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Ant Local Search for Combinatorial Optimization

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Published in Di Caro, G.-A. & Theraulaz, G. Bio-Inspired Models of Network, Information, and Computing Systems: 7th International ICST Conference, BIONETICS 2012 (Lugano, Switzerland, Dec. 2012). Berlin: Springer. 2014, p. 233-236
Collection Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering; 134
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.
Keywords Ant algorithmsLocal searchCombinatorial optimization
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ISBN: 978-3-319-06943-2
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ZUFFEREY, Nicolas. Ant Local Search for Combinatorial Optimization. In: Di Caro, G.-A. & Theraulaz, G. (Ed.). Bio-Inspired Models of Network, Information, and Computing Systems: 7th International ICST Conference, BIONETICS 2012 (Lugano, Switzerland, Dec. 2012). Berlin : Springer, 2014. p. 233-236. (Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering; 134) https://archive-ouverte.unige.ch/unige:47395

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Deposited on : 2015-03-03

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