Modified genetic algorithm using Box Complex method: Application to optimal control problems

Patel, Narendra; Padhiyar, Nitin

doi:10.1016/j.jprocont.2015.01.001

Cited by 19 publications

(6 citation statements)

References 45 publications

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“…GAs are a family of popular heuristic optimization techniques that search the parameter space for the optimal solution of a problem in a manner inspired by Darwin's principle of natural selection (Holland, 1975). GAs have generally higher demands in CPU time compared to gradientbased algorithms, but they are capable of providing potentially global optimal solutions for complex functions (Patel and Padhiyar, 2015). The parameter vectors ( 1, , 2, 1, , 2, ) and ( 1, , 2, , 1, , 2, ) were bounded between 0-20 seconds and 0-3 seconds, respectively.…”

Section: Parameter Estimationmentioning

confidence: 99%

Identification of physiological response functions to correct for fluctuations in resting-state fMRI related to heart rate and respiration

Kassinopoulos

Mitsis

2019

NeuroImage

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Highlights:• Physiological response functions () vary considerably across subjects/sessions• Scan-specific curves can be obtained from data records longer than 5 minutes• The shape of the cardiac response function is linked to the mean heart rate (HR)• Brain regions affected by HR and breathing patterns exhibit substantial overlap• HR and breathing patterns affect distinct regions as compared to cardiac pulsatility

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Section: Parameter Estimationmentioning

confidence: 99%

Identification of physiological response functions to correct for fluctuations in resting-state fMRI related to heart rate and respiration

Kassinopoulos

Mitsis

2019

NeuroImage

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“…GAs are a family of popular heuristic optimization techniques that search the parameter space for the optimal solution of a problem in a manner inspired by Darwin's principle of natural selection (Holland, 1975). GAs have generally higher demands in CPU time compared to gradient-based algorithms, but 235 they are capable of providing potentially global optimal solutions for complex functions (Patel and Padhiyar, 2015). The parameters ( 1, , 2, 1, , 2, ) and ( 1, , 2, , 1, , 2, ) were bounded between 0-20 seconds and 0-3 seconds, respectively.…”

Section: Parameter Estimationmentioning

confidence: 99%

Identification of Physiological Response Functions to Correct for Fluctuations in Resting-State fMRI related to Heart Rate and Respiration

Kassinopoulos

Mitsis

2019

Preprint

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Functional magnetic resonance imaging (fMRI) is widely viewed as the gold standard for studying brain function due to its high spatial resolution and non-invasive nature. However, it is well established that changes in breathing patterns and heart rate strongly influence the blood oxygen-level dependent (BOLD) fMRI signal and this, in turn, can have considerable effects on fMRI studies, particularly resting-state studies. The dynamic effects of physiological processes are often quantified by using convolution models along with simultaneously recorded physiological data. In this context, physiological response function ( ) curves (cardiac and respiratory response functions), which are convolved with the corresponding physiological fluctuations, are commonly employed. Initially, these curves were assumed to be identical across subjects, but more recently, the use of subject-specific curves has been suggested (derived by e.g. using the global fMRI signal). In the present study, we propose a novel framework for the robust estimation of curves and use this framework to rigorously examine the implications of using population-, subject-, session-and scanspecific curves. The proposed framework was tested on resting-state fMRI and physiological data from the Human Connectome Project. Our results suggest that curves vary significantly across subjects and, to a lesser extent, across sessions from the same subject. These differences can be partly attributed to physiological variables such as the mean and variance of the heart rate during the scan. The proposed methodological framework can be used to obtain robust scan-specific curves from data records with duration longer than 5 minutes, exhibiting significantly improved performance compared to previously defined canonical cardiac and respiration response functions. Besides removing physiological confounds from the BOLD signal, accurate modeling of subject-(or session-/scan-) specific curves is of importance in studies that involve populations with altered vascular responses, such as aging subjects.

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“…The GTSP has been widely studied, since it can be the model for many practical problems in industries such as flowshop scheduling and toolpath planning [22][23][24] . The optimization issue can be dealed with a genetic algorithm (GA) combined with some powerful algorithms for local searching [25][26][27] .…”

Section: Figure 1 Pictures Of Modernizing the Traditional Solar Greenmentioning

confidence: 99%

Sustainable energy management of solar greenhouses using open weather data on MACQU platform

Wang

et al. 2018

International Journal of Agricultural and Biological Engineering

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Precision energy management is very important for sustainability development of solar greenhouses, since huge energy demand for agricultural production both in quantity and quality. A proactive energy management, according to the optimal energy utilization in a look-ahead period with weather prediction, is presented and tested in this research. A multi-input-multi-output linear model of the energy balance of solar greenhouses based on on-line identification system can simulate greenhouse behavior and allow for predictive control. The good time allocation of available solar energy can be achieved by intelligent use of controls, such as store/retrieve fans and ventilation windows, i.e. solar energy to warm up the air or to be stored in the storage elements (wall, soil, etc.) or to be exhausted to outside. The proactive energy management can select an optimal trajectory of air temperature for the forecasted weather period to minimize plants' thermal 'cost' defined by an 'expert' in terms of set-points for the specific crop. The selection of temperature trajectory is formulated as a generalized traveling salesman problem (GTSP) with precedence constraints and is solved by a genetic algorithm (GA) in this research. The simulation study showed good potential for energy saving and timely allocation to prevent excessive crop stress. The active control elements in addition to predefining and applying, within energy constraints, optimal climate in the greenhouse, it also reduces the energy deficit, i.e. the working hours of the 'heater' in the sustained freezing weather, as well as the ventilation hours, that is, more energy harvest in the warm days. This intelligent solar greenhouse management system is being migrated to the web for serving a 'customer base' in the Internet Plus era. The capacity, of the concrete ground CAUA system (CAUA is an abbreviations from both China Agricultural University and Agricultural University of Athens), to implement web 'updates' of criteria, open weather data and models, on which control actions are based, is what makes use of Cloud Data for closing the loop of an effective Internet of Things (IoT) system, based on MACQU (MAnagement and Control for QUality) technological platform.

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Modified genetic algorithm using Box Complex method: Application to optimal control problems

Cited by 19 publications

References 45 publications

Identification of physiological response functions to correct for fluctuations in resting-state fMRI related to heart rate and respiration

Identification of physiological response functions to correct for fluctuations in resting-state fMRI related to heart rate and respiration

Identification of Physiological Response Functions to Correct for Fluctuations in Resting-State fMRI related to Heart Rate and Respiration

Sustainable energy management of solar greenhouses using open weather data on MACQU platform

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