Football team training algorithm: A novel sport-inspired meta-heuristic optimization algorithm for global optimization
A more efficient optimization algorithm has always been the pursuit of researchers, but the performance of the current optimization algorithm in some complex test functions is not always satisfactory. In order to solve this problem, a new meta-heuristic optimization algorithm—Football Team Training...
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| Published in: | Expert systems with applications Vol. 245; p. 123088 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
01.07.2024
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| ISSN: | 0957-4174, 1873-6793 |
| Online Access: | Get full text |
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| Abstract | A more efficient optimization algorithm has always been the pursuit of researchers, but the performance of the current optimization algorithm in some complex test functions is not always satisfactory. In order to solve this problem, a new meta-heuristic optimization algorithm—Football Team Training Algorithm (FTTA) is proposed according to the training method of the football team, which simulates the three stages of the training session: Collective Training, Group Training and Individual Extra Training. By the test on two groups of test functions, CEC2005 and CEC2020, the proposed optimization algorithm (FTTA) achieves the best results, which far exceeds the traditional Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA) algorithms and so on. In the engineering application, a new hybrid wind speed prediction system is proposed based on FTTA. The FTTA is used to optimize variational mode decomposition (VMD) to improve the effect of data denoising. At the same time, based on unconstrained weighting algorithm, FTTA and combination prediction model build a new hybrid prediction strategy. Through the experiments on four groups of wind speed data in Dalian, the accuracy, stability, advancement, and CPU running speed of the system are verified. It is obvious that the practical application ability of the system is much better than previous methods, which can effectively improve the utilization efficiency of renewable energy. |
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| AbstractList | A more efficient optimization algorithm has always been the pursuit of researchers, but the performance of the current optimization algorithm in some complex test functions is not always satisfactory. In order to solve this problem, a new meta-heuristic optimization algorithm—Football Team Training Algorithm (FTTA) is proposed according to the training method of the football team, which simulates the three stages of the training session: Collective Training, Group Training and Individual Extra Training. By the test on two groups of test functions, CEC2005 and CEC2020, the proposed optimization algorithm (FTTA) achieves the best results, which far exceeds the traditional Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA) algorithms and so on. In the engineering application, a new hybrid wind speed prediction system is proposed based on FTTA. The FTTA is used to optimize variational mode decomposition (VMD) to improve the effect of data denoising. At the same time, based on unconstrained weighting algorithm, FTTA and combination prediction model build a new hybrid prediction strategy. Through the experiments on four groups of wind speed data in Dalian, the accuracy, stability, advancement, and CPU running speed of the system are verified. It is obvious that the practical application ability of the system is much better than previous methods, which can effectively improve the utilization efficiency of renewable energy. |
| ArticleNumber | 123088 |
| Author | Tian, Zhirui Gai, Mei |
| Author_xml | – sequence: 1 givenname: Zhirui orcidid: 0000-0001-7680-6770 surname: Tian fullname: Tian, Zhirui email: zhiruitian@link.cuhk.edu.cn organization: School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen), Shenzhen 518172, Guangdong, China – sequence: 2 givenname: Mei orcidid: 0000-0003-3500-5760 surname: Gai fullname: Gai, Mei email: gaimei71@lnnu.edu.cn organization: Key Research Base of Humanities and Social Sciences of the Ministry of Education, Center for Studies of Marine Economy and Sustainable Development, Liaoning Normal University, Dalian 116029, Liaoning, China |
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| Keywords | Data preprocessing strategy Football team training algorithm Wind speed prediction Neural network Unconstrained weighting method |
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