Crowding Population-based Ant Colony Optimisation for the Multi-objective Travelling Salesman Problem

Ant inspired algorithms have gained popularity for use in multi-objective problem domains. One specific algorithm, Population-based ACO, which uses a population as well as the traditional pheromone matrix, has been shown to be effective at solving combinatorial multi-objective optimisation problems....

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Bibliographic Details
Published in:2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision Making, Honolulu, HI, 1-5 April 2007 pp. 333 - 340
Main Author: Angus, D.
Format: Conference Proceeding
Language:English
Published: IEEE 01.04.2007
Subjects:
Ant colony optimization
Communications technology
Competitive intelligence
Computational intelligence
Decision making
Information technology
Intelligent systems
Laboratories
Testing
Traveling salesman problems
ISBN:9781424407026, 1424407028
Online Access:Get full text
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Summary:Ant inspired algorithms have gained popularity for use in multi-objective problem domains. One specific algorithm, Population-based ACO, which uses a population as well as the traditional pheromone matrix, has been shown to be effective at solving combinatorial multi-objective optimisation problems. This paper extends the population-based ACO algorithm with a crowding population replacement scheme to increase the search efficacy and efficiency. Results are shown for a suite of multi-objective travelling salesman problems of varying complexity
ISBN:9781424407026
1424407028
DOI:10.1109/MCDM.2007.369110