Electric Vehicle Ordered Charging Planning Based on Improved Dual-Population Genetic Moth–Flame Optimization
This work discusses the electric vehicle (EV) ordered charging planning (OCP) optimization problem. To address this issue, an improved dual-population genetic moth–flame optimization (IDPGMFO) is proposed. Specifically, to obtain an appreciative solution of EV OCP, the design for a dual-population g...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/17/3/110 |
| _version_ | 1797242330056491008 |
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| author | Shuang Che Yan Chen Longda Wang Chuanfang Xu |
| author_facet | Shuang Che Yan Chen Longda Wang Chuanfang Xu |
| author_sort | Shuang Che |
| collection | DOAJ |
| description | This work discusses the electric vehicle (EV) ordered charging planning (OCP) optimization problem. To address this issue, an improved dual-population genetic moth–flame optimization (IDPGMFO) is proposed. Specifically, to obtain an appreciative solution of EV OCP, the design for a dual-population genetic mechanism integrated into moth–flame optimization is provided. To enhance the global optimization performance, the adaptive nonlinear decreasing strategies with selection, crossover and mutation probability, as well as the weight coefficient, are also designed. Additionally, opposition-based learning (OBL) is also introduced simultaneously. The simulation results show that the proposed improvement strategies can effectively improve the global optimization performance. Obviously, more ideal optimization solution of the EV OCP optimization problem can be obtained by using IDPGMFO. |
| first_indexed | 2024-04-24T18:37:30Z |
| format | Article |
| id | doaj.art-37464ed4261f42568f4fded6c1ae04d1 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-04-24T18:37:30Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-37464ed4261f42568f4fded6c1ae04d12024-03-27T13:17:24ZengMDPI AGAlgorithms1999-48932024-03-0117311010.3390/a17030110Electric Vehicle Ordered Charging Planning Based on Improved Dual-Population Genetic Moth–Flame OptimizationShuang Che0Yan Chen1Longda Wang2Chuanfang Xu3School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, ChinaSchool of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, ChinaSchool of Automation and Electrical Engineering, Dalian Jiaotong University, Dalian 116028, ChinaSchool of Automation and Electrical Engineering, Dalian Jiaotong University, Dalian 116028, ChinaThis work discusses the electric vehicle (EV) ordered charging planning (OCP) optimization problem. To address this issue, an improved dual-population genetic moth–flame optimization (IDPGMFO) is proposed. Specifically, to obtain an appreciative solution of EV OCP, the design for a dual-population genetic mechanism integrated into moth–flame optimization is provided. To enhance the global optimization performance, the adaptive nonlinear decreasing strategies with selection, crossover and mutation probability, as well as the weight coefficient, are also designed. Additionally, opposition-based learning (OBL) is also introduced simultaneously. The simulation results show that the proposed improvement strategies can effectively improve the global optimization performance. Obviously, more ideal optimization solution of the EV OCP optimization problem can be obtained by using IDPGMFO.https://www.mdpi.com/1999-4893/17/3/110electric vehicleordered charging planningimproved dual-population genetic moth-flame optimizationadaptive nonlinear decreasing strategiesopposition-based learning |
| spellingShingle | Shuang Che Yan Chen Longda Wang Chuanfang Xu Electric Vehicle Ordered Charging Planning Based on Improved Dual-Population Genetic Moth–Flame Optimization Algorithms electric vehicle ordered charging planning improved dual-population genetic moth-flame optimization adaptive nonlinear decreasing strategies opposition-based learning |
| title | Electric Vehicle Ordered Charging Planning Based on Improved Dual-Population Genetic Moth–Flame Optimization |
| title_full | Electric Vehicle Ordered Charging Planning Based on Improved Dual-Population Genetic Moth–Flame Optimization |
| title_fullStr | Electric Vehicle Ordered Charging Planning Based on Improved Dual-Population Genetic Moth–Flame Optimization |
| title_full_unstemmed | Electric Vehicle Ordered Charging Planning Based on Improved Dual-Population Genetic Moth–Flame Optimization |
| title_short | Electric Vehicle Ordered Charging Planning Based on Improved Dual-Population Genetic Moth–Flame Optimization |
| title_sort | electric vehicle ordered charging planning based on improved dual population genetic moth flame optimization |
| topic | electric vehicle ordered charging planning improved dual-population genetic moth-flame optimization adaptive nonlinear decreasing strategies opposition-based learning |
| url | https://www.mdpi.com/1999-4893/17/3/110 |
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