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An adaptive parameter free particle swarm optimization algorithm for the permutation flowshop scheduling problem

Marinaki Magdalini, Marinakis Ioannis

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URIhttp://purl.tuc.gr/dl/dias/23CBDAEC-3F99-416D-972E-198E84EFEB3D-
Identifierhttps://doi.org/10.1007/978-3-030-37599-7_15-
Identifierhttps://link.springer.com/chapter/10.1007/978-3-030-37599-7_15-
Languageen-
Extent12 pagesen
TitleAn adaptive parameter free particle swarm optimization algorithm for the permutation flowshop scheduling problemen
CreatorMarinaki Magdalinien
CreatorΜαρινακη Μαγδαληνηel
CreatorMarinakis Ioannisen
CreatorΜαρινακης Ιωαννηςel
PublisherSpringer Natureen
Content SummaryThe 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 ItemΠλήρης Δημοσίευση σε Συνέδριοel
Type of ItemConference Full Paperen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2020-10-26-
Date of Publication2019-
SubjectParticle swarm optimizationen
SubjectPath relinkingen
SubjectPermutation flowshop scheduling problemen
SubjectVariable neighborhood searchen
Bibliographic CitationY. 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_15en

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