Optymalizacja parametryczna modeli i systemów z opóźnieniem czasowym z wykorzystaniem metaheurystyk
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ID: 2020/39/I/ST7/02285
Completion Time
Start: 2021-10-08
End: 2024-10-07
Status
Coordinator
Finansowanie
Resource Type
Institution: Narodowe Centrum Nauki (NCN)
Funders
Program: OPUS 20 (LAP)
Description
Projekt polega na opracowaniu nowych metaheurystyk w zastosowaniu do optymalizacji parametrycznej modeli systemów z opóźnieniem czasowym.
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5 results
Publication Search Results
Now showing 1 - 5 of 5
Item type:Article, Access status: Open Access , Configuring a hierarchical evolutionary strategy using exploratory landscape analysis(2023) Guzowski, Hubert; Smołka, Maciej
Wydział InformatykiHierarchic Memetic Strategy (HMS) is a stochastic global optimizer designed to tackle highly multimodal problems. It consists of parallel running optimization methods organized in a tree hierarchy. Depending on the task, different algorithms can be utilized on each of the levels. In this paper, we incorporate into HMS's structure a mechanism for choosing its configuration based on information gathered by a set of Exploratory Landscape Analysis (ELA) methods and hyperparametric optimization. We compared the performance of such configured HMS with a portfolio of proven state-of-the-art algorithms on the suite of black-box optimization functions. The results of this work show the efficacy of HMS and provide a set of default parameters evaluated for algorithms users. The use of ELA methods to select the configuration of a composite algorithm extends their standard use as part of an algorithm selector and provides insight into the relationship between exploration and exploitation for different types of fitness functions.Item type:Article, Access status: Open Access , Operating point shift induced by relay asymmetry: an iterative solution proposal(2023) Pekař, Libor; Matušů, Radek; Guzowski, Hubert; Gazdoš, František
Wydział InformatykiThis contribution focuses on a problem that appears when using a relay with non-symmetric output in the closed loop. Such a scheme is usually used for process model parameters identification, possibly followed by automatic controller tuning. Whenever static or dynamic properties of the process reveal asymmetry when the sign of the input changes, the setpoint (reference) becomes different from the operating point value of the process output. As a class of relay-based identification methods utilize calculations in the frequency domain that are based on integral computation around the operating point, the discrepancy between the setpoint and the operating point can lead to incorrect results. The aim of the paper is mainly to provide the reader with problem formulation and step-by-step proposition of how it can be solved. Concise numerical examples are also given. The concluding remarks suggest possible further ways of research.Item type:Book Chapter, Access status: Open Access , Enhancing a hierarchical evolutionary strategy using the Nearest-Better Clustering(2024) Guzowski, Hubert; Smołka, Maciej; Pekař, Libor
Wydział InformatykiA straightforward way of solving global optimization problems is to find all local optima of the objective function. Therefore, the ability of detecting multiple local optima is a key feature of a practically usable global optimization method. One of such methods is a multi-population evolutionary strategy called the Hierarchic Memetic Strategy (HMS). Although HMS has already proven its global optimization capabilities there is an area for improvement. In this paper we show such an enhancement resulting from the application of the Nearest-Better Clustering. Results of experiments consisting both of curated benchmarks and a real-world inverse problem show that on average the performance is indeed improved compared to the baseline HMS and remains on par with state-of-the-art evolutionary global optimization methods.Item type:Article, Access status: Open Access , Maximizing efficiency: a comparative study of SOMA variants and constraint handling methods for time delay system optimization(2023) Senkerik, Roman; Kadavy, Tomas; Janku, Peter; Pluhacek, Michal; Guzowski, Hubert; Pekar, Libor; Matusu, Radek; Viktorin, Adam; Smołka, Maciej; Byrski, Aleksander; Komínková Oplatková, Zuzana
Wydział InformatykiThis paper presents an experimental study that compares four adaptive variants of the self-organizing migrating algorithm (SOMA). Each variant uses three different constraint handling methods for the optimization of a time delay system model. The paper emphasizes the importance of metaheuristic algorithms in control engineering for time-delayed systems to develop more effective and efficient control strategies and precise model identifications. The study includes a detailed description of the selected variants of the SOMA and the adaptive mechanisms used. A complex workflow of experiments is described, and the results and discussion are presented. The experimental results highlight the effectiveness of the SOMA variants with specific constraint handling methods for time delay system optimization. Overall, this study contributes to the understanding of the challenges and advantages of using metaheuristic algorithms in control engineering for time delay systems. The results provide valuable insights into the performance of the SOMA variants and can help guide the selection of appropriate constraint handling methods and the adaptive mechanisms of metaheuristics.Item type:Article, Access status: Open Access , Efficient time-delay system optimization with auto-configured metaheuristics(2024) Senkerik, Roman; Guzowski, Hubert; Janku, Peter; Kadavy, Tomas; Kominkova Oplatkova, Zuzana; Matusu, Radek; Pluhacek, Michal; Pekar, Libor; Viktorin, Adam; Byrski, Aleksander; Smołka, Maciej
Wydział InformatykiThis paper presents an experimental study that compares the performance of four selected metaheuristic algorithms for optimizing a time delay system model. Time delay system models are complex and challenging to optimize due to their inherent characteristics, such as non-linearity, multi-modality, and constraints. The study includes an explanation of the choice and core functionality of the selected algorithms, which are both baseline and state-of-the-art variants of self-organizing migrating algorithm (SOMA), state-of-the-art variant from the Success-History-based Adaptive Differential Evolution family of algorithms, with emphasis on diverse search (DISH algorithm), and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm. The hyperparameters of the metaheuristic algorithms were set using the iRace automatic algorithm configuration framework. The paper emphasizes the importance of metaheuristic algorithms in control engineering for time-delay systems to develop more effective and efficient control strategies and precise model identifications. The experimental results highlight the effectiveness of the state-of-the-art algorithms with specific adaptive mechanisms like population organization process, diverse search and adaptation mechanisms ensuring a gradual transition from exploration to exploitation. Overall, this study contributes to understanding the challenges and advantages of using metaheuristic algorithms in control engineering for time delay systems. The results provide valuable insights into the performance of modern metaheuristic algorithms and can help guide the selection of appropriate adaptive mechanisms of metaheuristics.