A Mixed-Strategy-Based Whale Optimization Algorithm for Parameter Identification of Hydraulic Turbine Governing Systems with a Delayed Water Hammer Effect
For solving the parameter optimization problem of a hydraulic turbine governing system (HTGS) with a delayed water hammer (DWH) effect, a Mixed-Strategy-based Whale Optimization Algorithm (MSWOA) is proposed in this paper, in which three improved strategies are designed and integrated to promote the...
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MDPI AG
2018-09-01
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| Series: | Energies |
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| Online Access: | http://www.mdpi.com/1996-1073/11/9/2367 |
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| author | Tan Ding Li Chang Chaoshun Li Chen Feng Nan Zhang |
| author_facet | Tan Ding Li Chang Chaoshun Li Chen Feng Nan Zhang |
| author_sort | Tan Ding |
| collection | DOAJ |
| description | For solving the parameter optimization problem of a hydraulic turbine governing system (HTGS) with a delayed water hammer (DWH) effect, a Mixed-Strategy-based Whale Optimization Algorithm (MSWOA) is proposed in this paper, in which three improved strategies are designed and integrated to promote the optimization ability. Firstly, the movement strategies of WOA have been improved to balance the exploration and exploitation. In the improved movement strategies, a dynamic ratio based on improved JAYA algorithm is applied on the strategy of searching for prey and a chaotic dynamic weight is designed for improving the strategies of bubble-net attacking and encircling prey. Secondly, a guidance of the elite’s memory inspired by Particle swarm optimization (PSO) is proposed to lead the movement of the population to accelerate the convergence speed. Thirdly, the mutation strategy based on the sinusoidal chaotic map is employed to avoid prematurity and local optimum points. The proposed MSWOA are compared with six popular meta-heuristic optimization algorithms on 23 benchmark functions in numerical experiments and the results show that the MSWOA has achieved significantly better performance than others. Finally, the MSWOA is applied on parameter identification problem of HTGS with a DWH effect, and the comparative results confirm the effectiveness and identification accuracy of the proposed method. |
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| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T11:07:45Z |
| publishDate | 2018-09-01 |
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| series | Energies |
| spelling | doaj.art-b0618ea00aa1496a8deb5c6774ecf5062022-12-22T04:28:14ZengMDPI AGEnergies1996-10732018-09-01119236710.3390/en11092367en11092367A Mixed-Strategy-Based Whale Optimization Algorithm for Parameter Identification of Hydraulic Turbine Governing Systems with a Delayed Water Hammer EffectTan Ding0Li Chang1Chaoshun Li2Chen Feng3Nan Zhang4School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaFor solving the parameter optimization problem of a hydraulic turbine governing system (HTGS) with a delayed water hammer (DWH) effect, a Mixed-Strategy-based Whale Optimization Algorithm (MSWOA) is proposed in this paper, in which three improved strategies are designed and integrated to promote the optimization ability. Firstly, the movement strategies of WOA have been improved to balance the exploration and exploitation. In the improved movement strategies, a dynamic ratio based on improved JAYA algorithm is applied on the strategy of searching for prey and a chaotic dynamic weight is designed for improving the strategies of bubble-net attacking and encircling prey. Secondly, a guidance of the elite’s memory inspired by Particle swarm optimization (PSO) is proposed to lead the movement of the population to accelerate the convergence speed. Thirdly, the mutation strategy based on the sinusoidal chaotic map is employed to avoid prematurity and local optimum points. The proposed MSWOA are compared with six popular meta-heuristic optimization algorithms on 23 benchmark functions in numerical experiments and the results show that the MSWOA has achieved significantly better performance than others. Finally, the MSWOA is applied on parameter identification problem of HTGS with a DWH effect, and the comparative results confirm the effectiveness and identification accuracy of the proposed method.http://www.mdpi.com/1996-1073/11/9/2367whale optimization algorithmmeta-heuristicimproved JAYA algorithmchaotic mutationHTGSparameter identification |
| spellingShingle | Tan Ding Li Chang Chaoshun Li Chen Feng Nan Zhang A Mixed-Strategy-Based Whale Optimization Algorithm for Parameter Identification of Hydraulic Turbine Governing Systems with a Delayed Water Hammer Effect Energies whale optimization algorithm meta-heuristic improved JAYA algorithm chaotic mutation HTGS parameter identification |
| title | A Mixed-Strategy-Based Whale Optimization Algorithm for Parameter Identification of Hydraulic Turbine Governing Systems with a Delayed Water Hammer Effect |
| title_full | A Mixed-Strategy-Based Whale Optimization Algorithm for Parameter Identification of Hydraulic Turbine Governing Systems with a Delayed Water Hammer Effect |
| title_fullStr | A Mixed-Strategy-Based Whale Optimization Algorithm for Parameter Identification of Hydraulic Turbine Governing Systems with a Delayed Water Hammer Effect |
| title_full_unstemmed | A Mixed-Strategy-Based Whale Optimization Algorithm for Parameter Identification of Hydraulic Turbine Governing Systems with a Delayed Water Hammer Effect |
| title_short | A Mixed-Strategy-Based Whale Optimization Algorithm for Parameter Identification of Hydraulic Turbine Governing Systems with a Delayed Water Hammer Effect |
| title_sort | mixed strategy based whale optimization algorithm for parameter identification of hydraulic turbine governing systems with a delayed water hammer effect |
| topic | whale optimization algorithm meta-heuristic improved JAYA algorithm chaotic mutation HTGS parameter identification |
| url | http://www.mdpi.com/1996-1073/11/9/2367 |
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