Runtime Analysis of an Ant Colony Optimization Algorithm for TSP Instances

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...

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Bibliographic Details
Published in:	IEEE transactions on evolutionary computation Vol. 13; no. 5; pp. 1083 - 1092
Main Author:	Yuren Zhou, Yuren Zhou
Format:	Journal Article
Language:	English
Published:	New York, NY IEEE 01.10.2009 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithm design and analysis Algorithmics. Computability. Computer arithmetics Algorithms Ant colony optimization Ant colony optimization (ACO) Applied sciences Artificial intelligence Benchmarking Biological system modeling Colonies Combinatorial analysis Computer science; control theory; systems Evolution (biology) Evolutionary algorithms Evolutionary computation Exact sciences and technology Flows in networks. Combinatorial problems Graphs heuristic algorithm Heuristic algorithms Learning and adaptive systems Logistics Operational research and scientific management Operational research. Management science Optimization Particle swarm optimization Partitioning algorithms Runtime runtime analysis Theoretical computing Traveling salesman problem traveling salesman problem (TSP) Traveling salesman problems Insecta Evolutionary algorithm Travelling salesman problem Combinatorial optimization Formicoidea heuristic algorithm Swarm intelligence Ant colony optimization (ACO) Aculeata Mathematical programming Probabilistic approach Complete graph Pheromone Social insect Particle swarm optimization Arthropoda Heuristic method Minimax method Problem solving runtime analysis Visibility Hymenoptera Invertebrata Algorithm analysis traveling salesman problem (TSP)
ISSN:	1089-778X, 1941-0026
Online Access:	Get full text
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