Industrial applications of the ant colony optimization algorithm |
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Authors: | Bud Fox Wei Xiang Heow Pueh Lee |
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Affiliation: | (1) Institute of High Performance Computing, 1 Science Park Rd., #01-01 The Capricorn, Singapore Science Park 2, Singapore, 117528, Singapore;(2) Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore, 117576, Singapore |
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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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Keywords: | Ant colony optimization Combinatorial optimization Hybrid algorithms Job shop scheduling problem Node-arc graphs Traveling salesperson problem |
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