### Abstract

The multiple Traveling Salesman Problem (mTSP) is a complex combinatorial optimization problem, which is a generalization of the well-known Traveling Salesman Problem (TSP), where one or more salesmen can be used in the solution. The optimization task can be described as follows: given a fleet of vehicles, a common depot and several requests by the customers, find the set of routes with overall minimum route cost which service all the demands. Because of the fact that TSP is already a complex, namely an NP-complete problem, heuristic optimization algorithms, like genetic algorithms (GAs) need to take into account. The extension of classical GA tools for mTSP is not a trivial problem, it requires special, interpretable encoding to ensure efficiency. The aim of this paper is to review how genetic algorithms can be applied to solve these problems and propose a novel, easily interpretable representation based GA.

Original language | English |
---|---|

Title of host publication | Studies in Computational Intelligence |

Pages | 141-151 |

Number of pages | 11 |

Volume | 313 |

DOIs | |

Publication status | Published - 2010 |

### Publication series

Name | Studies in Computational Intelligence |
---|---|

Volume | 313 |

ISSN (Print) | 1860949X |

### Fingerprint

### Keywords

- genetic algorithm
- mTSP
- multi-chromosome
- optimization
- VRP

### ASJC Scopus subject areas

- Artificial Intelligence

### Cite this

*Studies in Computational Intelligence*(Vol. 313, pp. 141-151). (Studies in Computational Intelligence; Vol. 313). https://doi.org/10.1007/978-3-642-15220-7_12

**A novel approach to solve multiple traveling salesmen problem by genetic algorithm.** / Király, András; Abonyi, J.

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

*Studies in Computational Intelligence.*vol. 313, Studies in Computational Intelligence, vol. 313, pp. 141-151. https://doi.org/10.1007/978-3-642-15220-7_12

}

TY - CHAP

T1 - A novel approach to solve multiple traveling salesmen problem by genetic algorithm

AU - Király, András

AU - Abonyi, J.

PY - 2010

Y1 - 2010

N2 - The multiple Traveling Salesman Problem (mTSP) is a complex combinatorial optimization problem, which is a generalization of the well-known Traveling Salesman Problem (TSP), where one or more salesmen can be used in the solution. The optimization task can be described as follows: given a fleet of vehicles, a common depot and several requests by the customers, find the set of routes with overall minimum route cost which service all the demands. Because of the fact that TSP is already a complex, namely an NP-complete problem, heuristic optimization algorithms, like genetic algorithms (GAs) need to take into account. The extension of classical GA tools for mTSP is not a trivial problem, it requires special, interpretable encoding to ensure efficiency. The aim of this paper is to review how genetic algorithms can be applied to solve these problems and propose a novel, easily interpretable representation based GA.

AB - The multiple Traveling Salesman Problem (mTSP) is a complex combinatorial optimization problem, which is a generalization of the well-known Traveling Salesman Problem (TSP), where one or more salesmen can be used in the solution. The optimization task can be described as follows: given a fleet of vehicles, a common depot and several requests by the customers, find the set of routes with overall minimum route cost which service all the demands. Because of the fact that TSP is already a complex, namely an NP-complete problem, heuristic optimization algorithms, like genetic algorithms (GAs) need to take into account. The extension of classical GA tools for mTSP is not a trivial problem, it requires special, interpretable encoding to ensure efficiency. The aim of this paper is to review how genetic algorithms can be applied to solve these problems and propose a novel, easily interpretable representation based GA.

KW - genetic algorithm

KW - mTSP

KW - multi-chromosome

KW - optimization

KW - VRP

UR - http://www.scopus.com/inward/record.url?scp=78049303195&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=78049303195&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-15220-7_12

DO - 10.1007/978-3-642-15220-7_12

M3 - Chapter

AN - SCOPUS:78049303195

SN - 9783642152191

VL - 313

T3 - Studies in Computational Intelligence

SP - 141

EP - 151

BT - Studies in Computational Intelligence

ER -