@article{scholars6304,
            year = {2015},
             doi = {10.1155/2015/620425},
          volume = {2015},
            note = {cited By 20},
         journal = {Mathematical Problems in Engineering},
       publisher = {Hindawi Limited},
           title = {Swarm Intelligence-Based Smart Energy Allocation Strategy for Charging Stations of Plug-In Hybrid Electric Vehicles},
        keywords = {Amphibious vehicles; Artificial intelligence; Charging (batteries); Electric power transmission networks; Energy management; Evolutionary algorithms; Hybrid vehicles; Particle swarm optimization (PSO); Renewable energy resources; Smart power grids, Allocation strategy; Gravitational search algorithm (GSA); Intelligent energy management; Nonlinear objective functions; Optimization techniques; Plug-in hybrid electric vehicles; Renewable energy source; Transportation sector, Plug-in hybrid vehicles},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84937780741&doi=10.1155\%2f2015\%2f620425&partnerID=40&md5=f161d8c6fb2ec2407020c3e589acc46d},
        abstract = {Recent researches towards the use of green technologies to reduce pollution and higher penetration of renewable energy sources in the transportation sector have been gaining popularity. In this wake, extensive participation of plug-in hybrid electric vehicles (PHEVs) requires adequate charging allocation strategy using a combination of smart grid systems and smart charging infrastructures. Daytime charging stations will be needed for daily usage of PHEVs due to the limited all-electric range. Intelligent energy management is an important issue which has already drawn much attention of researchers. Most of these works require formulation of mathematical models with extensive use of computational intelligence-based optimization techniques to solve many technical problems. In this paper, gravitational search algorithm (GSA) has been applied and compared with another member of swarm family, particle swarm optimization (PSO), considering constraints such as energy price, remaining battery capacity, and remaining charging time. Simulation results obtained for maximizing the highly nonlinear objective function evaluate the performance of both techniques in terms of best fitness. {\^A}{\copyright} 2015 Imran Rahman et al.},
            issn = {1024123X},
          author = {Rahman, I. and Vasant, P. M. and Mahinder Singh, B. S. and Abdullah-Al-Wadud, M.}
}