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Extremal Optimisation with a Penalty Approach for the Multidimensional Knapsack Problem

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Book cover Simulated Evolution and Learning (SEAL 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5361))

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Abstract

The extremal optimisation (EO) meta-heuristic is a recent form of search that is suitable for combinatorial optimisation problems. EO has been applied to problems such as graph partitioning, spin glass, and graph colouring. However, only a relatively small amount of work has been done on other combinatorial problems particularly those having constraints. This paper examines the issue of satisfying constraints with a penalty approach using the multidimensional knapsack problem. An EO model is presented which finds solutions through the analysis of the number of overloaded constraints. This approach allows the solution state move between feasible and infeasible spaces. The results show that the new algorithm is able to obtain optimal results for small problems and finds competitive solutions for large problems.

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Author information

Authors and Affiliations

  1. School of Information Technology, Bond University, QLD 4229, Australia

    Pedro Gómez-Meneses & Marcus Randall

  2. Universidad Católica de la Santísima Concepción, Concepción, Chile

    Pedro Gómez-Meneses

Authors
  1. Pedro Gómez-Meneses
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  2. Marcus Randall
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Editor information

Editors and Affiliations

  1. School of Computer Science and information Technology, RMIT University, VIC 3001, Melbourne, Australia

    Xiaodong Li

  2. Melbourne School Engineering, Department of Computer Science and Software Engineering, The University of Melbourne, VIC 3001, Melbourne, Australia

    Michael Kirley

  3. School of Mathematics, Statistics and Computer Science, Victoria University of Wellington, P.O. Box 600, 6140, Wellington, New Zealand

    Mengjie Zhang

  4. Faculty of Information Technology, Monash University, Clayton Campus, VIC 3800, Australia

    David Green

  5. School of Computer Science and Information Technology, RMIT University, VIC 3001, Melbourne, Australia

    Vic Ciesielski

  6. School of Information Technology and Electrical Engineering, Australian Defence Force Academy, University of New South Wales, Canberra, Australia

    Hussein Abbass

  7. School of Computer Science, University of Adelaide, SA 5005, Adelaide, Australia

    Zbigniew Michalewicz

  8. Faculty of Information and Communication Technologies, Swinburne University of Technology, Melbourne, Australia

    Tim Hendtlass

  9. Indian Institute of Technology Kanpur, Department of Mechanical Engineering, Kanpur Genetic Algorithms Laboratory (KanGAL), 208016, Kanpur, Uttar Pradesh, India

    Kalyanmoy Deb

  10. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117576, Singapore

    Kay Chen Tan

  11. Institute AIFB, University of Karlsruhe, 76128, Karlsruhe, Germany

    Jürgen Branke

  12. Xi’an Jiaotong-Liverpool University, 215123, Suzhoum, China

    Yuhui Shi

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© 2008 Springer-Verlag Berlin Heidelberg

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Gómez-Meneses, P., Randall, M. (2008). Extremal Optimisation with a Penalty Approach for the Multidimensional Knapsack Problem. In: Li, X., et al. Simulated Evolution and Learning. SEAL 2008. Lecture Notes in Computer Science, vol 5361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89694-4_24

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  • DOI: https://doi.org/10.1007/978-3-540-89694-4_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89693-7

  • Online ISBN: 978-3-540-89694-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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