Abstract
This contribution presents a comparison between two extensions of the particle swarm optimization algorithm in a hybrid setting where the evaluation of the objective function requires a high computational effort. A first approach using simulation-based optimization via particle swarm optimization was developed in order to reach an improved setup optimization support of the workpiece position and orientation in a CNC tooling machine. For that, a 1:1 interface between the machine simulation model and the simulation-based optimization approach produced a high number of simulation runs. The idea arose that the extension of the PSO algorithm as well as the usage of an NC interpreter operating as a pre-processing component could support the setup process of the tooling machines. The extension of the PSO algorithm deals with the segmentation of the parameter search space taking collisions and lower computational effort into consideration. A significant reduction of simulation runs has been achieved.
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Mueß, A., Weber, J., Reisch, RE., Jurke, B. (2016). Implementation and Comparison of Cluster-Based PSO Extensions in Hybrid Settings with Efficient Approximation. In: Niggemann, O., Beyerer, J. (eds) Machine Learning for Cyber Physical Systems. Technologien für die intelligente Automation. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48838-6_11
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DOI: https://doi.org/10.1007/978-3-662-48838-6_11
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