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An Improved Hybrid Genetic Algorithm: New Results for the Quadratic Assignment Problem

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Book cover Research and Development in Intelligent Systems XX (SGAI 2003)

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Abstract

Genetic algorithms (GAs) have been proven to be among the most powerful intelligent techniques in various areas of the computer science, including difficult optimization problems. In this paper, we propose an improved hybrid genetic algorithm (IHGA). It uses a robust local improvement procedure (a limited iterated tabu search (LITS)) as well as an effective restart (diversification) mechanism that is based on so-called “shift mutations”. IHGA has been applied to the well-known combinatorial optimization problem, the quadratic assignment problem (QAP). The results obtained from the numerous experiments on different QAP instances from the instances library QAPLIB show that the proposed algorithm appears to be superior to other modem heuristic approaches that are among the best algorithms for the QAP. The high efficiency of our algorithm is also corroborated by the fact that the new, recordbreaking solutions were obtained for a number of large real-life instances.

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Author information

Authors and Affiliations

  1. Department of Practical Informatics, Kaunas University of Technology, Studentų St. 50-400a, LT-3031, Kaunas, Lithuania

    Alfonsas Misevicius

Authors
  1. Alfonsas Misevicius
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Liverpool, Liverpool, UK

    Frans Coenen

  2. Dept of Computer Science, University of Aberdeen, Aberdeen, UK

    Alun Preece

  3. Napier University, International Teledemocracy Centre, Edinburgh, EH10 5DT, UK

    Ann Macintosh BSc, CEng

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© 2004 Springer-Verlag London

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Misevicius, A. (2004). An Improved Hybrid Genetic Algorithm: New Results for the Quadratic Assignment Problem. In: Coenen, F., Preece, A., Macintosh, A. (eds) Research and Development in Intelligent Systems XX. SGAI 2003. Springer, London. https://doi.org/10.1007/978-0-85729-412-8_1

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  • DOI: https://doi.org/10.1007/978-0-85729-412-8_1

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-85233-780-3

  • Online ISBN: 978-0-85729-412-8

  • eBook Packages: Springer Book Archive

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