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

With the advancement of science and technology, new complex optimization problems have emerged, and the achievement of optimal solutions has become increasingly important. Many of these problems have features and difficulties such as non-convex, nonlinear, discrete search space, and a non-differenti...

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Autores principales: Dehghani, Mohammad, Trojovský, Pavel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8914702/
https://www.ncbi.nlm.nih.gov/pubmed/35270941
http://dx.doi.org/10.3390/s22051795
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author Dehghani, Mohammad
Trojovský, Pavel
author_facet Dehghani, Mohammad
Trojovský, Pavel
author_sort Dehghani, Mohammad
collection PubMed
description With the advancement of science and technology, new complex optimization problems have emerged, and the achievement of optimal solutions has become increasingly important. Many of these problems have features and difficulties such as non-convex, nonlinear, discrete search space, and a non-differentiable objective function. Achieving the optimal solution to such problems has become a major challenge. To address this challenge and provide a solution to deal with the complexities and difficulties of optimization applications, a new stochastic-based optimization algorithm is proposed in this study. Optimization algorithms are a type of stochastic approach for addressing optimization issues that use random scanning of the search space to produce quasi-optimal answers. The Selecting Some Variables to Update-Based Algorithm (SSVUBA) is a new optimization algorithm developed in this study to handle optimization issues in various fields. The suggested algorithm’s key principles are to make better use of the information provided by different members of the population and to adjust the number of variables used to update the algorithm population during the iterations of the algorithm. The theory of the proposed SSVUBA is described, and then its mathematical model is offered for use in solving optimization issues. Fifty-three objective functions, including unimodal, multimodal, and CEC 2017 test functions, are utilized to assess the ability and usefulness of the proposed SSVUBA in addressing optimization issues. SSVUBA’s performance in optimizing real-world applications is evaluated on four engineering design issues. Furthermore, the performance of SSVUBA in optimization was compared to the performance of eight well-known algorithms to further evaluate its quality. The simulation results reveal that the proposed SSVUBA has a significant ability to handle various optimization issues and that it outperforms other competitor algorithms by giving appropriate quasi-optimal solutions that are closer to the global optima.
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spelling pubmed-89147022022-03-12 Selecting Some Variables to Update-Based Algorithm for Solving Optimization Problems Dehghani, Mohammad Trojovský, Pavel Sensors (Basel) Article With the advancement of science and technology, new complex optimization problems have emerged, and the achievement of optimal solutions has become increasingly important. Many of these problems have features and difficulties such as non-convex, nonlinear, discrete search space, and a non-differentiable objective function. Achieving the optimal solution to such problems has become a major challenge. To address this challenge and provide a solution to deal with the complexities and difficulties of optimization applications, a new stochastic-based optimization algorithm is proposed in this study. Optimization algorithms are a type of stochastic approach for addressing optimization issues that use random scanning of the search space to produce quasi-optimal answers. The Selecting Some Variables to Update-Based Algorithm (SSVUBA) is a new optimization algorithm developed in this study to handle optimization issues in various fields. The suggested algorithm’s key principles are to make better use of the information provided by different members of the population and to adjust the number of variables used to update the algorithm population during the iterations of the algorithm. The theory of the proposed SSVUBA is described, and then its mathematical model is offered for use in solving optimization issues. Fifty-three objective functions, including unimodal, multimodal, and CEC 2017 test functions, are utilized to assess the ability and usefulness of the proposed SSVUBA in addressing optimization issues. SSVUBA’s performance in optimizing real-world applications is evaluated on four engineering design issues. Furthermore, the performance of SSVUBA in optimization was compared to the performance of eight well-known algorithms to further evaluate its quality. The simulation results reveal that the proposed SSVUBA has a significant ability to handle various optimization issues and that it outperforms other competitor algorithms by giving appropriate quasi-optimal solutions that are closer to the global optima. MDPI 2022-02-24 /pmc/articles/PMC8914702/ /pubmed/35270941 http://dx.doi.org/10.3390/s22051795 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Dehghani, Mohammad
Trojovský, Pavel
Selecting Some Variables to Update-Based Algorithm for Solving Optimization Problems
title Selecting Some Variables to Update-Based Algorithm for Solving Optimization Problems
title_full Selecting Some Variables to Update-Based Algorithm for Solving Optimization Problems
title_fullStr Selecting Some Variables to Update-Based Algorithm for Solving Optimization Problems
title_full_unstemmed Selecting Some Variables to Update-Based Algorithm for Solving Optimization Problems
title_short Selecting Some Variables to Update-Based Algorithm for Solving Optimization Problems
title_sort selecting some variables to update-based algorithm for solving optimization problems
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8914702/
https://www.ncbi.nlm.nih.gov/pubmed/35270941
http://dx.doi.org/10.3390/s22051795
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