PKNUmets.xmloDIASAn adaptive parameter free particle swarm optimization algorithm for the permutation flowshop scheduling problem10.1007/978-3-030-37599-7_15https://link.springer.com/chapter/10.1007/978-3-030-37599-7_15en12 pagesThe 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.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_15http://creativecommons.org/licenses/by/4.0/2020-10-26Machine Learning, Optimization, and Data Science168-179Lecture Notes in Computer Science vol. 11943LOD 20192019falsePKetoPKNUetomets.xmlPK6METS archive created by DIAS