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Product Design Optimization Using Ant Colony And Bee Algorithms: A Comparison

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Algorithms from and for Nature and Life

Abstract

In recent years, heuristic algorithms, especially swarm intelligence algorithms, have become popular for product design, where problem formulations often are NP-hard (Socha and Dorigo, Eur J Oper Res 185:1155–1173, 2008). Swarm intelligence algorithms offer an alternative for large-scale problems to reach near-optimal solutions, without constraining the problem formulations immoderately (Albritton and McMullen, Eur J Oper Res 176:498–520 2007). In this paper, ant colony (Albritton and McMullen, Eur J Oper Res 176:498–520 2007) and bee colony algorithms (Karaboga and Basturk, J Glob Optim 39:459–471, 2007) are compared. Simulated conjoint data for different product design settings are used for this comparison, their generation uses a Monte Carlo design similar to the one applied in (Albritton and McMullen, Eur J Oper Res 176:498–520 2007). The purpose of the comparison is to provide an assistance, which algorithm should be applied in which product design setting.

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Authors and Affiliations

  1. Institute of Business Administration and Economics, Brandenburg University of Technology Cottbus, D-03013, Cottbus, Germany

    Sascha Voekler, Daniel Krausche & Daniel Baier

Authors
  1. Sascha Voekler
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  2. Daniel Krausche
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  3. Daniel Baier
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Corresponding author

Correspondence to Sascha Voekler .

Editor information

Editors and Affiliations

  1. University of Essex Department of Mathematical Sciences, Colchester, United Kingdom

    Berthold Lausen

  2. Ghent University Department of Marketing, Ghent, Belgium

    Dirk Van den Poel

  3. University of Marburg Databionics, FB 12, Marburg, Germany

    Alfred Ultsch

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Voekler, S., Krausche, D., Baier, D. (2013). Product Design Optimization Using Ant Colony And Bee Algorithms: A Comparison. In: Lausen, B., Van den Poel, D., Ultsch, A. (eds) Algorithms from and for Nature and Life. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-00035-0_50

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