Population-based ant colony optimisation for multi-objective function optimisation

Authors:
Daniel Angus
Affiliations:
Complex Intelligent Systems Laboratory, Centre for Information Technology Research, Faculty of Information and Communication Technologies, Swinburne University of Technology, Melbourne, Australia
Venue:
ACAL'07 Proceedings of the 3rd Australian conference on Progress in artificial life
Year:
2007

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Abstract

Ant inspired algorithms have recently gained popularity for use in multi-objective problem domains. The Population-based ACO, which uses a population of solutions as well as the traditional pheromone matrix, has been demonstrated as an effective problem solving strategy for solving combinatorial multi-objective optimisation problems, although this algorithm has yet to be applied to multi-objective function optimisation problems. This paper tests the suitability of a Population-based ACO algorithm for the multi-objective function optimisation problem. Results are given for a suite of problems of varying complexity.