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Selecting Some Variables to Update-Based Algorithm for Solving Optimization Problems.

Mohammad Dehghani1, Pavel Trojovský1

  • 1Department of Mathematics, Faculty of Science, University of Hradec Králové, 500 03 Hradec Kralove, Czech Republic.

Sensors (Basel, Switzerland)
|March 10, 2022
PubMed
Summary
This summary is machine-generated.

A new stochastic optimization algorithm, the Selecting Some Variables to Update-Based Algorithm (SSVUBA), effectively solves complex optimization problems. SSVUBA demonstrates superior performance compared to existing methods, providing near-optimal solutions for engineering design challenges.

Keywords:
optimizationoptimization problempopulation updatingpopulation-based algorithmselected variablesstochastic methods

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Area of Science:

  • Computational Science and Engineering
  • Operations Research
  • Artificial Intelligence

Background:

  • Complex optimization problems with non-convex, nonlinear, and non-differentiable characteristics pose significant challenges.
  • Traditional optimization algorithms struggle with the discrete search spaces and multifaceted nature of modern scientific and technological problems.
  • The increasing demand for optimal solutions in diverse fields necessitates the development of advanced optimization techniques.

Purpose of the Study:

  • To introduce a novel stochastic optimization algorithm, the Selecting Some Variables to Update-Based Algorithm (SSVUBA).
  • To address the limitations of existing methods in handling complex, real-world optimization challenges.
  • To enhance the efficiency and accuracy of finding quasi-optimal solutions in optimization applications.

Main Methods:

  • Development of the Selecting Some Variables to Update-Based Algorithm (SSVUBA), a stochastic optimization approach.
  • Mathematical modeling and theoretical description of the SSVUBA algorithm.
  • Comprehensive evaluation using 53 objective functions (unimodal, multimodal, CEC 2017) and four engineering design problems.
  • Comparative analysis against eight established optimization algorithms.

Main Results:

  • The proposed SSVUBA algorithm demonstrates a significant capability in addressing a wide range of optimization issues.
  • SSVUBA consistently outperformed eight well-known competitor algorithms in simulation tests.
  • The algorithm successfully provided quasi-optimal solutions closer to the global optima for tested problems.
  • Effective performance was observed in optimizing real-world engineering design problems.

Conclusions:

  • The SSVUBA algorithm is a robust and effective tool for solving complex optimization problems.
  • SSVUBA offers a promising alternative to existing optimization methods, particularly for non-convex and non-differentiable problems.
  • The algorithm's ability to leverage population information and adapt variable updates contributes to its superior performance.