### Abstract

It is known that the Ant Colony Optimization (ACO) inspired from the collective behavior of real ants, and it is effective to find a better solution for the Traveling Salesman Problem (TSP). Rank based Ant System AS
_{rank}
has been proposed as a developed version of basic Ant System. In the algorithm of AS
_{rank}
, each agent in Ant System is ranked from the viewpoint outside the system as to the participation in pheromone update. Then, in spite of the fact that the collective behavior of real ants has inspired in constructing the algorithm of Ant System, AS
_{rank}
as a developed version includes the viewpoint outside the system that does not exist in the actual ants’ swarm. Furthermore, there is a problem that it tends to be easy to fall into a local solution. In our study, we introduce the behavior observed in real ants’ experiments in order to construct a new algorithm of Ant System. That is, each ant agent in Ant System estimates its own rank by interaction with encountered agents to determine whether it should contribute to pheromone deposition. Therefore, we carried out exploring simulations in several TSP datasets, and we will show some analysis results that indicate the proposed model has superiority than AS
_{rank}
.

Original language | English |
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Title of host publication | Proceedings of the International MultiConference of Engineers and Computer Scientists 2018, IMECS 2018 |

Editors | Oscar Castillo, David Dagan Feng, A.M. Korsunsky, Craig Douglas, S. I. Ao |

Publisher | Newswood Limited |

ISBN (Electronic) | 9789881404886 |

Publication status | Published - Jan 1 2018 |

Event | 2018 International MultiConference of Engineers and Computer Scientists, IMECS 2018 - Hong Kong, Hong Kong Duration: Mar 14 2018 → Mar 16 2018 |

### Publication series

Name | Lecture Notes in Engineering and Computer Science |
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Volume | 2 |

ISSN (Print) | 2078-0958 |

### Conference

Conference | 2018 International MultiConference of Engineers and Computer Scientists, IMECS 2018 |
---|---|

Country | Hong Kong |

City | Hong Kong |

Period | 3/14/18 → 3/16/18 |

### Fingerprint

### ASJC Scopus subject areas

- Computer Science (miscellaneous)

### Cite this

*Proceedings of the International MultiConference of Engineers and Computer Scientists 2018, IMECS 2018*(Lecture Notes in Engineering and Computer Science; Vol. 2). Newswood Limited.

**Proposal of new ant system based on consistency and discrepancy of subjective ranking.** / Uneme, Kotaro; Sakiyama, Tomoko; Arizono, Ikuo.

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

*Proceedings of the International MultiConference of Engineers and Computer Scientists 2018, IMECS 2018.*Lecture Notes in Engineering and Computer Science, vol. 2, Newswood Limited, 2018 International MultiConference of Engineers and Computer Scientists, IMECS 2018, Hong Kong, Hong Kong, 3/14/18.

}

TY - GEN

T1 - Proposal of new ant system based on consistency and discrepancy of subjective ranking

AU - Uneme, Kotaro

AU - Sakiyama, Tomoko

AU - Arizono, Ikuo

PY - 2018/1/1

Y1 - 2018/1/1

N2 - It is known that the Ant Colony Optimization (ACO) inspired from the collective behavior of real ants, and it is effective to find a better solution for the Traveling Salesman Problem (TSP). Rank based Ant System AS rank has been proposed as a developed version of basic Ant System. In the algorithm of AS rank , each agent in Ant System is ranked from the viewpoint outside the system as to the participation in pheromone update. Then, in spite of the fact that the collective behavior of real ants has inspired in constructing the algorithm of Ant System, AS rank as a developed version includes the viewpoint outside the system that does not exist in the actual ants’ swarm. Furthermore, there is a problem that it tends to be easy to fall into a local solution. In our study, we introduce the behavior observed in real ants’ experiments in order to construct a new algorithm of Ant System. That is, each ant agent in Ant System estimates its own rank by interaction with encountered agents to determine whether it should contribute to pheromone deposition. Therefore, we carried out exploring simulations in several TSP datasets, and we will show some analysis results that indicate the proposed model has superiority than AS rank .

AB - It is known that the Ant Colony Optimization (ACO) inspired from the collective behavior of real ants, and it is effective to find a better solution for the Traveling Salesman Problem (TSP). Rank based Ant System AS rank has been proposed as a developed version of basic Ant System. In the algorithm of AS rank , each agent in Ant System is ranked from the viewpoint outside the system as to the participation in pheromone update. Then, in spite of the fact that the collective behavior of real ants has inspired in constructing the algorithm of Ant System, AS rank as a developed version includes the viewpoint outside the system that does not exist in the actual ants’ swarm. Furthermore, there is a problem that it tends to be easy to fall into a local solution. In our study, we introduce the behavior observed in real ants’ experiments in order to construct a new algorithm of Ant System. That is, each ant agent in Ant System estimates its own rank by interaction with encountered agents to determine whether it should contribute to pheromone deposition. Therefore, we carried out exploring simulations in several TSP datasets, and we will show some analysis results that indicate the proposed model has superiority than AS rank .

UR - http://www.scopus.com/inward/record.url?scp=85062593843&partnerID=8YFLogxK

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M3 - Conference contribution

AN - SCOPUS:85062593843

T3 - Lecture Notes in Engineering and Computer Science

BT - Proceedings of the International MultiConference of Engineers and Computer Scientists 2018, IMECS 2018

A2 - Castillo, Oscar

A2 - Feng, David Dagan

A2 - Korsunsky, A.M.

A2 - Douglas, Craig

A2 - Ao, S. I.

PB - Newswood Limited

ER -