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Population-Based Ant Colony Optimisation for Multi-objective Function Optimisation

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Progress in Artificial Life (ACAL 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4828))

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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.

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Authors and Affiliations

  1. Complex Intelligent Systems Laboratory, Centre for Information Technology Research, Faculty of Information and Communication Technologies, Swinburne University of Technology, Melbourne, 3122, Australia

    Daniel Angus

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  1. Daniel Angus
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Marcus Randall Hussein A. Abbass Janet Wiles

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Angus, D. (2007). Population-Based Ant Colony Optimisation for Multi-objective Function Optimisation. In: Randall, M., Abbass, H.A., Wiles, J. (eds) Progress in Artificial Life. ACAL 2007. Lecture Notes in Computer Science(), vol 4828. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76931-6_21

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  • DOI: https://doi.org/10.1007/978-3-540-76931-6_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-76930-9

  • Online ISBN: 978-3-540-76931-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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