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Advances in Metaheuristics for Hard Optimization pp 87–110Cite as

A Parallel Ant Colony Optimization Algorithm Based on Crossover Operation

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Part of the book series: Natural Computing Series ((NCS))

Abstract

In this work, we introduce a new parallel ant colony optimization algorithm based on an ant metaphor and the crossover operator from genetic algorithms.The performance of the proposed model is evaluated usingwell-known numerical test problems and then it is applied to train recurrent neural networks to identify linear and nonlinear dynamic plants. The simulation results are compared with results using other algorithms.

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Author information

Authors and Affiliations

  1. Kayseri Meslek Yuksekokulu, Erciyes Universitesi, 38039, Kayseri, Turkey

    Adem Kalinli & Fatih Sarikoc

Authors
  1. Adem Kalinli
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  2. Fatih Sarikoc
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Editor information

Editors and Affiliations

  1. Laboratory LiSSi, University of Paris 12, 61 Avenue du Général de Gaulle, 94010, Créteil, France

    Patrick Siarry

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

    Zbigniew Michalewicz

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Kalinli, A., Sarikoc, F. (2007). A Parallel Ant Colony Optimization Algorithm Based on Crossover Operation. In: Siarry, P., Michalewicz, Z. (eds) Advances in Metaheuristics for Hard Optimization. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72960-0_5

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  • DOI: https://doi.org/10.1007/978-3-540-72960-0_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72959-4

  • Online ISBN: 978-3-540-72960-0

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