Novel hybrid of AOA-BSA with double adaptive and random spare for global optimization and engineering problems
Archimedes Optimization Algorithm (AOA) is a new physics-based optimizer that simulates Archimedes principles. AOA has been used in a variety of real-world applications because of potential properties such as a limited number of control parameters, adaptability, and changing the set of solutions to...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-07-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016823003332 |
| _version_ | 1797803981809909760 |
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| author | Fatma A. Hashim Ruba Abu Khurma Dheeb Albashish Mohamed Amin Abdelazim G. Hussien |
| author_facet | Fatma A. Hashim Ruba Abu Khurma Dheeb Albashish Mohamed Amin Abdelazim G. Hussien |
| author_sort | Fatma A. Hashim |
| collection | DOAJ |
| description | Archimedes Optimization Algorithm (AOA) is a new physics-based optimizer that simulates Archimedes principles. AOA has been used in a variety of real-world applications because of potential properties such as a limited number of control parameters, adaptability, and changing the set of solutions to prevent being trapped in local optima. Despite the wide acceptance of AOA, it has some drawbacks, such as the assumption that individuals modify their locations depending on altered densities, volumes, and accelerations. This causes various shortcomings such as stagnation into local optimal regions, low diversity of the population, weakness of exploitation phase, and slow convergence curve. Thus, the exploitation of a specific local region in the conventional AOA may be examined to achieve a balance between exploitation and exploration capabilities in the AOA. The bird Swarm Algorithm (BSA) has an efficient exploitation strategy and a strong ability of search process. In this study, a hybrid optimizer called AOA-BSA is proposed to overcome the limitations of AOA by replacing its exploitation phase with a BSA exploitation one. Moreover, a transition operator is used to have a high balance between exploration and exploitation. To test and examine the AOA-BSA performance, in the first experimental series, 29 unconstrained functions from CEC2017 have been used whereas the series of the second experiments use seven constrained engineering problems to test the AOA-BSA’s ability in handling unconstrained issues. The performance of the suggested algorithm is compared with 10 optimizers. These are the original algorithms and 8 other algorithms. The first experiment’s results show the effectiveness of the AOA-BSA in optimizing the functions of the CEC2017 test suite. AOABSA outperforms the other metaheuristic algorithms compared with it across 16 functions. The results of AOABSA are statically validated using Wilcoxon Rank sum. The AOABSA shows superior convergence capability. This is due to the added power to the AOA by the integration with BSA to balance exploration and exploitation. This is not only seen in the faster convergence achieved by the AOABSA, but also in the optimal solutions found by the search process. For further validation of the AOABSA, an extensive statistical analysis is performed during the search process by recording the ratios of the exploration and exploitation. For engineering problems, AOABSA achieves competitive results compared with other algorithms. the convergence curve of the AOABSA reaches the lowest values of the problem. It also has the minimum standard deviation which indicates the robustness of the algorithm in solving these problems. Also, it obtained competitive results compared with other counterparts algorithms regarding the values of the problem variables and convergence behavior that reaches the best minimum values. |
| first_indexed | 2024-03-13T05:30:19Z |
| format | Article |
| id | doaj.art-3ef6d47f0cc244e896243abd9f1f6cfb |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-03-13T05:30:19Z |
| publishDate | 2023-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-3ef6d47f0cc244e896243abd9f1f6cfb2023-06-15T04:54:19ZengElsevierAlexandria Engineering Journal1110-01682023-07-0173543577Novel hybrid of AOA-BSA with double adaptive and random spare for global optimization and engineering problemsFatma A. Hashim0Ruba Abu Khurma1Dheeb Albashish2Mohamed Amin3Abdelazim G. Hussien4Faculty of Engineering, Helwan University, Egypt; MEU Research Unit, Middle East University, Amman 11831, JordanComputer Science Department, Faculty of Information Technology, Al-Ahliyya University, Amman, JordanComputer Science Department, Prince Abdullah bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Salt, JordanFaculty of Science, Menoufia University, EgyptDepartment of Computer and Information Science, Linköping University, Linköping, Sweden; Faculty of Science, Fayoum University, Egypt; Corresponding author.Archimedes Optimization Algorithm (AOA) is a new physics-based optimizer that simulates Archimedes principles. AOA has been used in a variety of real-world applications because of potential properties such as a limited number of control parameters, adaptability, and changing the set of solutions to prevent being trapped in local optima. Despite the wide acceptance of AOA, it has some drawbacks, such as the assumption that individuals modify their locations depending on altered densities, volumes, and accelerations. This causes various shortcomings such as stagnation into local optimal regions, low diversity of the population, weakness of exploitation phase, and slow convergence curve. Thus, the exploitation of a specific local region in the conventional AOA may be examined to achieve a balance between exploitation and exploration capabilities in the AOA. The bird Swarm Algorithm (BSA) has an efficient exploitation strategy and a strong ability of search process. In this study, a hybrid optimizer called AOA-BSA is proposed to overcome the limitations of AOA by replacing its exploitation phase with a BSA exploitation one. Moreover, a transition operator is used to have a high balance between exploration and exploitation. To test and examine the AOA-BSA performance, in the first experimental series, 29 unconstrained functions from CEC2017 have been used whereas the series of the second experiments use seven constrained engineering problems to test the AOA-BSA’s ability in handling unconstrained issues. The performance of the suggested algorithm is compared with 10 optimizers. These are the original algorithms and 8 other algorithms. The first experiment’s results show the effectiveness of the AOA-BSA in optimizing the functions of the CEC2017 test suite. AOABSA outperforms the other metaheuristic algorithms compared with it across 16 functions. The results of AOABSA are statically validated using Wilcoxon Rank sum. The AOABSA shows superior convergence capability. This is due to the added power to the AOA by the integration with BSA to balance exploration and exploitation. This is not only seen in the faster convergence achieved by the AOABSA, but also in the optimal solutions found by the search process. For further validation of the AOABSA, an extensive statistical analysis is performed during the search process by recording the ratios of the exploration and exploitation. For engineering problems, AOABSA achieves competitive results compared with other algorithms. the convergence curve of the AOABSA reaches the lowest values of the problem. It also has the minimum standard deviation which indicates the robustness of the algorithm in solving these problems. Also, it obtained competitive results compared with other counterparts algorithms regarding the values of the problem variables and convergence behavior that reaches the best minimum values.http://www.sciencedirect.com/science/article/pii/S1110016823003332Archimedes optimization algorithmBird Swarm AlgorithmOptimizationMetaheuristicEngineering problems |
| spellingShingle | Fatma A. Hashim Ruba Abu Khurma Dheeb Albashish Mohamed Amin Abdelazim G. Hussien Novel hybrid of AOA-BSA with double adaptive and random spare for global optimization and engineering problems Alexandria Engineering Journal Archimedes optimization algorithm Bird Swarm Algorithm Optimization Metaheuristic Engineering problems |
| title | Novel hybrid of AOA-BSA with double adaptive and random spare for global optimization and engineering problems |
| title_full | Novel hybrid of AOA-BSA with double adaptive and random spare for global optimization and engineering problems |
| title_fullStr | Novel hybrid of AOA-BSA with double adaptive and random spare for global optimization and engineering problems |
| title_full_unstemmed | Novel hybrid of AOA-BSA with double adaptive and random spare for global optimization and engineering problems |
| title_short | Novel hybrid of AOA-BSA with double adaptive and random spare for global optimization and engineering problems |
| title_sort | novel hybrid of aoa bsa with double adaptive and random spare for global optimization and engineering problems |
| topic | Archimedes optimization algorithm Bird Swarm Algorithm Optimization Metaheuristic Engineering problems |
| url | http://www.sciencedirect.com/science/article/pii/S1110016823003332 |
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