Gradient-Based Optimizer (GBO): A Review, Theory, Variants, and Applications

Daoud, Mohammad Sh.; Shehab, Mohammad; Al-Mimi, Hani M; Abualigah, Laith; Zitar, Raed Abu; Shambour, Mohd Khaled Yousef

doi:10.1007/s11831-022-09872-y

Cited by 37 publications

(8 citation statements)

References 132 publications

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“…Our results suggest that the interneuron population’s emergent attractors depend primarily on the E-I connectivity and the frequency of the driving excitatory oscillations. There are robust gradients ( Daoud et al, 2023 ) towards maximally stable interneuron phase cluster arrangements along the dimensions of pyramidal frequency and E-I connection probability ( Figures 3 , 4 ).…”

Section: Resultsmentioning

confidence: 99%

Biophysical modulation and robustness of itinerant complexity in neuronal networks

Venkadesh,

Shaikh,

Shakeri

et al. 2024

Front. Netw. Physiol.

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Transient synchronization of bursting activity in neuronal networks, which occurs in patterns of metastable itinerant phase relationships between neurons, is a notable feature of network dynamics observed in vivo. However, the mechanisms that contribute to this dynamical complexity in neuronal circuits are not well understood. Local circuits in cortical regions consist of populations of neurons with diverse intrinsic oscillatory features. In this study, we numerically show that the phenomenon of transient synchronization, also referred to as metastability, can emerge in an inhibitory neuronal population when the neurons’ intrinsic fast-spiking dynamics are appropriately modulated by slower inputs from an excitatory neuronal population. Using a compact model of a mesoscopic-scale network consisting of excitatory pyramidal and inhibitory fast-spiking neurons, our work demonstrates a relationship between the frequency of pyramidal population oscillations and the features of emergent metastability in the inhibitory population. In addition, we introduce a method to characterize collective transitions in metastable networks. Finally, we discuss potential applications of this study in mechanistically understanding cortical network dynamics.

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Section: Resultsmentioning

confidence: 99%

Biophysical modulation and robustness of itinerant complexity in neuronal networks

Venkadesh,

Shaikh,

Shakeri

et al. 2024

Front. Netw. Physiol.

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“…Gradient-Based Optimization methods present several merits in optimization. They exhibit notable advantages, particularly their rapid convergence, especially when dealing with smooth and convex objective functions (Daoud et al, 2023). Moreover, their suitability for high-dimensional problems, coupled with their amenable parallelization, renders them highly efficient in resource-rich computational settings.…”

Section: Gradient-based Algorithms Vs Metaheuristic Algorithms In Opt...mentioning

confidence: 99%

Metaheuristic Optimization Algorithms: an overview

Dr. Benaissa Brahim,

Kobayashi,

Al Ali

et al. 2024

HCMCOUJS - Advances in Computational Structures

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Metaheuristic optimization algorithms are known for their versatility and adaptability, making them effective tools for solving a wide range of complex optimization problems. They don't rely on specific problem types, gradients, and can explore globally while handling multi-objective optimization. They strike a balance between exploration and exploitation, contributing to advancements in optimization. However, it's important to note their limitations, including the lack of a guaranteed global optimum, varying convergence rates, and their somewhat opaque functioning. In contrast, metaphor-based optimization algorithms, while intuitively appealing, have faced controversy due to potential oversimplification and unrealistic expectations. Despite these considerations, metaheuristic algorithms continue to be widely used for tackling complex problems. This research paper aims to explore the fundamental components and concepts that underlie optimization algorithms, focusing on the use of search references and the delicate balance between exploration and exploitation. Visual representations of the search behavior of selected metaheuristic algorithms will also be provided.

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“…It was effectively employed to multiple engineering problems, such as static var compensator operation in power systems [41], feature selection [42], [43], parameter identification of photovoltaic models [44], human activity recognition using smartphones [45], proton exchange membrane fuel cell parameter estimation [46], structural optimization [47] and economic dispatch [48]. Despite the GBO methods' age of roughly two years, the researchers developed multiple modifications (i.e., versions) that enabled it to become compatible for tackling various types of issues [49]. Despite the fact that GBO has demonstrated its ability to successfully tackle a variety of challenges.…”

Section: A) Problem Statementmentioning

confidence: 99%

Modified Gradient-Based Algorithm for Distributed Generation and Capacitors Integration in Radial Distribution Networks

El-Rifaie,

Shaheen,

Tolba

et al. 2023

IEEE Access

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With the use of cutting-edge demand management at the system or home level and powerful network reconfiguration tools, smart grids are expected to introduce advanced hardware and software resources to strengthen the operation of power systems. This article describes a Modified Gradient-Based Optimization (MGBO) algorithm for Distributed Generation (DG) and capacitors integration in distribution feeders. To increase the variety of the produced searching individuals, the suggested MGBO combines the basic Gradient Searching Method (GSM) and Local Escape Mechanism (LEM) with a binomial crossover strategy. This combined cross-over strategy upgrades the forthcoming searching individuals to be more random. The LEM assists in evading local optima, whereas the GSM guides the searching scan to promising regions and facilitates its convergence to the optimum solution. The suggested MGBO method is designed and implemented to improve the performance of radial distribution networks by reducing technical power losses while taking into account the peak loading. Its relevance is tested on a practical radial 59-bus Cairo distribution feeder in Egypt and a large-scale radial 135-bus distribution feeder. The proposed MGBO is compared with the original GBO, Manta ray foraging optimization (MRFO) and honey badger algorithm (HBA). The whole comparison of the suggested MGBO with the original GBO and the newly developed optimization algorithms demonstrates that the suggested MGBO derives the best performance in all of the cases studied. For the practical radial 59-bus Cairo distribution feeder in Egypt, the proposed MGBO shows great improvement of 18.40%, 20.17%, and 2.29% in robustness indicator of the standard deviation compared with GBO, MRFO, and HBA, respectively. For the large-scale radial 135-bus distribution feeder, the proposed MGBO shows great improvement of 46.92%, 62.94%, and 67.87% in robustness indicator of the standard deviation compared with GBO, MRFO, and HBA, respectively.INDEX TERMS Gradient-based optimizer; distributed generation; capacitors; distribution systems; power losses minimization.

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Gradient-Based Optimizer (GBO): A Review, Theory, Variants, and Applications

Cited by 37 publications

References 132 publications

Biophysical modulation and robustness of itinerant complexity in neuronal networks

Biophysical modulation and robustness of itinerant complexity in neuronal networks

Metaheuristic Optimization Algorithms: an overview

Modified Gradient-Based Algorithm for Distributed Generation and Capacitors Integration in Radial Distribution Networks

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