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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/8/6/468 |
| _version_ | 1797574594880602112 |
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| author | Mohammad Dehghani Eva Trojovská Pavel Trojovský Om Parkash Malik |
| author_facet | Mohammad Dehghani Eva Trojovská Pavel Trojovský Om Parkash Malik |
| author_sort | Mohammad Dehghani |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T21:24:41Z |
| format | Article |
| id | doaj.art-9f7e598deee4440094e94cb5ed07d644 |
| institution | Directory Open Access Journal |
| issn | 2313-7673 |
| language | English |
| last_indexed | 2024-03-10T21:24:41Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj.art-9f7e598deee4440094e94cb5ed07d6442023-11-19T15:48:34ZengMDPI AGBiomimetics2313-76732023-10-018646810.3390/biomimetics8060468OOBO: A New Metaheuristic Algorithm for Solving Optimization ProblemsMohammad Dehghani0Eva Trojovská1Pavel Trojovský2Om Parkash Malik3Department of Mathematics, Faculty of Science, University of Hradec Králové, 50003 Hradec Králové, Czech RepublicDepartment of Mathematics, Faculty of Science, University of Hradec Králové, 50003 Hradec Králové, Czech RepublicDepartment of Mathematics, Faculty of Science, University of Hradec Králové, 50003 Hradec Králové, Czech RepublicDepartment of Electrical and Software Engineering, University of Calgary, Calgary, AB T2N 1N4, CanadaThis 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.https://www.mdpi.com/2313-7673/8/6/468metaheuristic algorithmone-to-one correspondenceexplorationexploitationsensorsengineering |
| spellingShingle | Mohammad Dehghani Eva Trojovská Pavel Trojovský Om Parkash Malik OOBO: A New Metaheuristic Algorithm for Solving Optimization Problems Biomimetics metaheuristic algorithm one-to-one correspondence exploration exploitation sensors engineering |
| 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 | metaheuristic algorithm one-to-one correspondence exploration exploitation sensors engineering |
| url | https://www.mdpi.com/2313-7673/8/6/468 |
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