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The ant colony metaphor for searching continuous design spaces

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Evolutionary Computing (AISB EC 1995)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 993))

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Abstract

This paper describes a form of dynamical computational system—the ant colony—and presents an ant colony model for continuous space optimisation problems. The ant colony metaphor is applied to a real world heavily constrained engineering design problem. It is capable of accelerating the search process and finding acceptable solutions which otherwise could not be discovered by a GA. By integrating the Pareto optimality concept within the selection mechanism in GAs and Ant Colony it is possible to treat both hard and soft constraints. Hard constraints participate in a penalty term while soft constraints become part of a multi-criteria formulation of the problem.

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References

  • D.E.Rumelhart and J.L.McClelland, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Volume 1, Bradford Books, Cambridge, MA

    Google Scholar 

  • Deniz Yuret, and Michael de la Maza, Dynamic Hill Climbing: Overcoming the limitations of optimization techniques, Technical report, Numinous Noetics Group, Artificial Intelligence Laboratory, MIT, MA 02139

    Google Scholar 

  • George Bilchev, Evolutionary Algorithms for the Bin-packing Problem, Chapter 4, Comparison between Ant Colony Search and Genetic Algorithms, MSc thesis, 1994, New Bulgarian University, Sofia 1125, Bulgaria (in Bulgarian)

    Google Scholar 

  • George Bilchev, and I.C. Parmee, Searching Heavily Constrained Design Spaces, Procs. of the 22nd International Conference on CAD-95, 8–13 May, 1995, Yalta, Ukraine.

    Google Scholar 

  • George Bilchev, and I.C. Parmee, Natural Self-organizing Systems, Internal Report PEDC-03-95, Engineering Design Centre, University of Plymouth, UK

    Google Scholar 

  • Gerard Weisbuch, Complex Systems Dynamics, Lecture Notes Volume II, Santa Fe Institute, Studies in the Sciences of Complexity, Addison-Wesley, 1991

    Google Scholar 

  • I.C.Parmee, and G.Purchase, The Development of a Directed Genetic Search Technique for Heavily Constrained Design Spaces, in Proc. of Adaptive Computing in Engineering Design and Control, Plymouth, 1994.

    Google Scholar 

  • I.C. Parmee and M.J. Denham, Emergent Computing Methods in Engineering Design, NATO Advanced Research Workshop, Nafplio, Greece, August 1994.

    Google Scholar 

  • J.H.Holland, Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications in Biology, Control, and Artificial Intelligence, University of Michigan Press, Ann Arbor, 1975

    Google Scholar 

  • J.Torreele, Optimization by Simulated Annealing. Introduction and Case Study, AI-MEMO 88-18, AI-lab VUB, Brussles

    Google Scholar 

  • Jeffray Horn, N. Nafpliotis, and D.E.Goldberg, A Niched Pareto Genetic Algorithm for multiobjective optimization, in Proceedings of the First IEEE Conference on Evolutionary Computation, IEEE World Congress on Computational Intelligence, Volume 1, 1994, Piscataway, NJ

    Google Scholar 

  • Jeffrey Horn, David E. Goldberg, and Kalyanmoy Deb, Long Path Problems, Proceedings of Parallel Problem Solving from Nature, Lecture Notes in Computer Science, Springer-Verlag, 1995

    Google Scholar 

  • L.Nadel and D.Stein, eds., Better than the Best: The Power of Cooperation, Complex Systems, 163–184, Addison-Wesley 1993

    Google Scholar 

  • L.Steels, Artificial Intelligence and Complex Systems, AI-MEMO 88-2, AI-lab VUB, Brussels

    Google Scholar 

  • Marco Dorigo, The Ant Cycle Algorithm, Technical report, Free University of Brussels

    Google Scholar 

  • P.J.Courtois. On line and Space Decomposition of Complex Structures, Comm. of the ACM, Vol.28, no.6

    Google Scholar 

  • S.Kirkpatrick, Gelatt C.D., M.P.Vechi, Optimisation by Simulated Annealing, Science, Vol.220, No.4598, May, 1983

    Google Scholar 

  • Scott H. Clearwater, Bernardo A. Huberman, and Tad Hogg, Cooperative Problem Solving, in B. Huberman, editor, Computation: The Micro and the Macro View, pages 33–70, World Scientific, Singapore, 1992

    Google Scholar 

  • W.Kinzel, Learning and Pattern Recognition in Spin Glass Models, 1985

    Google Scholar 

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Authors and Affiliations

  1. Plymouth Engineering Design Centre, University of Plymouth, USA

    G. Bilchev & I. C. Parmee

Authors
  1. G. Bilchev
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  2. I. C. Parmee
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Editor information

Terence C. Fogarty

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© 1995 Springer-Verlag Berlin Heidelberg

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Bilchev, G., Parmee, I.C. (1995). The ant colony metaphor for searching continuous design spaces. In: Fogarty, T.C. (eds) Evolutionary Computing. AISB EC 1995. Lecture Notes in Computer Science, vol 993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60469-3_22

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  • DOI: https://doi.org/10.1007/3-540-60469-3_22

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60469-3

  • Online ISBN: 978-3-540-47515-6

  • eBook Packages: Springer Book Archive

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