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Technical University of Crete
Technical University of Crete
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| URI | http://purl.tuc.gr/dl/dias/23CBDAEC-3F99-416D-972E-198E84EFEB3D | - |
| Identifier | https://doi.org/10.1007/978-3-030-37599-7_15 | - |
| Identifier | https://link.springer.com/chapter/10.1007/978-3-030-37599-7_15 | - |
| Language | en | - |
| Extent | 12 pages | en |
| Title | An adaptive parameter free particle swarm optimization algorithm for the permutation flowshop scheduling problem | en |
| Creator | Marinaki Magdalini | en |
| Creator | Μαρινακη Μαγδαληνη | el |
| Creator | Marinakis Ioannis | en |
| Creator | Μαρινακης Ιωαννης | el |
| Publisher | Springer Nature | en |
| Content Summary | The finding of suitable values for all parameters of a Particle Swarm Optimization (PSO) algorithm is a crucial issue in the design of the algorithm. A trial and error procedure is the most common way to find the parameters but, also, a number of different procedures have been applied in the past. In this paper, an adaptive strategy is used where random values are assigned in the initialization of the algorithm and, then, during the iterations the parameters are optimized together and simultaneously with the optimization of the objective function of the problem. This approach is used for the solution of the Permutation Flowshop Scheduling Problem. The algorithm is tested in 120 benchmark instances and is compared with a number of algorithms from the literature. | en |
| Type of Item | Πλήρης Δημοσίευση σε Συνέδριο | el |
| Type of Item | Conference Full Paper | en |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2020-10-26 | - |
| Date of Publication | 2019 | - |
| Subject | Particle swarm optimization | en |
| Subject | Path relinking | en |
| Subject | Permutation flowshop scheduling problem | en |
| Subject | Variable neighborhood search | en |
| Bibliographic Citation | Y. Marinakis and M. Marinaki, "An adaptive parameter free particle swarm optimization algorithm for the permutation flowshop scheduling problem," in Machine Learning, Optimization, and Data Science, vol. 11943, Lecture Notes in Computer Science, G. Nicosia, P. Pardalos, R. Umeton, G. Giuffrida, V. Sciacca, Eds., Cham, Switzerland: Springer Nature, 2019, pp. 168-179. doi: 10.1007/978-3-030-37599-7_15 | en |
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