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Intelligent Automation and Computer Engineering pp 41–52Cite as

Adaptive Ant Colony Optimization with Cranky Ants

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Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 52))

Abstract

Ant Colony Optimization (ACO) is the algorithm inspired by the feeding behavior of ants and its search mechanism is based on the positive feedback reinforcement using pheromone communication. This chapter discusses a new adaptive ACO algorithm and its characteristics are as follows: (1) a novel cranky ant who behaves strangely is introduced to strengthen the pressure of diversification, (2) a new observation technique for the convergence behavior is employed to judge whether it is trapping at local optimal solution. Experiments using benchmark data prove that the proposed algorithm with the cranky ants and the observation technique enables to control the trade-off between intensification and diversification, in comparison with conventional ACO.

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Author information

Authors and Affiliations

  1. Department of Information Engineering, Meijo University, 1–501 Shiogamaguchi Tenpaku, Nagoya, 468–8502, Japan

    Masaya Yoshikawa

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  1. Masaya Yoshikawa
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Correspondence to Masaya Yoshikawa .

Editor information

Editors and Affiliations

  1. School of Information Sciences &, Engineering, University of Canberra, Canberra, 2601, Australia

    Xu Huang

  2. International Association of Engineers, Hung To Road 37-39, Hong Kong, Hong Kong/PR China

    Sio-Iong Ao

  3. Dept. Computer Science, Tijuana Institute of Technology, Chula Vista, USA

    Oscar Castillo

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© 2009 Springer Science+Business Media B.V.

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Yoshikawa, M. (2009). Adaptive Ant Colony Optimization with Cranky Ants. In: Huang, X., Ao, SI., Castillo, O. (eds) Intelligent Automation and Computer Engineering. Lecture Notes in Electrical Engineering, vol 52. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-3517-2_4

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  • DOI: https://doi.org/10.1007/978-90-481-3517-2_4

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  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-90-481-3516-5

  • Online ISBN: 978-90-481-3517-2

  • eBook Packages: EngineeringEngineering (R0)

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