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OOBO: A New Metaheuristic Algorithm for Solving Optimization Problems

This study proposes the One-to-One-Based Optimizer (OOBO), a new optimization technique for solving optimization problems in various scientific areas. The key idea in designing the suggested OOBO is to effectively use the knowledge of all members in the process of updating the algorithm population w...

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Detalles Bibliográficos
Autores principales: Dehghani, Mohammad, Trojovská, Eva, Trojovský, Pavel, Malik, Om Parkash
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10604662/
https://www.ncbi.nlm.nih.gov/pubmed/37887599
http://dx.doi.org/10.3390/biomimetics8060468
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author Dehghani, Mohammad
Trojovská, Eva
Trojovský, Pavel
Malik, Om Parkash
author_facet Dehghani, Mohammad
Trojovská, Eva
Trojovský, Pavel
Malik, Om Parkash
author_sort Dehghani, Mohammad
collection PubMed
description This study proposes the One-to-One-Based Optimizer (OOBO), a new optimization technique for solving optimization problems in various scientific areas. The key idea in designing the suggested OOBO is to effectively use the knowledge of all members in the process of updating the algorithm population while preventing the algorithm from relying on specific members of the population. We use a one-to-one correspondence between the two sets of population members and the members selected as guides to increase the involvement of all population members in the update process. Each population member is chosen just once as a guide and is only utilized to update another member of the population in this one-to-one interaction. The proposed OOBO’s performance in optimization is evaluated with fifty-two objective functions, encompassing unimodal, high-dimensional multimodal, and fixed-dimensional multimodal types, and the CEC 2017 test suite. The optimization results highlight the remarkable capacity of OOBO to strike a balance between exploration and exploitation within the problem-solving space during the search process. The quality of the optimization results achieved using the proposed OOBO is evaluated by comparing them to eight well-known algorithms. The simulation findings show that OOBO outperforms the other algorithms in addressing optimization problems and can give more acceptable quasi-optimal solutions. Also, the implementation of OOBO in six engineering problems shows the effectiveness of the proposed approach in solving real-world optimization applications.
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spelling pubmed-106046622023-10-28 OOBO: A New Metaheuristic Algorithm for Solving Optimization Problems Dehghani, Mohammad Trojovská, Eva Trojovský, Pavel Malik, Om Parkash Biomimetics (Basel) Article This study proposes the One-to-One-Based Optimizer (OOBO), a new optimization technique for solving optimization problems in various scientific areas. The key idea in designing the suggested OOBO is to effectively use the knowledge of all members in the process of updating the algorithm population while preventing the algorithm from relying on specific members of the population. We use a one-to-one correspondence between the two sets of population members and the members selected as guides to increase the involvement of all population members in the update process. Each population member is chosen just once as a guide and is only utilized to update another member of the population in this one-to-one interaction. The proposed OOBO’s performance in optimization is evaluated with fifty-two objective functions, encompassing unimodal, high-dimensional multimodal, and fixed-dimensional multimodal types, and the CEC 2017 test suite. The optimization results highlight the remarkable capacity of OOBO to strike a balance between exploration and exploitation within the problem-solving space during the search process. The quality of the optimization results achieved using the proposed OOBO is evaluated by comparing them to eight well-known algorithms. The simulation findings show that OOBO outperforms the other algorithms in addressing optimization problems and can give more acceptable quasi-optimal solutions. Also, the implementation of OOBO in six engineering problems shows the effectiveness of the proposed approach in solving real-world optimization applications. MDPI 2023-10-01 /pmc/articles/PMC10604662/ /pubmed/37887599 http://dx.doi.org/10.3390/biomimetics8060468 Text en © 2023 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
Trojovská, Eva
Trojovský, Pavel
Malik, Om Parkash
OOBO: A New Metaheuristic Algorithm for Solving Optimization Problems
title OOBO: A New Metaheuristic Algorithm for Solving Optimization Problems
title_full OOBO: A New Metaheuristic Algorithm for Solving Optimization Problems
title_fullStr OOBO: A New Metaheuristic Algorithm for Solving Optimization Problems
title_full_unstemmed OOBO: A New Metaheuristic Algorithm for Solving Optimization Problems
title_short OOBO: A New Metaheuristic Algorithm for Solving Optimization Problems
title_sort oobo: a new metaheuristic algorithm for solving optimization problems
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10604662/
https://www.ncbi.nlm.nih.gov/pubmed/37887599
http://dx.doi.org/10.3390/biomimetics8060468
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