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

Abstract

This paper introduces a comprehensive survey of a new population-based algorithm so-called gradient-based optimizer (GBO) and analyzes its major features. GBO considers as one of the most effective optimization algorithm where it was utilized in different problems and domains, successfully. This review introduces set of related works of GBO where distributed into; GBO variants, GBO applications, and evaluate the efficiency of GBO compared with other metaheuristic algorithms. Finally, the conclusions concentrate on the existing work on GBO, showing its disadvantages, and propose future works. The review paper will be helpful for the researchers and practitioners of GBO belonging to a wide range of audiences from the domains of optimization, engineering, medical, data mining and clustering. As well, it is wealthy in research on health, environment and public safety. Also, it will aid those who are interested by providing them with potential future research.

Biophysical modulation and robustness of itinerant complexity in neuronal networks

“…Nevertheless, the current work demonstrates that the emergent metastability in an interneuron population depends on their connectivity with excitatory neurons and the frequency of the excitatory drive. Their relationships are revealed in the curves that show robust gradients ( Daoud et al, 2023 ) along the dimensions excitatory-inhibitory connection probability ( Figure 4A ) and the frequency of the excitatory drive ( Figures 3 , 4B ). Thus, a numerical optimization using as an objective function can, in principle, estimate optimal intraregional connectivity configurations among groups of neurons with heterogenous frequency profiles.…”

“…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 ).…”

Metaheuristic Optimization Algorithms: an overview

“…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.…”

Symmetric projection optimizer: concise and efficient solving engineering problems using the fundamental wave of the Fourier series

“…The main feature of this class of algorithms is the use of specific mathematical methods as inspiration for building the algorithm. Some of these algorithms are more competitive, but their search mechanisms are still essentially linear combinations or weight-based combinations, such as gradient-based optimizer (GBO) 51 , generalized normal distribution optimization (GNDO) 52 , geometric mean optimizer (GMO) 53 , arithmetic optimization algorithm (AOA) 54 , subtractive averaging base optimizer (SABO) 55 . And some algorithms contribute more unique search mechanisms.…”