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Bi-Criterion Optimization with Multi Colony Ant Algorithms

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Evolutionary Multi-Criterion Optimization (EMO 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1993))

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Abstract

In this paper we propose a new approach to solve bi-criterion optimization problems with ant algorithms where several colonies of ants cooperate in finding good solutions. We introduce two methods for cooperation between the colonies and compare them with a multistart ant algorithm that corresponds to the case of no cooperation. Heterogeneous colonies are used in the algorithm, i.e. the ants differ in their preferences between the two criteria. Every colony uses two pheromone matrices — each suitable for one optimization criterion. As a test problem we use the Single Machine Total Tardiness problem with changeover costs.

Corresponding author. Part of this work was done while the author stayed at the Institute of Computer Science at the University of Hannover.

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Author information

Authors and Affiliations

  1. Institute AIFB, University of Karlsruhe, D-76128, Karlsruhe, Germany

    Steffen Iredi, Daniel Merkle & Martin Middendorf

Authors
  1. Steffen Iredi
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  2. Daniel Merkle
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  3. Martin Middendorf
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Editor information

Editors and Affiliations

  1. Department of Electrical Engineering Computer Engineering and Networks Laboratory, Swiss Federal Institute of Technology, ETH Zentrum, Gloriastrasse 35, 8092, Zurich, Switzerland

    Eckart Zitzler  & Lothar Thiele  & 

  2. Department of Mechanical Engineering Kanpur Genetic Algorithms Laboratory, Indian Institute of Technology, UP 208 016, Kanpur, India

    Kalyanmoy Deb

  3. Electrical Engineering Department,Computer Science Section Av.Instituto Politecnico Nacional No.2508, CINVESTAV-IPN, Col.San Pedro Zacatenco, 07300, Mexico City, Mexico

    Carlos Artemio Coello Coello

  4. Department of Computer Science, University of Reading, P.O.Box 225, Reading RG6 6AY, Whiteknights, UK

    David Corne

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© 2001 Springer-Verlag Berlin Heidelberg

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Iredi, S., Merkle, D., Middendorf, M. (2001). Bi-Criterion Optimization with Multi Colony Ant Algorithms. In: Zitzler, E., Thiele, L., Deb, K., Coello Coello, C.A., Corne, D. (eds) Evolutionary Multi-Criterion Optimization. EMO 2001. Lecture Notes in Computer Science, vol 1993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44719-9_25

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  • DOI: https://doi.org/10.1007/3-540-44719-9_25

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41745-3

  • Online ISBN: 978-3-540-44719-1

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