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[nazev] => Geometry Optimization of a Highly Flexible Gradient Metamaterial Structure Using a Differential Evolution Algorithm
[nazev_orig] => Geometry Optimization of a Highly Flexible Gradient Metamaterial Structure Using a Differential Evolution Algorithm
[duvernost_udaju_id] => S
[popis] => Gradient structures can offer great flexibility in parameter tuning, enabling the achievement of tailored mechanical properties for specific applications, and can even outperform their uniform counterparts. However, the design of such complex structures can be challenging, especially when many tunable geometric parameters are involved. To address this challenge, a simplified model that captures the main attributes and behavior of the structure can be employed. In this approach, a reduced number of parameters are optimized, while the remaining parameters are treated as constants throughout the structure. This also reduces the computational demands for simulating the structure with each iteration, thus accelerating the overall optimization process. A method for the geometry optimization of a highly flexible gradient metamaterial structure for the skin of a morphing aircraft wing's leading edge is proposed, utilizing a differential evolution algorithm. Initially, the basics of evolutionary algorithms and their representative, differential evolution, are introduced. Then, the flexible metamaterial skin with gradient bending stiffness, its simplified spring model, and the set of parameters for optimization are presented. Finally, the geometry parameters and the required acting loads are optimized using DE to achieve various deformed shapes that correspond to the morphing wing leading edge at different flight stages. Three levels of complexity of the optimized model are explored: a foundational version suitable for algorithm parameter tuning, an intermediate version for the optimization of geometric parameters and loading for one target shape, and an advanced version aimed at achieving multiple morphing shapes under different loading conditions.
[popis_orig] => Gradient structures can offer great flexibility in parameter tuning, enabling the achievement of tailored mechanical properties for specific applications, and can even outperform their uniform counterparts. However, the design of such complex structures can be challenging, especially when many tunable geometric parameters are involved. To address this challenge, a simplified model that captures the main attributes and behavior of the structure can be employed. In this approach, a reduced number of parameters are optimized, while the remaining parameters are treated as constants throughout the structure. This also reduces the computational demands for simulating the structure with each iteration, thus accelerating the overall optimization process. A method for the geometry optimization of a highly flexible gradient metamaterial structure for the skin of a morphing aircraft wing's leading edge is proposed, utilizing a differential evolution algorithm. Initially, the basics of evolutionary algorithms and their representative, differential evolution, are introduced. Then, the flexible metamaterial skin with gradient bending stiffness, its simplified spring model, and the set of parameters for optimization are presented. Finally, the geometry parameters and the required acting loads are optimized using DE to achieve various deformed shapes that correspond to the morphing wing leading edge at different flight stages. Three levels of complexity of the optimized model are explored: a foundational version suitable for algorithm parameter tuning, an intermediate version for the optimization of geometric parameters and loading for one target shape, and an advanced version aimed at achieving multiple morphing shapes under different loading conditions.
[klicova_slova] => metamaterial skin, functional gradient material, optimization, differential evolution, morphing wing
[klicova_slova_orig] => metamaterial skin, functional gradient material, optimization, differential evolution, morphing wing
[url] => https://ieeexplore.ieee.org/document/10789725
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[citace_text] => Jan Bajer; Miroslav Hrstka; Zahra Sharif Khodaei; M.H. Aliabadi; Zdenek Hadas. Geometry Optimization of a Highly Flexible Gradient Metamaterial Structure Using a Differential Evolution Algorithm. In 2024 21st International Conference on Mechatronics - Mechatronika (ME). 21. Brno, Czech Republic: IEEE, 2024. p. 186-191. ISBN: 979-8-3503-9490-0.
[citace_html] => Jan Bajer; Miroslav Hrstka; Zahra Sharif Khodaei; M.H. Aliabadi; Zdenek Hadas. Geometry Optimization of a Highly Flexible Gradient Metamaterial Structure Using a Differential Evolution Algorithm. In 2024 21st International Conference on Mechatronics - Mechatronika (ME). 21. Brno, Czech Republic: IEEE, 2024. p. 186-191. ISBN: 979-8-3503-9490-0.
[citace_rtf] =>
[citace_bibtex] => @inproceedings{BUT194119,
author="Jan Bajer and Miroslav Hrstka and Zahra Sharif Khodaei and M.H. Aliabadi and Zdenek Hadas",
title="Geometry Optimization of a Highly Flexible Gradient Metamaterial Structure Using a Differential Evolution Algorithm",
booktitle="2024 21st International Conference on Mechatronics - Mechatronika (ME)",
year="2024",
series="21",
number="1",
pages="186--191",
publisher="IEEE",
address="Brno, Czech Republic",
doi="10.1109/ME61309.2024.10789725",
isbn="979-8-3503-9490-0",
url="https://ieeexplore.ieee.org/document/10789725"
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[nazev_en] => Geometry Optimization of a Highly Flexible Gradient Metamaterial Structure Using a Differential Evolution Algorithm
[popis_en] => Gradient structures can offer great flexibility in parameter tuning, enabling the achievement of tailored mechanical properties for specific applications, and can even outperform their uniform counterparts. However, the design of such complex structures can be challenging, especially when many tunable geometric parameters are involved. To address this challenge, a simplified model that captures the main attributes and behavior of the structure can be employed. In this approach, a reduced number of parameters are optimized, while the remaining parameters are treated as constants throughout the structure. This also reduces the computational demands for simulating the structure with each iteration, thus accelerating the overall optimization process. A method for the geometry optimization of a highly flexible gradient metamaterial structure for the skin of a morphing aircraft wing's leading edge is proposed, utilizing a differential evolution algorithm. Initially, the basics of evolutionary algorithms and their representative, differential evolution, are introduced. Then, the flexible metamaterial skin with gradient bending stiffness, its simplified spring model, and the set of parameters for optimization are presented. Finally, the geometry parameters and the required acting loads are optimized using DE to achieve various deformed shapes that correspond to the morphing wing leading edge at different flight stages. Three levels of complexity of the optimized model are explored: a foundational version suitable for algorithm parameter tuning, an intermediate version for the optimization of geometric parameters and loading for one target shape, and an advanced version aimed at achieving multiple morphing shapes under different loading conditions.
[klicova_slova_en] => metamaterial skin, functional gradient material, optimization, differential evolution, morphing wing
[vysledek_datum] => 2024-12-06T00:00:00+01:00
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[nazev] => Modal Properties Tuning Analysis of Dynamic System with Piezoelectric Components
[nazev_orig] => Modal Properties Tuning Analysis of Dynamic System with Piezoelectric Components
[duvernost_udaju_id] => S
[popis] => This paper explores the modal properties tuning of mechanical systems using piezoelectric transducers. The study focuses on piezoelectric shunt damping techniques, particularly Synchronous Switch Damping on Voltage (SSDV), to mitigate vibration and alter resonant frequencies. Simulations and experiments were conducted to investigate the influence of phase shifts and applied voltage on the system's dynamics. The simulation results indicate significant changes in the resonant frequency at certain phase shifts, while the experimental results showed partial alignment with the simulations, revealing challenges in electronics and the need for precise control. This research contributes to the development of adaptive vibration control technologies, enhancing the stability and lifespan of mechanical systems.
[popis_orig] => This paper explores the modal properties tuning of mechanical systems using piezoelectric transducers. The study focuses on piezoelectric shunt damping techniques, particularly Synchronous Switch Damping on Voltage (SSDV), to mitigate vibration and alter resonant frequencies. Simulations and experiments were conducted to investigate the influence of phase shifts and applied voltage on the system's dynamics. The simulation results indicate significant changes in the resonant frequency at certain phase shifts, while the experimental results showed partial alignment with the simulations, revealing challenges in electronics and the need for precise control. This research contributes to the development of adaptive vibration control technologies, enhancing the stability and lifespan of mechanical systems.
[klicova_slova] => Damping
,
Vibrations
,
Adaptation models
,
System dynamics
,
Simulation
,
Resonant frequency
,
Vibration control
,
Mechanical systems
,
Voltage control
,
Tuning
[klicova_slova_orig] => Damping
,
Vibrations
,
Adaptation models
,
System dynamics
,
Simulation
,
Resonant frequency
,
Vibration control
,
Mechanical systems
,
Voltage control
,
Tuning
[url] => https://ieeexplore.ieee.org/document/10789718
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[citace_text] => SKŘIVÁNEK, V.; RUBEŠ, O.; HADAŠ, Z. Modal Properties Tuning Analysis of Dynamic System with Piezoelectric Components. In 2024 21st International Conference on Mechatronics - Mechatronika (ME). 21. Brno, Czech Republic: IEEE, 2024. 5 p. ISBN: 979-8-3503-9490-0.
[citace_html] => SKŘIVÁNEK, V.; RUBEŠ, O.; HADAŠ, Z. Modal Properties Tuning Analysis of Dynamic System with Piezoelectric Components. In 2024 21st International Conference on Mechatronics - Mechatronika (ME). 21. Brno, Czech Republic: IEEE, 2024. 5 p. ISBN: 979-8-3503-9490-0.
[citace_rtf] =>
[citace_bibtex] => @inproceedings{BUT194123,
author="Vladimír {Skřivánek} and Ondřej {Rubeš} and Zdeněk {Hadaš}",
title="Modal Properties Tuning Analysis of Dynamic System with Piezoelectric Components",
booktitle="2024 21st International Conference on Mechatronics - Mechatronika (ME)",
year="2024",
series="21",
number="1",
pages="5",
publisher="IEEE",
address="Brno, Czech Republic",
doi="10.1109/ME61309.2024.10789718",
isbn="979-8-3503-9490-0",
url="https://ieeexplore.ieee.org/document/10789718"
}
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[popis_en] => This paper explores the modal properties tuning of mechanical systems using piezoelectric transducers. The study focuses on piezoelectric shunt damping techniques, particularly Synchronous Switch Damping on Voltage (SSDV), to mitigate vibration and alter resonant frequencies. Simulations and experiments were conducted to investigate the influence of phase shifts and applied voltage on the system's dynamics. The simulation results indicate significant changes in the resonant frequency at certain phase shifts, while the experimental results showed partial alignment with the simulations, revealing challenges in electronics and the need for precise control. This research contributes to the development of adaptive vibration control technologies, enhancing the stability and lifespan of mechanical systems.
[klicova_slova_en] => Damping
,
Vibrations
,
Adaptation models
,
System dynamics
,
Simulation
,
Resonant frequency
,
Vibration control
,
Mechanical systems
,
Voltage control
,
Tuning
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[nazev] => Inverse identification of thermal behaviour of a paraffin-based phase change material in complete and partial phase change cycles
[nazev_orig] => Inverse identification of thermal behaviour of a paraffin-based phase change material in complete and partial phase change cycles
[duvernost_udaju_id] => S
[popis] => From the macroscopic point of view, phase change hysteresis (PCH) means that a phase change process (e.g. solidification) does not follow the same temperature-enthalpy path as the opposite phase change process (melting). Although the PCH is observed in most phase change materials (PCMs), it is often neglected in computational models, resulting in discrepancies when compared to experimental data. The PCH can particularly be an issue in the modelling of latent heat thermal energy storage systems, where incomplete (partial) phase transitions are quite common. Lab-scale experimental methods for characterisation of PCMs, such as differential scanning calorimetry or the temperature-history method, employ only small PCM samples, and the obtained results are often insufficient for predicting the thermal behaviour of large volumes of PCMs. The present study explores numerical modelling approaches to the PCH, addressing both complete and partial melting-to-solidification cycles. A set of validation experiments was performed, focusing on phase transitions in a paraffin-based PCM enclosed in a rectangular cavity. An inverse identification method was used to minimise the root mean square error (RMSE) of temperatures in the PCM using the particle swarm optimisation method. A two-curve approach showed the highest accuracy in complete phase change cycles, with a 62% improvement in the RMSE when compared to the manufacturer data. As for cycles with partial phase changes, a curve-scale approach showed superior behaviour, reducing the RMSE as much as 99%. Conversely, another investigated approach – a curve-track model – exhibited inferior performance, making it less suitable for the modelling of partial phase changes.
[popis_orig] => From the macroscopic point of view, phase change hysteresis (PCH) means that a phase change process (e.g. solidification) does not follow the same temperature-enthalpy path as the opposite phase change process (melting). Although the PCH is observed in most phase change materials (PCMs), it is often neglected in computational models, resulting in discrepancies when compared to experimental data. The PCH can particularly be an issue in the modelling of latent heat thermal energy storage systems, where incomplete (partial) phase transitions are quite common. Lab-scale experimental methods for characterisation of PCMs, such as differential scanning calorimetry or the temperature-history method, employ only small PCM samples, and the obtained results are often insufficient for predicting the thermal behaviour of large volumes of PCMs. The present study explores numerical modelling approaches to the PCH, addressing both complete and partial melting-to-solidification cycles. A set of validation experiments was performed, focusing on phase transitions in a paraffin-based PCM enclosed in a rectangular cavity. An inverse identification method was used to minimise the root mean square error (RMSE) of temperatures in the PCM using the particle swarm optimisation method. A two-curve approach showed the highest accuracy in complete phase change cycles, with a 62% improvement in the RMSE when compared to the manufacturer data. As for cycles with partial phase changes, a curve-scale approach showed superior behaviour, reducing the RMSE as much as 99%. Conversely, another investigated approach – a curve-track model – exhibited inferior performance, making it less suitable for the modelling of partial phase changes.
[klicova_slova] => Inverse problem; Phase change materials; Phase change hysteresis; Complete and partial phase changes; Particle swarm optimisation
[klicova_slova_orig] => Inverse problem; Phase change materials; Phase change hysteresis; Complete and partial phase changes; Particle swarm optimisation
[url] => https://www.sciencedirect.com/science/article/pii/S2451904924002038
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[citace_text] => ZÁLEŠÁK, M.; CHARVÁT, P.; KLIMEŠ, L.; KŮDELA, J.; PECH, O. Inverse identification of thermal behaviour of a paraffin-based phase change material in complete and partial phase change cycles. Thermal Science and Engineering Progress, 2024, vol. 51, no. 1, 18 p. ISSN: 2451-9049.
[citace_html] => ZÁLEŠÁK, M.; CHARVÁT, P.; KLIMEŠ, L.; KŮDELA, J.; PECH, O. Inverse identification of thermal behaviour of a paraffin-based phase change material in complete and partial phase change cycles. Thermal Science and Engineering Progress, 2024, vol. 51, no. 1, 18 p. ISSN: 2451-9049.
[citace_rtf] =>
[citace_bibtex] => @article{BUT194168,
author="Martin {Zálešák} and Pavel {Charvát} and Lubomír {Klimeš} and Jakub {Kůdela} and Ondřej {Pech}",
title="Inverse identification of thermal behaviour of a paraffin-based phase change material in complete and partial phase change cycles",
journal="Thermal Science and Engineering Progress",
year="2024",
volume="51",
number="1",
pages="18",
doi="10.1016/j.tsep.2024.102585",
issn="2451-9057",
url="https://www.sciencedirect.com/science/article/pii/S2451904924002038"
}
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[oecd_tree_podobor_id] => 20303
[oecd_tree_podobor_nazev] => Thermodynamics
[poznamka_metriky] =>
[nazev_en] => Inverse identification of thermal behaviour of a paraffin-based phase change material in complete and partial phase change cycles
[popis_en] => From the macroscopic point of view, phase change hysteresis (PCH) means that a phase change process (e.g. solidification) does not follow the same temperature-enthalpy path as the opposite phase change process (melting). Although the PCH is observed in most phase change materials (PCMs), it is often neglected in computational models, resulting in discrepancies when compared to experimental data. The PCH can particularly be an issue in the modelling of latent heat thermal energy storage systems, where incomplete (partial) phase transitions are quite common. Lab-scale experimental methods for characterisation of PCMs, such as differential scanning calorimetry or the temperature-history method, employ only small PCM samples, and the obtained results are often insufficient for predicting the thermal behaviour of large volumes of PCMs. The present study explores numerical modelling approaches to the PCH, addressing both complete and partial melting-to-solidification cycles. A set of validation experiments was performed, focusing on phase transitions in a paraffin-based PCM enclosed in a rectangular cavity. An inverse identification method was used to minimise the root mean square error (RMSE) of temperatures in the PCM using the particle swarm optimisation method. A two-curve approach showed the highest accuracy in complete phase change cycles, with a 62% improvement in the RMSE when compared to the manufacturer data. As for cycles with partial phase changes, a curve-scale approach showed superior behaviour, reducing the RMSE as much as 99%. Conversely, another investigated approach – a curve-track model – exhibited inferior performance, making it less suitable for the modelling of partial phase changes.
[klicova_slova_en] => Inverse problem; Phase change materials; Phase change hysteresis; Complete and partial phase changes; Particle swarm optimisation
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[popis] => This article addresses the challenge of selecting the most suitable optimization algorithm by presenting a comprehensive computational comparison between stochastic and deterministic methods. The complexity of algorithm selection arises from the absence of a universal algorithm and the abundance of available options. Manual selection without comprehensive studies can lead to suboptimal or incorrect results. In order to address this issue, we carefully selected 25 promising and representative state-of-the-art algorithms from both aforementioned classes. The evaluation with up to the 20 dimensions and large evaluation budgets $(10<^>{5}{\times }n)$ was carried out in a significantly expanded and improved version of the DIRECTGOLib v2.0 library, which included ten distinct collections of primarily continuous test functions. The evaluation covered various aspects, such as solution quality, time complexity, and function evaluation usage. The rankings were determined using statistical tests and performance profiles. When it comes to the problems and algorithms examined in this study, EA4eig, EBOwithCMAR, APGSK-IMODE, 1-DTC-GL, OQNLP, and DIRMIN stand out as superior to other derivative-free solvers in terms of solution quality. While deterministic algorithms can locate reasonable solutions with comparatively fewer function evaluations, most stochastic algorithms require more extensive evaluation budgets to deliver comparable results. However, the performance of stochastic algorithms tends to excel in more complex and higher-dimensional problems. These research findings offer valuable insights for practitioners and researchers, enabling them to tackle diverse optimization problems effectively.
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[klicova_slova] => Derivative-free global optimization; deterministic algorithms; evolutionary computation (EC) algorithms;
nature-inspired meta-heuristics; numerical benchmarking
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nature-inspired meta-heuristics; numerical benchmarking
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[citace_text] => STRIPINIS, L.; KŮDELA, J.; PAULAVIČIUS, R. Benchmarking Derivative-Free Global Optimization Algorithms Under Limited Dimensions and Large Evaluation Budgets. IEEE transactions on evolutionary computation, 2025, vol. 29, no. 1, p. 187-204. ISSN: 1089-778X.
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author="Linas {Stripinis} and Jakub {Kůdela} and Remigijus {Paulavičius}",
title="Benchmarking Derivative-Free Global Optimization Algorithms Under Limited Dimensions and Large Evaluation Budgets",
journal="IEEE transactions on evolutionary computation",
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volume="29",
number="1",
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[nazev_en] => Benchmarking Derivative-Free Global Optimization Algorithms Under Limited Dimensions and Large Evaluation Budgets
[popis_en] => This article addresses the challenge of selecting the most suitable optimization algorithm by presenting a comprehensive computational comparison between stochastic and deterministic methods. The complexity of algorithm selection arises from the absence of a universal algorithm and the abundance of available options. Manual selection without comprehensive studies can lead to suboptimal or incorrect results. In order to address this issue, we carefully selected 25 promising and representative state-of-the-art algorithms from both aforementioned classes. The evaluation with up to the 20 dimensions and large evaluation budgets $(10<^>{5}{\times }n)$ was carried out in a significantly expanded and improved version of the DIRECTGOLib v2.0 library, which included ten distinct collections of primarily continuous test functions. The evaluation covered various aspects, such as solution quality, time complexity, and function evaluation usage. The rankings were determined using statistical tests and performance profiles. When it comes to the problems and algorithms examined in this study, EA4eig, EBOwithCMAR, APGSK-IMODE, 1-DTC-GL, OQNLP, and DIRMIN stand out as superior to other derivative-free solvers in terms of solution quality. While deterministic algorithms can locate reasonable solutions with comparatively fewer function evaluations, most stochastic algorithms require more extensive evaluation budgets to deliver comparable results. However, the performance of stochastic algorithms tends to excel in more complex and higher-dimensional problems. These research findings offer valuable insights for practitioners and researchers, enabling them to tackle diverse optimization problems effectively.
[klicova_slova_en] => Derivative-free global optimization; deterministic algorithms; evolutionary computation (EC) algorithms;
nature-inspired meta-heuristics; numerical benchmarking
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[popis_orig] =>
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[citace_text] => Genetic and Evolutionary Computation Conference 2024. Melbourne (14.07.2024)
[citace_html] => Genetic and Evolutionary Computation Conference 2024. Melbourne (14.07.2024)
[citace_rtf] =>
[citace_bibtex] => @misc{BUT196882,
title="Genetic and Evolutionary Computation Conference 2024",
year="2024",
url="https://gecco-2024.sigevo.org/HomePage",
note="Holding a conference"
}
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[popis] => This Hot Off the Press paper provides a brief summary of our recent work "Benchmarking Derivative-Free Global Optimization Algorithms under Limited Dimensions and Large Evaluation Budgets" published in IEEE Transactions on Evolutionary Computation [5]. In the paper, we performed a comprehensive computational comparison between stochastic and deterministic global optimization algorithms with twenty-five representative state-of-the-art methods selected from both classes. The experiments were set up with up to twenty dimensions and relatively large evaluation budgets (105 X n). Benchmarking was carried out in a significantly expanded version of the DIRECTGOLib v2.0 library, which included ten distinct collections of primarily continuous test functions. The evaluation of the methods focused on various aspects, such as solution quality, time complexity, and function evaluation usage. The rankings were determined using statistical tests and performance profiles. Our findings suggest that while state-of-the-art deterministic methods could find reasonable solutions with comparatively fewer function evaluations, most stochastic algorithms require more extensive evaluation budgets to deliver comparable results. However, the performance of stochastic algorithms excelled in more complex and higher-dimensional problems. These research findings offer valuable insights for practitioners and researchers, enabling them to tackle diverse optimization problems effectively.
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[klicova_slova] => Numerical benchmarking; derivative-free global optimization; evolutionary computation algorithms; nature-inspired meta-heuristics; deterministic algorithms
[klicova_slova_orig] => Numerical benchmarking; derivative-free global optimization; evolutionary computation algorithms; nature-inspired meta-heuristics; deterministic algorithms
[url] => https://dl.acm.org/doi/10.1145/3638530.3664072
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[citace_text] => STRIPINIS, L.; KŮDELA, J.; PAULAVIČIUS, R. Hot Off the Press: Benchmarking Derivative-Free Global Optimization Algorithms under Limited Dimensions and Large Evaluation Budgets. In 2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion. Association for Computing Machinery, Inc, 2024. p. 57-58. ISBN: 979-8-4007-0495-6.
[citace_html] => STRIPINIS, L.; KŮDELA, J.; PAULAVIČIUS, R. Hot Off the Press: Benchmarking Derivative-Free Global Optimization Algorithms under Limited Dimensions and Large Evaluation Budgets. In 2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion. Association for Computing Machinery, Inc, 2024. p. 57-58. ISBN: 979-8-4007-0495-6.
[citace_rtf] =>
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author="Linas {Stripinis} and Jakub {Kůdela} and Remigijus {Paulavičius}",
title="Hot Off the Press: Benchmarking Derivative-Free Global Optimization Algorithms under Limited Dimensions and Large Evaluation Budgets",
booktitle="2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion",
year="2024",
pages="57--58",
publisher="Association for Computing Machinery, Inc",
doi="10.1145/3638530.3664072",
isbn="979-8-4007-0495-6",
url="https://dl.acm.org/doi/10.1145/3638530.3664072"
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[nazev_en] => Hot Off the Press: Benchmarking Derivative-Free Global Optimization Algorithms under Limited Dimensions and Large Evaluation Budgets
[popis_en] => This Hot Off the Press paper provides a brief summary of our recent work "Benchmarking Derivative-Free Global Optimization Algorithms under Limited Dimensions and Large Evaluation Budgets" published in IEEE Transactions on Evolutionary Computation [5]. In the paper, we performed a comprehensive computational comparison between stochastic and deterministic global optimization algorithms with twenty-five representative state-of-the-art methods selected from both classes. The experiments were set up with up to twenty dimensions and relatively large evaluation budgets (105 X n). Benchmarking was carried out in a significantly expanded version of the DIRECTGOLib v2.0 library, which included ten distinct collections of primarily continuous test functions. The evaluation of the methods focused on various aspects, such as solution quality, time complexity, and function evaluation usage. The rankings were determined using statistical tests and performance profiles. Our findings suggest that while state-of-the-art deterministic methods could find reasonable solutions with comparatively fewer function evaluations, most stochastic algorithms require more extensive evaluation budgets to deliver comparable results. However, the performance of stochastic algorithms excelled in more complex and higher-dimensional problems. These research findings offer valuable insights for practitioners and researchers, enabling them to tackle diverse optimization problems effectively.
[klicova_slova_en] => Numerical benchmarking; derivative-free global optimization; evolutionary computation algorithms; nature-inspired meta-heuristics; deterministic algorithms
[vysledek_datum] => 2024-08-01T00:00:00+02:00
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[nazev] => Hot Off the Press: Soft computing methods in the solution of an inverse heat transfer problem with phase change
[nazev_orig] => Hot Off the Press: Soft computing methods in the solution of an inverse heat transfer problem with phase change
[duvernost_udaju_id] => S
[popis] => This Hot Off the Press paper summarizes our recent work “Soft computing methods in the solution of an inverse heat transfer problem with phase change: A comparative study” published in Engineering Applications of Artificial Intelligence [5]. In the paper, we study inverse heat transfer problems with phase change, where the
boundary heat flux is estimated. Such problems are ill-posed and their solution is challenging. Although there were conventional developed for this problem in the past, they are not well-suited for cases including phase change, as these contain strong nonlinearities that bring additional computational difficulties. For such
problems, soft computing methods provide a promising approach. Four methods from distinct categories of techniques are applied to this problem and thoroughly compared - the conventional gradientbased
method, a fuzzy logic-based method, a population-based meta-heuristic, and a surrogate-assisted method. A reformulation of the problem utilizing dimension reduction and decomposition schemes was developed, bringing extensive computational improvements. The metaheuristic and the surrogate-based methods showed
superior performance. Their performance was also rather stable and insensitive to the location of the temperature sensor (the source of data for the inverse estimation). A Zenodo repository with the complete implementation of all considered problems and methods is available
[popis_orig] => This Hot Off the Press paper summarizes our recent work “Soft computing methods in the solution of an inverse heat transfer problem with phase change: A comparative study” published in Engineering Applications of Artificial Intelligence [5]. In the paper, we study inverse heat transfer problems with phase change, where the
boundary heat flux is estimated. Such problems are ill-posed and their solution is challenging. Although there were conventional developed for this problem in the past, they are not well-suited for cases including phase change, as these contain strong nonlinearities that bring additional computational difficulties. For such
problems, soft computing methods provide a promising approach. Four methods from distinct categories of techniques are applied to this problem and thoroughly compared - the conventional gradientbased
method, a fuzzy logic-based method, a population-based meta-heuristic, and a surrogate-assisted method. A reformulation of the problem utilizing dimension reduction and decomposition schemes was developed, bringing extensive computational improvements. The metaheuristic and the surrogate-based methods showed
superior performance. Their performance was also rather stable and insensitive to the location of the temperature sensor (the source of data for the inverse estimation). A Zenodo repository with the complete implementation of all considered problems and methods is available
[klicova_slova] => Inverse heat transfer; Soft computing; Machine learning; Metaheuristics; Surrogate model; Fuzzy logic
[klicova_slova_orig] => Inverse heat transfer; Soft computing; Machine learning; Metaheuristics; Surrogate model; Fuzzy logic
[url] => https://dl.acm.org/doi/10.1145/3638530.3664073
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[citace_text] => MAUDER, T.; KŮDELA, J.; KLIMEŠ, L.; ZÁLEŠÁK, M.; CHARVÁT, P. Hot Off the Press: Soft computing methods in the solution of an inverse heat transfer problem with phase change. In 2024 Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery, Inc, 2024. p. 47-48. ISBN: 979-8-4007-0495-6.
[citace_html] => MAUDER, T.; KŮDELA, J.; KLIMEŠ, L.; ZÁLEŠÁK, M.; CHARVÁT, P. Hot Off the Press: Soft computing methods in the solution of an inverse heat transfer problem with phase change. In 2024 Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery, Inc, 2024. p. 47-48. ISBN: 979-8-4007-0495-6.
[citace_rtf] =>
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author="Tomáš {Mauder} and Jakub {Kůdela} and Lubomír {Klimeš} and Martin {Zálešák} and Pavel {Charvát}",
title="Hot Off the Press: Soft computing methods in the solution of an inverse heat transfer problem with phase change",
booktitle="2024 Genetic and Evolutionary Computation Conference Companion",
year="2024",
pages="47--48",
publisher="Association for Computing Machinery, Inc",
doi="10.1145/3638530.3664073",
isbn="979-8-4007-0495-6",
url="https://dl.acm.org/doi/10.1145/3638530.3664073"
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[nazev_en] => Hot Off the Press: Soft computing methods in the solution of an inverse heat transfer problem with phase change
[popis_en] => This Hot Off the Press paper summarizes our recent work “Soft computing methods in the solution of an inverse heat transfer problem with phase change: A comparative study” published in Engineering Applications of Artificial Intelligence [5]. In the paper, we study inverse heat transfer problems with phase change, where the
boundary heat flux is estimated. Such problems are ill-posed and their solution is challenging. Although there were conventional developed for this problem in the past, they are not well-suited for cases including phase change, as these contain strong nonlinearities that bring additional computational difficulties. For such
problems, soft computing methods provide a promising approach. Four methods from distinct categories of techniques are applied to this problem and thoroughly compared - the conventional gradientbased
method, a fuzzy logic-based method, a population-based meta-heuristic, and a surrogate-assisted method. A reformulation of the problem utilizing dimension reduction and decomposition schemes was developed, bringing extensive computational improvements. The metaheuristic and the surrogate-based methods showed
superior performance. Their performance was also rather stable and insensitive to the location of the temperature sensor (the source of data for the inverse estimation). A Zenodo repository with the complete implementation of all considered problems and methods is available
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[duvernost_udaju_id] => S
[popis] => Several researchers have turned their attention to the structural components of Evolutionary Computation and
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approaches. It also discusses how these algorithmic components can be integrated into modular frameworks and how they can be assessed and benchmarked. The work aims to emphasize the importance of the research direction about nature-inspired mechanisms and operators in the Evolutionary Computation field.
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Swarm Intelligence-oriented approaches. This direction offers various opportunities, such as developing automatic design and configuration frameworks and integrating operators and mechanisms addressing known limitations. This work lists recent operators and discusses promising mechanisms found in existing atureinspired
approaches. It also discusses how these algorithmic components can be integrated into modular frameworks and how they can be assessed and benchmarked. The work aims to emphasize the importance of the research direction about nature-inspired mechanisms and operators in the Evolutionary Computation field.
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Modular Algorithms.
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Modular Algorithms.
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[citace_text] => TZANETOS, A.; KŮDELA, J. Working on the Structural Components of Evolutionary Approaches. In Proceedings of the 16th International Joint Conference on Computational Intelligence. Science and Technology Publications, Lda, 2024. p. 375-382. ISBN: 978-989-758-721-4.
[citace_html] => TZANETOS, A.; KŮDELA, J. Working on the Structural Components of Evolutionary Approaches. In Proceedings of the 16th International Joint Conference on Computational Intelligence. Science and Technology Publications, Lda, 2024. p. 375-382. ISBN: 978-989-758-721-4.
[citace_rtf] =>
[citace_bibtex] => @inproceedings{BUT196899,
author="Alexandros {Tzanetos} and Jakub {Kůdela}",
title="Working on the Structural Components of Evolutionary Approaches",
booktitle="Proceedings of the 16th International Joint Conference on Computational Intelligence",
year="2024",
pages="375--382",
publisher="Science and Technology Publications, Lda",
doi="10.5220/0013083400003837",
isbn="978-989-758-721-4",
url="https://www.scitepress.org/Link.aspx?doi=10.5220/0013083400003837"
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approaches. It also discusses how these algorithmic components can be integrated into modular frameworks and how they can be assessed and benchmarked. The work aims to emphasize the importance of the research direction about nature-inspired mechanisms and operators in the Evolutionary Computation field.
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[duvernost_udaju_id] => S
[popis] => Surrogate-assisted evolutionary algorithms (SAEAs) are recently among the most widely studied methods for their capability to solve expensive real-world optimization problems. However, the development of new methods and benchmarking with other techniques still relies almost exclusively on artificially created problems. In this paper, we use two real-world computational fluid dynamics problems to compare the performance of eleven state-of-the-art single-objective SAEAs. We analyze the performance by investigating the quality and robustness of the obtained solutions and the convergence properties of the selected methods. Our findings suggest that the more recently published methods, as well as the techniques that utilize differential evolution as one of their optimization mechanisms, perform significantly better than the other considered methods.
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[klicova_slova] => Expensive optimization; evolutionary algorithm; surrogate model; computational fluid dynamics; benchmarking
[klicova_slova_orig] => Expensive optimization; evolutionary algorithm; surrogate model; computational fluid dynamics; benchmarking
[url] => https://link.springer.com/chapter/10.1007/978-3-031-70068-2_19
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[citace_text] => KŮDELA, J.; DOBROVSKÝ, L. Performance Comparison of Surrogate-Assisted Evolutionary Algorithms on Computational Fluid Dynamics Problems. In 18th International Conference on Parallel Problem Solving from Nature. Springer Science and Business Media Deutschland GmbH, 2024. p. 303-321. ISBN: 978-3-031-70068-2.
[citace_html] => KŮDELA, J.; DOBROVSKÝ, L. Performance Comparison of Surrogate-Assisted Evolutionary Algorithms on Computational Fluid Dynamics Problems. In 18th International Conference on Parallel Problem Solving from Nature. Springer Science and Business Media Deutschland GmbH, 2024. p. 303-321. ISBN: 978-3-031-70068-2.
[citace_rtf] =>
[citace_bibtex] => @inproceedings{BUT196901,
author="Jakub {Kůdela} and Ladislav {Dobrovský}",
title="Performance Comparison of Surrogate-Assisted Evolutionary Algorithms on Computational Fluid Dynamics Problems",
booktitle="18th International Conference on Parallel Problem Solving from Nature",
year="2024",
pages="303--321",
publisher="Springer Science and Business Media Deutschland GmbH",
doi="10.1007/978-3-031-70068-2\{_}19",
isbn="978-3-031-70068-2",
url="https://link.springer.com/chapter/10.1007/978-3-031-70068-2_19"
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[popis_en] => Surrogate-assisted evolutionary algorithms (SAEAs) are recently among the most widely studied methods for their capability to solve expensive real-world optimization problems. However, the development of new methods and benchmarking with other techniques still relies almost exclusively on artificially created problems. In this paper, we use two real-world computational fluid dynamics problems to compare the performance of eleven state-of-the-art single-objective SAEAs. We analyze the performance by investigating the quality and robustness of the obtained solutions and the convergence properties of the selected methods. Our findings suggest that the more recently published methods, as well as the techniques that utilize differential evolution as one of their optimization mechanisms, perform significantly better than the other considered methods.
[klicova_slova_en] => Expensive optimization; evolutionary algorithm; surrogate model; computational fluid dynamics; benchmarking
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[nazev_orig] => Benchmarking Derivative-Free Global Optimization Methods on Variable Dimension Robotics Problems
[duvernost_udaju_id] => S
[popis] => Several real-world applications introduce derivativefree optimization problems, called variable dimension problems, where the problem's dimension is not known in advance. Despite their importance, no unified framework for developing, comparing, and benchmarking variable dimension problems exists. The robot arm controlling problem is a variable dimension problem where the number of joints to optimize defines the problem's dimension. For a holistic study of global optimization methods, we studied 14 representative methods from 4 different categories, i.e., (i) local search optimization techniques with random restarts, (ii) state-of-the-art DIRECT-type methods, (iii) established Evolutionary Computation approaches, and (iv) state-of-the-art Evolutionary Computation approaches. To investigate the effect of the problem's dimensionality on the solution we generated 20 instances of various combinations among the number of predefined and open decision variables, and we performed experiments for various computational budgets. The results attest that the robot arm controlling problem provides a proper benchmark for variable dimensions. Furthermore, methods in-corporating local search techniques have dominant performance for higher dimensionalities of the problem, while state-of-the-art EC methods dominate in the lower dimensionalities.
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[klicova_slova] => benchmarking; derivative-free optimization; global optimization; variable dimension problem; evolutionary
computation
[klicova_slova_orig] => benchmarking; derivative-free optimization; global optimization; variable dimension problem; evolutionary
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[nazev] => CpX Hunter web tool allows high-throughput identification of CpG, CpA, CpT, and CpC islands: A case study in Drosophila genome
[nazev_orig] => CpX Hunter web tool allows high-throughput identification of CpG, CpA, CpT, and CpC islands: A case study in Drosophila genome
[duvernost_udaju_id] => S
[popis] => With continuous advances in DNA sequencing methods, accessibility to high-quality genomic information for all living organisms is ever-increasing. However, to interpret this information effectively and formulate hypotheses, users often require higher level programming skills. Therefore, the generation of web-based tools is becoming increasingly popular. CpG island regions in genomes are often found in gene promoters and are prone to DNA methylation, with their methylation status determining if a gene is expressed. Notably, understanding the biological impact of CpX modifications on genomic regulation is becoming increasingly important as these modifications have been associated with diseases such as cancer and neurodegeneration. However, there is currently no easy-to-use, scalable tool to detect and quantify CpX islands in full genomes. We have developed a Java-based web server for CpX island analyses that benefits from the DNA Analyzer Web server environment and overcomes several limitations. For a pilot demonstration study, we selected a well-described model organism Drosophila melanogaster. Subsequent analysis of the obtained CpX islands revealed several interesting and previously undescribed phenomena. One of them is the fact, that nearly half of long CpG islands were located on chromosome X, and that long CpA and CpT islands were significantly overrepresented at the subcentromeric regions of autosomes (chr2 and chr3) and also on chromosome Y. Wide genome overlays of predicted CpX islands revealed their co-occurrence with various (epi)genomics features comprising cytosine methylations, accessible chromatin, transposable elements, or binding of transcription factors and other proteins. CpX Hunter is freely available as a web tool at: https://bioinformatics.ibp.cz/ #/analyse/cpg.
[popis_orig] => With continuous advances in DNA sequencing methods, accessibility to high-quality genomic information for all living organisms is ever-increasing. However, to interpret this information effectively and formulate hypotheses, users often require higher level programming skills. Therefore, the generation of web-based tools is becoming increasingly popular. CpG island regions in genomes are often found in gene promoters and are prone to DNA methylation, with their methylation status determining if a gene is expressed. Notably, understanding the biological impact of CpX modifications on genomic regulation is becoming increasingly important as these modifications have been associated with diseases such as cancer and neurodegeneration. However, there is currently no easy-to-use, scalable tool to detect and quantify CpX islands in full genomes. We have developed a Java-based web server for CpX island analyses that benefits from the DNA Analyzer Web server environment and overcomes several limitations. For a pilot demonstration study, we selected a well-described model organism Drosophila melanogaster. Subsequent analysis of the obtained CpX islands revealed several interesting and previously undescribed phenomena. One of them is the fact, that nearly half of long CpG islands were located on chromosome X, and that long CpA and CpT islands were significantly overrepresented at the subcentromeric regions of autosomes (chr2 and chr3) and also on chromosome Y. Wide genome overlays of predicted CpX islands revealed their co-occurrence with various (epi)genomics features comprising cytosine methylations, accessible chromatin, transposable elements, or binding of transcription factors and other proteins. CpX Hunter is freely available as a web tool at: https://bioinformatics.ibp.cz/ #/analyse/cpg.
[klicova_slova] => CpA islands; CpG islands; CpT islands; dinucleotide; Drosophila; genome analyses; web server
[klicova_slova_orig] => CpA islands; CpG islands; CpT islands; dinucleotide; Drosophila; genome analyses; web server
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[citace_text] => BARTAS, M.; PETROVIČ, M.; BRÁZDA, V.; TRENZ, O.; ĎURČANSKÝ, A.; ŠŤASTNÝ, J. CpX Hunter web tool allows high-throughput identification of CpG, CpA, CpT, and CpC islands: A case study in Drosophila genome. Journal of biological chemistry, 2025, vol. 301, no. 6, p. 1-11. ISSN: 0021-9258.
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author="Martin {Bartas} and Michal {Petrovič} and Václav {Brázda} and Oldřich {Trenz} and Aleš {Ďurčanský} and Jiří {Šťastný}",
title="CpX Hunter web tool allows high-throughput identification of CpG, CpA, CpT, and CpC islands: A case study in Drosophila genome",
journal="Journal of biological chemistry",
year="2025",
volume="301",
number="6",
pages="1--11",
doi="10.1016/j.jbc.2025.108537",
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url="https://www.sciencedirect.com/science/article/pii/S0021925825003862?via%3Dihub"
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[nazev_en] => CpX Hunter web tool allows high-throughput identification of CpG, CpA, CpT, and CpC islands: A case study in Drosophila genome
[popis_en] => With continuous advances in DNA sequencing methods, accessibility to high-quality genomic information for all living organisms is ever-increasing. However, to interpret this information effectively and formulate hypotheses, users often require higher level programming skills. Therefore, the generation of web-based tools is becoming increasingly popular. CpG island regions in genomes are often found in gene promoters and are prone to DNA methylation, with their methylation status determining if a gene is expressed. Notably, understanding the biological impact of CpX modifications on genomic regulation is becoming increasingly important as these modifications have been associated with diseases such as cancer and neurodegeneration. However, there is currently no easy-to-use, scalable tool to detect and quantify CpX islands in full genomes. We have developed a Java-based web server for CpX island analyses that benefits from the DNA Analyzer Web server environment and overcomes several limitations. For a pilot demonstration study, we selected a well-described model organism Drosophila melanogaster. Subsequent analysis of the obtained CpX islands revealed several interesting and previously undescribed phenomena. One of them is the fact, that nearly half of long CpG islands were located on chromosome X, and that long CpA and CpT islands were significantly overrepresented at the subcentromeric regions of autosomes (chr2 and chr3) and also on chromosome Y. Wide genome overlays of predicted CpX islands revealed their co-occurrence with various (epi)genomics features comprising cytosine methylations, accessible chromatin, transposable elements, or binding of transcription factors and other proteins. CpX Hunter is freely available as a web tool at: https://bioinformatics.ibp.cz/ #/analyse/cpg.
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[nazev] => Molecularly Imprinted Polymer-Based Electronic Nose for Ultrasensitive, Selective Detection, and Concentration Estimation of VOC Mixtures
[nazev_orig] => Molecularly Imprinted Polymer-Based Electronic Nose for Ultrasensitive, Selective Detection, and Concentration Estimation of VOC Mixtures
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[popis] => This research introduces a groundbreaking electronic nose (E-Nose) that integrates advanced sensing materials with machine learning (ML). The sensing materials include molecularly imprinted polymers (MIPs) and multiwalled carbon nanotubes (MWCNTs), designed for enhanced performance. An optimized extreme learning machine (ELM) model enables highly selective detection and precise quantification of both individual and multiple volatile organic compounds (VOCs) within complex mixtures. Specifically, with transducers functionalized for specificity toward methanol, ethanol, butanol, and isopropanol, the proposed E-Nose achieved near-perfect estimation with an error of just 0.25% for individual VOCs and negligible error (0.75%-1.5%) for mixtures of two to four VOCs. The developed E-Nose demonstrated linear estimation of target VOC concentrations with high sensitivity and selectivity. Detection limits (DL) for all gases remained below safety thresholds, ensuring suitability for practical VOC sensing at room temperature (RT). Furthermore, the proposed E-Nose platform is adaptable and customizable for detecting and estimating the tested VOCs as well as other VOCs and gases, offering significant potential to revolutionize air quality monitoring.
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[klicova_slova] => Sensors; Sensitivity; Estimation; Accuracy; Training; Intelligent sensors; Chemical sensors; Transducers; Sensor arrays; Plastics; Air-quality monitoring; butanol; carbon nanotube; electronic nose (E-Nose); ethanol; extreme learning machine (ELM); isopropanol; machine learning (ML); methanol; molecularly imprinted polymer (MIP); volatile organic compound (VOC) sensor
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[url] => https://ieeexplore.ieee.org/document/10955118
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[nazev_en] => Molecularly Imprinted Polymer-Based Electronic Nose for Ultrasensitive, Selective Detection, and Concentration Estimation of VOC Mixtures
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[nazev] => Benchmarking global optimization techniques for unmanned aerial vehicle path planning
[nazev_orig] => Benchmarking global optimization techniques for unmanned aerial vehicle path planning
[duvernost_udaju_id] => S
[popis] => The Unmanned Aerial Vehicle (UAV) path planning problem is a complex optimization problem in the field of robotics. In this paper, we investigate the possible utilization of this problem in benchmarking global optimization methods. We devise a problem instance generator and pick 56 representative instances, which we compare to established benchmarking suits through Exploratory Landscape Analysis to show their uniqueness. For the computational comparison, we select fourteen well-performing global optimization techniques from both subfields of stochastic algorithms (evolutionary computation methods) and deterministic algorithms (Dividing RECTangles, or DIRECT-type methods). The experiments were conducted in settings with varying dimensionality and computational budgets. The results were analyzed through several criteria (number of best-found solutions, mean relative error, Friedman ranks) and utilized established statistical tests. The best-ranking methods for the UAV problems were almost universally the top-performing evolutionary techniques from recent competitions on numerical optimization at the Institute of Electrical and Electronics Engineers Congress on Evolutionary Computation. Lastly, we discussed the variable dimension characteristics of the studied UAV problems that remain still largely under-investigated. The code and results are available at a Zenodo repository https://doi.org/10.5281/zenodo.15424080.
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[klicova_slova] => Unmanned aerial vehicle; Path planning; Benchmarking; Global optimization; Exploratory landscape analysis; Variable dimension problem
[klicova_slova_orig] => Unmanned aerial vehicle; Path planning; Benchmarking; Global optimization; Exploratory landscape analysis; Variable dimension problem
[url] => https://www.sciencedirect.com/science/article/pii/S095741742502264X
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[citace_text] => SHEHADEH, M.; KŮDELA, J. Benchmarking global optimization techniques for unmanned aerial vehicle path planning. Expert Systems with Applications, 2025, vol. 293, no. Dec, p. 1-19. ISSN: 0957-4174.
[citace_html] => SHEHADEH, M.; KŮDELA, J. Benchmarking global optimization techniques for unmanned aerial vehicle path planning. Expert Systems with Applications, 2025, vol. 293, no. Dec, p. 1-19. ISSN: 0957-4174.
[citace_rtf] =>
[citace_bibtex] => @article{BUT198283,
author="Mhd Ali {Shehadeh} and Jakub {Kůdela}",
title="Benchmarking global optimization techniques for unmanned aerial vehicle path planning",
journal="Expert Systems with Applications",
year="2025",
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number="Dec",
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doi="10.1016/j.eswa.2025.128645",
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[nazev_en] => Benchmarking global optimization techniques for unmanned aerial vehicle path planning
[popis_en] => The Unmanned Aerial Vehicle (UAV) path planning problem is a complex optimization problem in the field of robotics. In this paper, we investigate the possible utilization of this problem in benchmarking global optimization methods. We devise a problem instance generator and pick 56 representative instances, which we compare to established benchmarking suits through Exploratory Landscape Analysis to show their uniqueness. For the computational comparison, we select fourteen well-performing global optimization techniques from both subfields of stochastic algorithms (evolutionary computation methods) and deterministic algorithms (Dividing RECTangles, or DIRECT-type methods). The experiments were conducted in settings with varying dimensionality and computational budgets. The results were analyzed through several criteria (number of best-found solutions, mean relative error, Friedman ranks) and utilized established statistical tests. The best-ranking methods for the UAV problems were almost universally the top-performing evolutionary techniques from recent competitions on numerical optimization at the Institute of Electrical and Electronics Engineers Congress on Evolutionary Computation. Lastly, we discussed the variable dimension characteristics of the studied UAV problems that remain still largely under-investigated. The code and results are available at a Zenodo repository https://doi.org/10.5281/zenodo.15424080.
[klicova_slova_en] => Unmanned aerial vehicle; Path planning; Benchmarking; Global optimization; Exploratory landscape analysis; Variable dimension problem
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