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Ant Local Search for Combinatorial Optimization

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Bio-Inspired Models of Network, Information, and Computing Systems (BIONETICS 2012)

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Abstract

In ant algorithms, each individual ant makes decisions according to the greedy force (short term profit) and the trail system based on the history of the search (information provided by other ants). Usually, each ant is a constructive process, which starts from scratch and builds step by step a complete solution of the considered problem. In contrast, in Ant Local Search (ALS), each ant is a local search, which starts from an initial solution and tries to improve it iteratively. In this paper are presented and discussed successful adaptations of ALS to different combinatorial optimization problems: graph coloring, a refueling problem in a railway network, and a job scheduling problem.

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Authors and Affiliations

  1. GSEM - University of Geneva, 1211, Geneva 4, Switzerland

    Nicolas Zufferey

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  1. Nicolas Zufferey
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Correspondence to Nicolas Zufferey .

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Editors and Affiliations

  1. Dalle Molle Institute for Artificial Intelligence (IDSIA), Manno-Lugano, Switzerland

    Gianni A. Di Caro

  2. CNRS UMR 5169, Research Center on Animal Cognition, Université Paul Sabatier, Toulouse, France

    Guy Theraulaz

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© 2014 Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Zufferey, N. (2014). Ant Local Search for Combinatorial Optimization. In: Di Caro, G., Theraulaz, G. (eds) Bio-Inspired Models of Network, Information, and Computing Systems. BIONETICS 2012. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 134. Springer, Cham. https://doi.org/10.1007/978-3-319-06944-9_16

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  • DOI: https://doi.org/10.1007/978-3-319-06944-9_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-06943-2

  • Online ISBN: 978-3-319-06944-9

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