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Screening the parameters affecting heuristic performance

Published: 07 July 2007 Publication History

Abstract

This research screens the tuning parameters of a combinatorial optimization heuristic. Specifically, it presents a Design of Experiments (DOE) approach that uses a Fractional Factorial Design to screen the tuning parameters of Ant Colony System (ACS) for the Travelling Sales person problem. Screening is a preliminary step towards building a full Response Surface Model (RSM) [2]. It identifies parametersthat have little influence on performance and can be omittedfrom the RSM design. This reduces the complexity andexpense of the RSM design. 10 algorithm parameters and 2 problem characteristics are considered. Open questionson the effect of 3 parameters on performance are answered.A further parameter, sometimes assumed important, was shown to have no effect on performance. A new problem characteristic that effects performance was identified. A full version of this paper is available [3].

References

[1]
M. Dorigo and T. Stutzle. Ant Colony Optimization. The MIT Press, Massachusetts, USA, 2004.
[2]
E. Ridge and D. Kudenko. Analyzing Heuristic Performance with Response Surface Models: Prediction, Optimization and Robustness. In Proceedings of the Genetic and Evolutionary Computation Conference. ACM, 2007.
[3]
E. Ridge and D. Kudenko. Screening the parameters affecting heuristic performance. Technical Report YCS 415 (www.cs.york.ac.uk/ftpdir/reports/index.php), The Department of Computer Science, The University of York, April 2007.

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    cover image ACM Conferences
    GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
    July 2007
    2313 pages
    ISBN:9781595936974
    DOI:10.1145/1276958

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 07 July 2007

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    Author Tags

    1. ant colony
    2. design of experiments
    3. parameter screening

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    GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
    Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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    • (2019)Electric fish optimization: a new heuristic algorithm inspired by electrolocationNeural Computing and Applications10.1007/s00521-019-04641-8Online publication date: 5-Dec-2019
    • (2014)Design of ExperimentsDesign of Experiments for Reinforcement Learning10.1007/978-3-319-12197-0_3(53-66)Online publication date: 23-Nov-2014
    • (2012)Investigation on the Effects of ACO Parameters for Feature Selection and ClassificationAdvances in Communication, Network, and Computing10.1007/978-3-642-35615-5_20(136-145)Online publication date: 2012
    • (2010)Design and Analysis of Computational Experiments: OverviewExperimental Methods for the Analysis of Optimization Algorithms10.1007/978-3-642-02538-9_3(51-72)Online publication date: 26-Oct-2010
    • (2007)Analyzing heuristic performance with response surface modelsProceedings of the 9th annual conference on Genetic and evolutionary computation10.1145/1276958.1276979(150-157)Online publication date: 7-Jul-2007

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