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Ant Colony Optimization Start Strategies Performance According Some of the Parameters

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Numerical Analysis and Its Applications (NAA 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8236))

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Abstract

Ant Colony Optimization (ACO) is a stochastic search method that mimic the social behavior of real ants colonies, which manage to establish the shortest rout to feeding sources and back. Such algorithms have been developed to arrive at near-optimal solutions to large-scale optimization problems, for which traditional mathematical techniques may fail. On this paper is proposed an ant algorithm with semi-random start. Several start strategies are prepared at the basis of the start nodes estimation. There are several parameters which manage the starting strategies. In this work we focus on influence on the quality of the achieved solutions of the parameters which shows the percentage of the solutions classified as good and as bad respectively. This new technique is tested on Multiple Knapsack Problem (MKP).

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Authors and Affiliations

  1. Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Acad. G. Bonchev str. bl25A, 1113, Sofia, Bulgaria

    Stefka Fidanova & Pencho Marinov

Authors
  1. Stefka Fidanova
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  2. Pencho Marinov
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Editors and Affiliations

  1. Institute of Information and Communication Technlogies, Bulgarian Academy of Sciences, Acad. G. Bonchev, Bl25A, 1113, Sofia, Bulgaria

    Ivan Dimov

  2. Department of Applied Analysis, Pázm\’{a}ny P\’{e}ter s\’{e}t\’{a}ny 1/C,, Eötvös Loránd University, 1117, Budapest, Hungary

    István Faragó

  3. University of Rousse, 8 Studentska Str.,, 7017, Rousse, Bulgaria

    Lubin Vulkov

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Fidanova, S., Marinov, P. (2013). Ant Colony Optimization Start Strategies Performance According Some of the Parameters. In: Dimov, I., Faragó, I., Vulkov, L. (eds) Numerical Analysis and Its Applications. NAA 2012. Lecture Notes in Computer Science, vol 8236. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41515-9_31

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  • DOI: https://doi.org/10.1007/978-3-642-41515-9_31

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41514-2

  • Online ISBN: 978-3-642-41515-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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