期刊名称：Lecture Notes in Engineering and Computer Science
印刷版ISSN：2078-0958
电子版ISSN：2078-0966
出版年度：2018
卷号：2233&2234
页码：820-825
出版社：Newswood and International Association of Engineers
摘要：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.
