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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ming Li Yuanbiao Zhang Weigang Jiang Jianwen Xie |
| Copyright Year | 2009 |
| Abstract | Resource and project optimization scheduling has become the key of the success of researching project in the enterprises. In order to solve the mass resource constrained project scheduling problem, in this paper, an improved particle swarm algorithm (PSO) called particle swarm algorithm with crossover (CPSO) was presented. This improved algorithm is based on PSO and genetic algorithm (GA). Through comparing with SPSO and GA on RCPSP, it is indicated that CPSO not only avoids premature convergence to a high degree, but also keeps a faster convergence rate than SPSO and GA. |
| Starting Page | 69 |
| Ending Page | 72 |
| File Size | 392580 |
| Page Count | 4 |
| File Format | |
| ISBN | 9780769537290 |
| DOI | 10.1109/SSME.2009.131 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-07-11 |
| Publisher Place | 09PR3729 |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Heuristic algorithms Project management Conference management Particle swarm optimization Scheduling algorithm particle swarm algorithm with crossover Genetic algorithms Convergence genetic algorithm NP-hard problem Innovation management Iterative algorithms resource constrained project scheduling problem |
| Content Type | Text |
| Resource Type | Article |
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