An adaptive parameter free particle swarm optimization algorithm for the permutation flowshop scheduling problemAn adaptive parameter free particle swarm optimization algorithm for the permutation flowshop scheduling problem
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2020-10-262019enThe 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.http://creativecommons.org/licenses/by/4.0/168-1795th International Conference on Machine Learning, Optimization, and Data ScienceMachine Learning, Optimization, and Data Science
Marinaki Magdalini
Μαρινακη Μαγδαληνη
Marinakis Ioannis
Μαρινακης Ιωαννης
Springer Nature
Particle swarm optimization
Path relinking
Permutation flowshop scheduling problem
Variable neighborhood search