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Industrial applications of the ant colony optimization algorithm

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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.

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Authors and Affiliations

  1. Institute of High Performance Computing, 1 Science Park Rd., #01-01 The Capricorn, Singapore Science Park 2, Singapore, 117528, Singapore

    Bud Fox, Wei Xiang & Heow Pueh Lee

  2. Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore, 117576, Singapore

    Heow Pueh Lee

Authors
  1. Bud Fox
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  2. Wei Xiang
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  3. Heow Pueh Lee
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Correspondence to Bud Fox.

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Fox, B., Xiang, W. & Lee, H.P. Industrial applications of the ant colony optimization algorithm. Int J Adv Manuf Technol 31, 805–814 (2007). https://doi.org/10.1007/s00170-005-0254-z

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  • DOI: https://doi.org/10.1007/s00170-005-0254-z

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