TY  - RPRT
ID  - preusse2009studienarbeit
T1  - Determination of Parameter Settings of Ant Colony System for the Traveling Salesman Problem
A1  - Preusse, Julia
Y1  - 2009
T2  - Otto-von-Guericke-Universität
AD  - Magdeburg
N2  - Many computational problems are too complex to be practically solved to
proven optimality. This is one of the main reasons why heuristics  algorithms
using problem specic knowledge to compute near-optimal solutions  became
very popular. Ant Colony Optimization (ACO) is one of the most successful
heuristics for the Traveling Salesman Problem (TSP), which is to nd a shortest
tour through a given list of cities. Unfortunately we cannot use the full
potential of ACO algorithms if we cannot determine good parameter settings.
Commonly, algorithms use a standard parameter setting, which might be good
for many problems. Nevertheless, there are problems for which better settings
can be found. This is why we dedicated this work to the search for measures
helping us to characterize TSP instances to derive appropriate parameter settings
for ACO. We have developed new adaptive settings which have been
shown experimentally to be better than the standard version.
ER  -