Show simple item record

dc.contributor.authorQi Lin;Yao Jian;Wang Xin-yue
dc.date.accessioned2019-02-26T07:37:03Z
dc.date.available2019-02-26T07:37:03Z
dc.date.issued2018
dc.identifier.otherJHTRCQ.0000631.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4248296
dc.description.abstractIn 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.
dc.publisherAmerican Society of Civil Engineers
dc.titleImproved Particle Swarm Optimization Algorithm to Solve the Problem of Layout Optimization of Electric Vehicle Charging Stations
dc.typeJournal Paper
dc.journal.volume12
dc.journal.issue2
dc.journal.titleJournal of Highway and Transportation Research and Development (English Edition)
dc.identifier.doi10.1061/JHTRCQ.0000631
dc.page96
dc.treeJournal of Highway and Transportation Research and Development (English Edition):;2018:;Volume ( 012 ):;issue: 002
dc.contenttypeFulltext


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record