### Abstract

In recent years a large number of evolutionary and other population based heuristics were proposed in the literature for solving NP-hard optimization problems. In 2015 we presented a Discrete Bacterial Memetic Evolutionary Algorithm (DBMEA) for The Traveling Salesman Problem. It provided results tested on series of TSP problems. In this paper we present an improved version of the DBMEA algorithm, where the local search is accelerated, which is the most time consuming part of the original DBMEA algorithm. This modification led to a significant improvement, the runtime of the improved DBMEA was 5– 20 times shorter than the original DBMEA algorithm. Our DBMEA algorithms calculate real value costs better than integer ones, so we modified the Concorde algorithm be comparable with our results. The improved DBMEA was tested on several TSPLIB benchmark problems and other VLSI benchmark problems and the following values were compared: - optima found by the improved DBMEA heuristic and by the modified Concorde algorithm with real cost values - runtimes of original DBMEA, improved DBMEA and modified Concorde algorithm. Based on the test results we suggest the use of the improved DBMEA heuristic for the more efficient solution of TSP problems.

Original language | English |
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Title of host publication | Computational Intelligence in Information Systems - Proceedings of the Computational Intelligence in Information Systems Conference, CIIS 2016 |

Publisher | Springer Verlag |

Pages | 27-38 |

Number of pages | 12 |

Volume | 532 |

ISBN (Print) | 9783319485164 |

DOIs | |

Publication status | Published - 2017 |

Event | International Conference on Computational Intelligence in Information Systems, CIIS 2016 - Gadong, Brunei Darussalam Duration: Nov 18 2016 → Nov 20 2016 |

### Publication series

Name | Advances in Intelligent Systems and Computing |
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Volume | 532 |

ISSN (Print) | 21945357 |

### Other

Other | International Conference on Computational Intelligence in Information Systems, CIIS 2016 |
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Country | Brunei Darussalam |

City | Gadong |

Period | 11/18/16 → 11/20/16 |

### Fingerprint

### Keywords

- Discrete optimization
- Memetic algorithm
- Traveling Salesman Problem

### ASJC Scopus subject areas

- Control and Systems Engineering
- Computer Science(all)

### Cite this

*Computational Intelligence in Information Systems - Proceedings of the Computational Intelligence in Information Systems Conference, CIIS 2016*(Vol. 532, pp. 27-38). (Advances in Intelligent Systems and Computing; Vol. 532). Springer Verlag. https://doi.org/10.1007/978-3-319-48517-1_3

**Improved discrete bacterial memetic evolutionary algorithm for the traveling salesman problem.** / Tüű-Szabó, Boldizsár; Földesi, Péter; Kóczy, L.

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

*Computational Intelligence in Information Systems - Proceedings of the Computational Intelligence in Information Systems Conference, CIIS 2016.*vol. 532, Advances in Intelligent Systems and Computing, vol. 532, Springer Verlag, pp. 27-38, International Conference on Computational Intelligence in Information Systems, CIIS 2016, Gadong, Brunei Darussalam, 11/18/16. https://doi.org/10.1007/978-3-319-48517-1_3

}

TY - GEN

T1 - Improved discrete bacterial memetic evolutionary algorithm for the traveling salesman problem

AU - Tüű-Szabó, Boldizsár

AU - Földesi, Péter

AU - Kóczy, L.

PY - 2017

Y1 - 2017

N2 - In recent years a large number of evolutionary and other population based heuristics were proposed in the literature for solving NP-hard optimization problems. In 2015 we presented a Discrete Bacterial Memetic Evolutionary Algorithm (DBMEA) for The Traveling Salesman Problem. It provided results tested on series of TSP problems. In this paper we present an improved version of the DBMEA algorithm, where the local search is accelerated, which is the most time consuming part of the original DBMEA algorithm. This modification led to a significant improvement, the runtime of the improved DBMEA was 5– 20 times shorter than the original DBMEA algorithm. Our DBMEA algorithms calculate real value costs better than integer ones, so we modified the Concorde algorithm be comparable with our results. The improved DBMEA was tested on several TSPLIB benchmark problems and other VLSI benchmark problems and the following values were compared: - optima found by the improved DBMEA heuristic and by the modified Concorde algorithm with real cost values - runtimes of original DBMEA, improved DBMEA and modified Concorde algorithm. Based on the test results we suggest the use of the improved DBMEA heuristic for the more efficient solution of TSP problems.

AB - In recent years a large number of evolutionary and other population based heuristics were proposed in the literature for solving NP-hard optimization problems. In 2015 we presented a Discrete Bacterial Memetic Evolutionary Algorithm (DBMEA) for The Traveling Salesman Problem. It provided results tested on series of TSP problems. In this paper we present an improved version of the DBMEA algorithm, where the local search is accelerated, which is the most time consuming part of the original DBMEA algorithm. This modification led to a significant improvement, the runtime of the improved DBMEA was 5– 20 times shorter than the original DBMEA algorithm. Our DBMEA algorithms calculate real value costs better than integer ones, so we modified the Concorde algorithm be comparable with our results. The improved DBMEA was tested on several TSPLIB benchmark problems and other VLSI benchmark problems and the following values were compared: - optima found by the improved DBMEA heuristic and by the modified Concorde algorithm with real cost values - runtimes of original DBMEA, improved DBMEA and modified Concorde algorithm. Based on the test results we suggest the use of the improved DBMEA heuristic for the more efficient solution of TSP problems.

KW - Discrete optimization

KW - Memetic algorithm

KW - Traveling Salesman Problem

UR - http://www.scopus.com/inward/record.url?scp=84994876204&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84994876204&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-48517-1_3

DO - 10.1007/978-3-319-48517-1_3

M3 - Conference contribution

SN - 9783319485164

VL - 532

T3 - Advances in Intelligent Systems and Computing

SP - 27

EP - 38

BT - Computational Intelligence in Information Systems - Proceedings of the Computational Intelligence in Information Systems Conference, CIIS 2016

PB - Springer Verlag

ER -