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Cited by 759

The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.

Ant colony optimization for resource-constrained project scheduling

Open access
Merkle, D., Middendorf, M., Schmeck, H.·2002·IEEE Transactions on Evolutionary Computation
DOI 10.1109/tevc.2002.802450
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Abstract

An ant colony optimization (ACO) approach for the resource-constrained project scheduling problem (RCPSP) is presented. Several new features that are interesting for ACO in general are proposed and evaluated. In particular, the use of a combination of two pheromone evaluation methods by the ants to find new solutions, a change of the influence of the heuristic on the decisions of the ants during the run of the algorithm, and the option that an elitist ant forgets the best-found solution are studied. We tested the ACO algorithm on a set of large benchmark problems from the Project Scheduling Library. Compared to several other heuristics for the RCPSP, including genetic algorithms, simulated annealing, tabu search, and different sampling methods, our algorithm performed best on average. For nearly one-third of all benchmark problems, which were not known to be solved optimally before, the algorithm was able to find new best solutions.

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Metadata

Authors
Merkle, D., Middendorf, M., & Schmeck, H.
Venue
IEEE Transactions on Evolutionary Computation
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2002-08
Volume
6
Issue
4
Pages
333-346
ISSN
1089-778X (ISSN-L)
DOI
10.1109/tevc.2002.802450
OpenAlex
W118692873
Cited by
759
References
15
FWCI
41.69
Type
journal article
Language
en
Open access
green

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