Please use this identifier to cite or link to this item: https://doi.org/10.1007/s00170-005-0254-z
Title: Industrial applications of the ant colony optimization algorithm
Authors: Fox, B.
Xiang, W.
Lee, H.P. 
Keywords: Ant colony optimization
Combinatorial optimization
Hybrid algorithms
Job shop scheduling problem
Node-arc graphs
Traveling salesperson problem
Issue Date: Jan-2007
Source: Fox, B., Xiang, W., Lee, H.P. (2007-01). Industrial applications of the ant colony optimization algorithm. International Journal of Advanced Manufacturing Technology 31 (7-8) : 805-814. ScholarBank@NUS Repository. https://doi.org/10.1007/s00170-005-0254-z
Abstract: The ant colony optimization (ACO) algorithm is a fast suboptimal meta-heuristic based on the behavior of a set of ants that communicate through the deposit of pheromone. It involves a node choice probability which is a function of pheromone strength and inter-node distance to construct a path through a node-arc graph. The algorithm allows fast near optimal solutions to be found and is useful in industrial environments where computational resources and time are limited. A hybridization using iterated local search (ILS) is made in this work to the existing heuristic to refine the optimality of the solution. Applications of the ACO algorithm also involve numerous traveling salesperson problem (TSP) instances and benchmark job shop scheduling problems (JSSPs), where the latter employs a simplified ant graph-construction model to minimize the number of edges for which pheromone update should occur, so as to reduce the spatial complexity in problem computation. © Springer-Verlag London Limited 2007.
Source Title: International Journal of Advanced Manufacturing Technology
URI: http://scholarbank.nus.edu.sg/handle/10635/60517
ISSN: 02683768
DOI: 10.1007/s00170-005-0254-z
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