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Title

Crowding population-based ant colony optimisation for the multi-objective travelling salesman problem

Author(s)

Angus, Daniel

Abstract

Ant inspired algorithms have recently 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.

Publication type

Conference paper

Research centre

Swinburne University of Technology. Faculty of Information and Communication Technologies. Centre for Intelligent Systems and Complex Processes

Source

Proceedings 2007 IEEE Symposium on Computational Intelligence in Multicriteria Decision Making (MCDM 2007), Honolulu, Hawaii, United States, 01-05 April 2007, p. 333-340

Publication year

2007

Keyword(s)

ACO;  Ant-inspired algorithms;  Ant colony optimisation;  Artificial intelligence;  Travelling Salesman Problems

Publisher

IEEE

ISBN

9781424407026

Publisher URL

http://dx.doi.org/10.1109/MCDM.2007.369110

Copyright

Copyright © 2007 IEEE. Paper reproduced here in accordance with the copyright policy of the publisher. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

Additional information

This paper received a Best Paper award at the 2007 IEEE Symposium on Computational Intelligence in Multicriteria Decision Making.

Full text

Peer reviewed