@book{scholars5960,
           title = {Swarm intelligence-based optimization for PHEV charging stations},
       publisher = {IGI Global},
         journal = {Handbook of Research on Swarm Intelligence in Engineering},
           pages = {374--405},
            note = {cited By 20},
             doi = {10.4018/978-1-4666-8291-7.ch012},
            year = {2015},
        keywords = {Benchmarking; Charging (batteries); Evolutionary algorithms; Hybrid vehicles; Particle swarm optimization (PSO); Swarm intelligence, Battery capacity; Charging infrastructures; Computation time; Gravitational search algorithm (GSA); Key performance indicators; Nonlinear objective functions; Particle swarm optimization technique; Plug-in hybrid electric vehicles, Plug-in hybrid vehicles},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84954245094&doi=10.4018\%2f978-1-4666-8291-7.ch012&partnerID=40&md5=636afcf8ff60030352f0598ed2afba91},
        abstract = {In this chapter, Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO) technique were applied for intelligent allocation of energy to the Plug-in Hybrid Electric Vehicles (PHEVs). Considering constraints such as energy price, remaining battery capacity, and remaining charging time, they optimized the State-of-Charge (SoC), a key performance indicator in hybrid electric vehicle for the betterment of charging infrastructure. Simulation results obtained for maximizing the highly nonlinear objective function evaluates the performance of both techniques in terms of global best fitness and computation time. {\^A}{\copyright} 2015, IGI Global. All rights reserved.},
          author = {Rahman, I. and Vasant, P. and Singh, B. S. M. and Abdullah-Al-Wadud, M.},
            isbn = {9781466682924; 1466682914; 9781466682917}
}