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J. Tondut, N. Di Cesare, S. Ronel, "Validation of an automated kriging-based methodology to calibrate PSO parameters: application to parametric optimization of truss structures", in B.H.V. Topping, J. Kruis, (Editors), "Proceedings of the Fourteenth International Conference on Computational Structures Technology", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 3, Paper 4.5, 2022, doi:10.4203/ccc.3.4.5

Keywords: optimization, metaheuristics, pso, calibration parameter, kriging, truss structures.

Abstract

For years, the Particle Swarm Optimization (PSO) algorithm has been widely studied and many improved versions have been developed: from the swarm's topologies to the addition of new parameters, including machine learning approaches. However, the tuning of the fundamental PSO parameters has been less studied, but may lead to significant improvements on the convergence accuracy of PSO. This paper aims to develop an automated methodology to calibrate PSO parameters for a given optimization problem. The process is based on the kriging estimation of the best combination of PSO parameters. In this way, the Automated Tuning parameter Calibration (ATpC) methodology gives the optimal PSO setup for each considered problem in order to lead to a better convergence accuracy. The proposed ATpC methodology is applied to parametric optimization of truss structures. ATpC methodology performance is assessed by comparison of two different PSO setups usually used in the literature. The numerical results show that the ATpC methodology allows to significantly improve the convergence accuracy of PSO.

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Front Page Browse CCC CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Conferences ISSN 2753-3239 CCC: 3 PROCEEDINGS OF THE FOURTEENTH INTERNATIONAL CONFERENCE ON COMPUTATIONAL STRUCTURES TECHNOLOGY Edited by: B.H.V. Topping and J. Kruis Paper 4.5 Validation of an automated kriging-based methodology to calibrate PSO parameters: application to parametric optimization of truss structures J. Tondut¹, N. Di Cesare² and S. Ronel¹ ¹LBMC, UCBL, Lyon, France ²LMC2, UCBL, Lyon, France doi:10.4203/ccc.3.4.5 Full Bibliographic Reference for this paper J. Tondut, N. Di Cesare, S. Ronel, "Validation of an automated kriging-based methodology to calibrate PSO parameters: application to parametric optimization of truss structures", in B.H.V. Topping, J. Kruis, (Editors), "Proceedings of the Fourteenth International Conference on Computational Structures Technology", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 3, Paper 4.5, 2022, doi:10.4203/ccc.3.4.5 Keywords: optimization, metaheuristics, pso, calibration parameter, kriging, truss structures. Abstract For years, the Particle Swarm Optimization (PSO) algorithm has been widely studied and many improved versions have been developed: from the swarm's topologies to the addition of new parameters, including machine learning approaches. However, the tuning of the fundamental PSO parameters has been less studied, but may lead to significant improvements on the convergence accuracy of PSO. This paper aims to develop an automated methodology to calibrate PSO parameters for a given optimization problem. The process is based on the kriging estimation of the best combination of PSO parameters. In this way, the Automated Tuning parameter Calibration (ATpC) methodology gives the optimal PSO setup for each considered problem in order to lead to a better convergence accuracy. The proposed ATpC methodology is applied to parametric optimization of truss structures. ATpC methodology performance is assessed by comparison of two different PSO setups usually used in the literature. The numerical results show that the ATpC methodology allows to significantly improve the convergence accuracy of PSO. download the full-text of this paper (PDF, 6 pages, 559 Kb) go to the previous paper go to the next paper return to the table of contents return to the volume description
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