Runtime Analysis of an Ant Colony Optimization Algorithm for TSP Instances
Yuren Zhou
Sch. of Comput. Sci. & Eng., South China Univ. of Technol., Guangzhou, China;
This paper appears in: Evolutionary Computation, IEEE Transactions on
Publication Date: Oct. 2009
Volume: 13,
Issue: 5
On page(s): 1083-1092
ISSN: 1089-778X
INSPEC Accession Number: 10879793
Digital Object Identifier: 10.1109/TEVC.2009.2016570
First Published: 2009-08-11
Current Version Published: 2009-09-22
Abstract
Ant colony optimization (ACO) is a relatively new random heuristic approach for solving optimization problems. The main application of the ACO algorithm lies in the field of combinatorial optimization, and the traveling salesman problem (TSP) is the first benchmark problem to which the ACO algorithm has been applied. However, relatively few results on the runtime analysis of the ACO on the TSP are available. This paper presents the first rigorous analysis of a simple ACO algorithm called (1 + 1) MMAA (Max-Min ant algorithm) on the TSP. The expected runtime bounds for (1 + 1) MMAA on two TSP instances of complete and non-complete graphs are obtained. The influence of the parameters controlling the relative importance of pheromone trail versus visibility is also analyzed, and their choice is shown to have an impact on the expected runtime.
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