Physics

A2 - Kóczy, László T.

A2 - Mesiar, Radko

A2 - Kóczy, László T.

A2 - Kulczycki, Piotr

A2 - Kulczycki, Piotr

A2 - Kacprzyk, Janusz

A2 - Wisniewski, Rafal

PB - Springer Verlag

ER -