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

N1 - Funding Information:
This work was supported by Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Number 17K01266.

PY - 2018

Y1 - 2018

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 ASrank has been proposed as a developed version of basic Ant System. In the algorithm of ASrank, 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, ASrank 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 ASrank.

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 ASrank has been proposed as a developed version of basic Ant System. In the algorithm of ASrank, 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, ASrank 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 ASrank.

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

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

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

T2 - 2018 International MultiConference of Engineers and Computer Scientists, IMECS 2018

Y2 - 14 March 2018 through 16 March 2018

ER -