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Ant colony optimization for resource-constrained project scheduling
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DOI：10.1109/TEVC.2002.802450.png)
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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.
Keyword:
ant algorithms
ant colony optimization
metaheuristics
project scheduling
RCPSP
summation evaluation
期刊
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12
论文数:
1.9K
被引数:
2.4W
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Serial and parallel resource-constrained project scheduling methods revisited: Theory and computation重新审视串行和并行资源受限的项目调度方法: 理论和计算
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