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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Updated: Aug 15, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Gradient-Based Optimizer (GBO): A Review, Theory, Variants, and Applications.

Mohammad Sh Daoud1, Mohammad Shehab2, Hani M Al-Mimi3

  • 1Al Ain University, Abu Dhabi, United Arab Emirates.

Archives of Computational Methods in Engineering : State of the Art Reviews
|January 4, 2023
PubMed
Summary

This survey reviews the gradient-based optimizer (GBO), a powerful population-based algorithm. It covers GBO variants, applications, and performance comparisons, highlighting future research directions for optimization and data mining.

Keywords:
Engineering problemsGBO’s applicationsGBO’s variantsGradient-Based OptimizerOptimization algorithms

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Data Science

Background:

  • The gradient-based optimizer (GBO) is a novel population-based algorithm demonstrating significant effectiveness across diverse applications.
  • Existing literature on GBO is fragmented, necessitating a consolidated review of its variants, applications, and comparative performance.

Purpose of the Study:

  • To provide a comprehensive survey of the gradient-based optimizer (GBO).
  • To analyze the major features, variants, applications, and comparative efficiency of GBO against other metaheuristic algorithms.
  • To identify current limitations and propose future research avenues for GBO.

Main Methods:

  • Systematic literature review of GBO-related research.
  • Categorization of existing works into GBO variants, applications, and comparative studies.
  • Analysis of GBO's performance metrics against established metaheuristic algorithms.

Main Results:

  • GBO has been successfully applied to a wide array of problems in engineering, medical, data mining, and clustering.
  • Comparative analyses indicate GBO's competitive or superior performance over many existing metaheuristic algorithms.
  • The review identifies specific GBO variants and application domains where its efficacy is particularly pronounced.

Conclusions:

  • GBO is a highly effective optimization algorithm with broad applicability.
  • Further research is needed to address GBO's current disadvantages and explore novel applications.
  • This review serves as a valuable resource for researchers and practitioners in optimization and related fields.