Reference
Z. Cong, B. De Schutter, and R. Babuška, "On the convergence of ant
colony optimization with stench pheromone,"
2013 IEEE Congress
on Evolutionary Computation, Cancún, Mexico, pp. 1876-1883, June
2013.
Abstract
Ant Colony Optimization (ACO) has proved to be a powerful metaheuristic for
combinatorial optimization problems. From a theoretical point of view, the
convergence of the ACO algorithm is an important issue. In this paper, we
analyze the convergence properties of a recently introduced ACO algorithm,
called ACO with stench pheromone (SACO), which can be used to solve dynamic
traffic routing problems through finding the minimum cost routes in a traffic
network. This new algorithm has two different types of pheromone: the regular
pheromone that is used to attract artificial ants to the arc in the network
with the lowest cost, and the stench pheromone that is used to push ants away
when too many ants converge to that arc. As a first step of a convergence proof
for SACO, we consider a network with two arcs. We show that the process of
pheromone update will transit among different modes, and finally stay in a
stable mode, thus proving convergence for this given case.
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BibTeX
@inproceedings{ConDeS:13-019,
author = {Cong, Zhe and De Schutter, Bart and Babu{\v{s}}ka, Robert},
title = {On the Convergence of Ant Colony Optimization with Stench
Pheromone},
booktitle = {2013 IEEE Congress on Evolutionary Computation},
address = {Canc\'un, Mexico},
pages = {1876--1883},
month = jun,
year = {2013}
}