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Parallel ant colony optimization for resource constrained job scheduling
journal contribution
posted on 2016-07-01, 00:00 authored by Dhananjay ThiruvadyDhananjay Thiruvady, Andreas T Ernst, Gaurav SinghIn mining supply chains, large combinatorial optimization problems arise. These are NP-hard and typically require a large number of computing resources to solve them. In particular, the run-time overheads can become increasingly prohibitive with increasing problem sizes. Parallel methods provide a way to manage such run-time issues by utilising several processors in independent or shared memory architectures. However it is not obvious how to adapt serial optimisation algorithms to perform best in a parallel environment. Here, we consider a resource constrained scheduling problem which is motivated in mining supply chains and present two popular meta-heuristics, ant colony optimization (ACO) and simulated annealing and investigate how best to parallelize these methods on a shared memory architecture consisting of several cores. ACO’s solution construction framework is inherently parallel allowing a relatively straightforward parallel implementation. However, for best performance, ACO needs an element of local search. This significantly complicates the paralellization. Several alternative schemes for parallel ACO with elements of local search are considered and evaluated empirically. We find that ACO with local search is the most effective single-threaded algorithm. The best parallel implementation can obtain similar quality results to the serial method in significantly less elapsed time.
History
Journal
Annals of operations researchVolume
242Issue
2Pagination
355 - 372Publisher
SpringerLocation
New York, N.Y.Publisher DOI
ISSN
0254-5330Language
engPublication classification
C1.1 Refereed article in a scholarly journalCopyright notice
2014, Springer Science+Business Media New YorkUsage metrics
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