Improvements of particle filter optimization algorithm for robust optimization under different types of uncertainties
This paper introduces a methodology for handling different types of uncertainties during robust optimization. In real-world industrial optimization problems, many types of uncertainties emerge, e.g., inaccurate setting of control variables, and the parameters of the system model are usually not know...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024176049 |
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| author | Éva Kenyeres Alex Kummer János Abonyi |
| author_facet | Éva Kenyeres Alex Kummer János Abonyi |
| author_sort | Éva Kenyeres |
| collection | DOAJ |
| description | This paper introduces a methodology for handling different types of uncertainties during robust optimization. In real-world industrial optimization problems, many types of uncertainties emerge, e.g., inaccurate setting of control variables, and the parameters of the system model are usually not known precisely. For these reasons, the global optimum considering the nominal values of the parameters may not give the best performance in practice. This paper presents a widely usable sampling-based methodology by improving the Particle Filter Optimization (PFO) algorithm. Case studies on benchmark functions and even on a practical example of a styrene reactor are introduced to verify the applicability of the proposed method on finding robust optimum, and show how the users can tune this algorithm according to their requirement. The results verify that the proposed method is able to find robust optimums efficiently under parameter and decision variable uncertainties, as well. |
| format | Article |
| id | doaj-art-287f96d3c91446b68340675ead298744 |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-287f96d3c91446b68340675ead2987442025-01-17T04:51:40ZengElsevierHeliyon2405-84402025-01-01111e41573Improvements of particle filter optimization algorithm for robust optimization under different types of uncertaintiesÉva Kenyeres0Alex Kummer1János Abonyi2Corresponding author.; HUN-REN-PE Complex Systems Monitoring Research Group, Department of Process Engineering, University of Pannonia, Egyetem u. 10, P.O. BOX 158, Veszprém, H-8200, HungaryHUN-REN-PE Complex Systems Monitoring Research Group, Department of Process Engineering, University of Pannonia, Egyetem u. 10, P.O. BOX 158, Veszprém, H-8200, HungaryHUN-REN-PE Complex Systems Monitoring Research Group, Department of Process Engineering, University of Pannonia, Egyetem u. 10, P.O. BOX 158, Veszprém, H-8200, HungaryThis paper introduces a methodology for handling different types of uncertainties during robust optimization. In real-world industrial optimization problems, many types of uncertainties emerge, e.g., inaccurate setting of control variables, and the parameters of the system model are usually not known precisely. For these reasons, the global optimum considering the nominal values of the parameters may not give the best performance in practice. This paper presents a widely usable sampling-based methodology by improving the Particle Filter Optimization (PFO) algorithm. Case studies on benchmark functions and even on a practical example of a styrene reactor are introduced to verify the applicability of the proposed method on finding robust optimum, and show how the users can tune this algorithm according to their requirement. The results verify that the proposed method is able to find robust optimums efficiently under parameter and decision variable uncertainties, as well.http://www.sciencedirect.com/science/article/pii/S2405844024176049Robust optimizationUncertainty quantificationStochastic objective functionSampling-based techniques |
| spellingShingle | Éva Kenyeres Alex Kummer János Abonyi Improvements of particle filter optimization algorithm for robust optimization under different types of uncertainties Heliyon Robust optimization Uncertainty quantification Stochastic objective function Sampling-based techniques |
| title | Improvements of particle filter optimization algorithm for robust optimization under different types of uncertainties |
| title_full | Improvements of particle filter optimization algorithm for robust optimization under different types of uncertainties |
| title_fullStr | Improvements of particle filter optimization algorithm for robust optimization under different types of uncertainties |
| title_full_unstemmed | Improvements of particle filter optimization algorithm for robust optimization under different types of uncertainties |
| title_short | Improvements of particle filter optimization algorithm for robust optimization under different types of uncertainties |
| title_sort | improvements of particle filter optimization algorithm for robust optimization under different types of uncertainties |
| topic | Robust optimization Uncertainty quantification Stochastic objective function Sampling-based techniques |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024176049 |
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