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Base Hybrid Approach for TSP Based on Neural Networks and Ant Colony Optimization

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Intelligent and Evolutionary Systems

Part of the book series: Proceedings in Adaptation, Learning and Optimization ((PALO,volume 5))

Abstract

This research article presents a hybrid approach based on an intelligent combination of artificial ants and neurons. Research on different parameter combinations are performed, in order to find the best performing parameter settings. The obtained insights are then subsumed into an intelligent architecture consisting of Ant Colony Optimization and Self Organizing Map.

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Author information

Authors and Affiliations

  1. Department of Information Technologies, Faculty of Informatics and Statistics, University of Economics, W. Churchill Sq. 4, 130 67, Prague 3, Czech Republic

    Carsten Mueller & Niklas Kiehne

Authors
  1. Carsten Mueller
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  2. Niklas Kiehne
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Corresponding author

Correspondence to Carsten Mueller .

Editor information

Editors and Affiliations

  1. School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand

    Kittichai Lavangnananda

  2. School of Computing and Informatics, Institut Teknologi Brunei, Gadong, Brunei Darussalam

    Somnuk Phon-Amnuaisuk

  3. School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand

    Worrawat Engchuan

  4. School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand

    Jonathan H. Chan

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© 2016 Springer International Publishing Switzerland

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Mueller, C., Kiehne, N. (2016). Base Hybrid Approach for TSP Based on Neural Networks and Ant Colony Optimization. In: Lavangnananda, K., Phon-Amnuaisuk, S., Engchuan, W., Chan, J. (eds) Intelligent and Evolutionary Systems. Proceedings in Adaptation, Learning and Optimization, vol 5. Springer, Cham. https://doi.org/10.1007/978-3-319-27000-5_18

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  • DOI: https://doi.org/10.1007/978-3-319-27000-5_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26999-3

  • Online ISBN: 978-3-319-27000-5

  • eBook Packages: EngineeringEngineering (R0)

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