| contributor author | Qi Lin;Yao Jian;Wang Xin-yue | |
| date accessioned | 2019-02-26T07:37:03Z | |
| date available | 2019-02-26T07:37:03Z | |
| date issued | 2018 | |
| identifier other | JHTRCQ.0000631.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4248296 | |
| description abstract | In charging station services, user charging requirements limit the layout optimization of charging stations to realize total cost minimization. By combining the k-center algorithm and cloud model particle swarm algorithm, this study puts forward a method to improve the global search ability of the adaptive parameter adjustment strategy. A simulation is performed accordingly. Results show that when solving the layout optimization problem for charging stations, the improved adaptive hybrid algorithm that combines the k-center and cloud model particle swarm algorithm outperforms the original cloud model particle swarm algorithm and the basic particle swarm optimization algorithm. The improved algorithm is thus effective. | |
| publisher | American Society of Civil Engineers | |
| title | Improved Particle Swarm Optimization Algorithm to Solve the Problem of Layout Optimization of Electric Vehicle Charging Stations | |
| type | Journal Paper | |
| journal volume | 12 | |
| journal issue | 2 | |
| journal title | Journal of Highway and Transportation Research and Development (English Edition) | |
| identifier doi | 10.1061/JHTRCQ.0000631 | |
| page | 96 | |
| tree | Journal of Highway and Transportation Research and Development (English Edition):;2018:;Volume ( 012 ):;issue: 002 | |
| contenttype | Fulltext | |