Ordered Charging Planning for Electric Vehicle Clusters in Tourist Attractions Based on Improved Moth-Flame Optimization Integrated With Genetic Mechanism
This is an optimization problem of ordered charging planning (OCP) for electric vehicle clusters (EVCs) in tourist attractions, and it is an important and difficult problem. In this work, for solving this optimization problem, an improved moth-flame optimization integrated with genetic mechanism (IM...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10453592/ |
| _version_ | 1797243270734020608 |
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| author | Shuang Che Yan Chen Longda Wang Chuanfang Xu Gang Liu |
| author_facet | Shuang Che Yan Chen Longda Wang Chuanfang Xu Gang Liu |
| author_sort | Shuang Che |
| collection | DOAJ |
| description | This is an optimization problem of ordered charging planning (OCP) for electric vehicle clusters (EVCs) in tourist attractions, and it is an important and difficult problem. In this work, for solving this optimization problem, an improved moth-flame optimization integrated with genetic mechanism (IMFO-G) is proposed. Specifically, based on the moth-flame optimization integrated with genetic mechanism, the adaptive nonlinear decreasing strategies with selection, crossover and mutation probability as well as weight coefficient are designed, and the opposition-based learning is also introduced simultaneously. To verify the effectiveness of proposed IMFO-G, simulation verification of an example of the OCP optimization problem of EVCs in tourist attractions is provided. The simulation results show that the proposed improvement strategies can effectively improve the global optimization performance for IMFO-G, and a more ideal optimization solution of the OCP optimization problem for EVCs in tourist can be obtained. |
| first_indexed | 2024-04-24T18:52:27Z |
| format | Article |
| id | doaj.art-1c14e6f436304a7a983c79ba1a764844 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T18:52:27Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-1c14e6f436304a7a983c79ba1a7648442024-03-26T17:47:32ZengIEEEIEEE Access2169-35362024-01-0112347083471910.1109/ACCESS.2024.337188910453592Ordered Charging Planning for Electric Vehicle Clusters in Tourist Attractions Based on Improved Moth-Flame Optimization Integrated With Genetic MechanismShuang Che0https://orcid.org/0000-0003-3253-5395Yan Chen1Longda Wang2https://orcid.org/0000-0002-7226-5900Chuanfang Xu3https://orcid.org/0000-0002-9900-8146Gang Liu4School of Maritime Economics and Management, Dalian Maritime University, Dalian, ChinaSchool of Maritime Economics and Management, Dalian Maritime University, Dalian, ChinaSchool of Automation and Electrical Engineering, Dalian Jiaotong University, Dalian, ChinaSchool of Automation and Electrical Engineering, Dalian Jiaotong University, Dalian, ChinaCollege of Engineering, Inner Mongolia Minzu University, Tongliao, ChinaThis is an optimization problem of ordered charging planning (OCP) for electric vehicle clusters (EVCs) in tourist attractions, and it is an important and difficult problem. In this work, for solving this optimization problem, an improved moth-flame optimization integrated with genetic mechanism (IMFO-G) is proposed. Specifically, based on the moth-flame optimization integrated with genetic mechanism, the adaptive nonlinear decreasing strategies with selection, crossover and mutation probability as well as weight coefficient are designed, and the opposition-based learning is also introduced simultaneously. To verify the effectiveness of proposed IMFO-G, simulation verification of an example of the OCP optimization problem of EVCs in tourist attractions is provided. The simulation results show that the proposed improvement strategies can effectively improve the global optimization performance for IMFO-G, and a more ideal optimization solution of the OCP optimization problem for EVCs in tourist can be obtained.https://ieeexplore.ieee.org/document/10453592/Ordered charging planningelectric vehicle clustersimproved moth-flame optimizationadaptive nonlinear decreasing strategyopposition-based learning |
| spellingShingle | Shuang Che Yan Chen Longda Wang Chuanfang Xu Gang Liu Ordered Charging Planning for Electric Vehicle Clusters in Tourist Attractions Based on Improved Moth-Flame Optimization Integrated With Genetic Mechanism IEEE Access Ordered charging planning electric vehicle clusters improved moth-flame optimization adaptive nonlinear decreasing strategy opposition-based learning |
| title | Ordered Charging Planning for Electric Vehicle Clusters in Tourist Attractions Based on Improved Moth-Flame Optimization Integrated With Genetic Mechanism |
| title_full | Ordered Charging Planning for Electric Vehicle Clusters in Tourist Attractions Based on Improved Moth-Flame Optimization Integrated With Genetic Mechanism |
| title_fullStr | Ordered Charging Planning for Electric Vehicle Clusters in Tourist Attractions Based on Improved Moth-Flame Optimization Integrated With Genetic Mechanism |
| title_full_unstemmed | Ordered Charging Planning for Electric Vehicle Clusters in Tourist Attractions Based on Improved Moth-Flame Optimization Integrated With Genetic Mechanism |
| title_short | Ordered Charging Planning for Electric Vehicle Clusters in Tourist Attractions Based on Improved Moth-Flame Optimization Integrated With Genetic Mechanism |
| title_sort | ordered charging planning for electric vehicle clusters in tourist attractions based on improved moth flame optimization integrated with genetic mechanism |
| topic | Ordered charging planning electric vehicle clusters improved moth-flame optimization adaptive nonlinear decreasing strategy opposition-based learning |
| url | https://ieeexplore.ieee.org/document/10453592/ |
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