改进粒子滤波优化算法,实现不同类型不确定性下的鲁棒优化。

IF 3.4 3区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Heliyon Pub Date : 2025-01-03 eCollection Date: 2025-01-15 DOI:10.1016/j.heliyon.2024.e41573
Éva Kenyeres, Alex Kummer, János Abonyi
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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 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.

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来源期刊
Heliyon
Heliyon MULTIDISCIPLINARY SCIENCES-
CiteScore
4.50
自引率
2.50%
发文量
2793
期刊介绍: Heliyon is an all-science, open access journal that is part of the Cell Press family. Any paper reporting scientifically accurate and valuable research, which adheres to accepted ethical and scientific publishing standards, will be considered for publication. Our growing team of dedicated section editors, along with our in-house team, handle your paper and manage the publication process end-to-end, giving your research the editorial support it deserves.
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