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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Matrenin, P.V. Sekaev, V.G. |
| Copyright Year | 2015 |
| Description | Author affiliation: Dept. of Automated Control Syst., Novosibirsk State Tech. Univ., Novosibirsk, Russia (Matrenin, P.V.; Sekaev, V.G.) |
| Abstract | The article presents a study of the Particle Swarm optimization method for scheduling problem. To improve the method's performance a restriction of particles' velocity and an evolutionary meta-optimization were realized. The approach proposed uses the Genetic algorithms for selection of the parameters of Particle Swarm optimization. Experiments were carried out on test tasks of the job-shop scheduling problem. This research proves the applicability of the approach and shows the importance of tuning the behavioral parameters of the swarm intelligence methods to achieve a high performance. |
| Starting Page | 1 |
| Ending Page | 5 |
| File Size | 474379 |
| Page Count | 5 |
| File Format | |
| e-ISBN | 9781479971039 |
| DOI | 10.1109/SIBCON.2015.7147143 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-05-21 |
| Publisher Place | Russia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Schedules combinatorial optimization Job shop scheduling Genetic algorithm adaptation scheduling problem Particle swarm optimization Optimization Tuning Particle Swarm Optimization Genetic algorithms |
| Content Type | Text |
| Resource Type | Article |
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