TY - GEN
T1 - Multi-constrained route optimization for Electric Vehicles (EVs) using Particle Swarm Optimization (PSO)
AU - Siddiqi, Umair Farooq
AU - Shiraishi, Yoichi
AU - Sait, Sadiq M.
PY - 2011
Y1 - 2011
N2 - Route optimization (RO) is an important feature of the Electric Vehicles (EVs) which is responsible for finding optimized paths between any source and destination nodes in the road network. In this paper, the RO problem of EVs is solved by using the Multi Constrained Optimal Path (MCOP) approach. The proposed MCOP problem aims to minimize the length of the path and meets constraints on total travelling time, total time delay due to signals, total recharging time, and total recharging cost. The Penalty Function method is used to transform the MCOP problem into unconstrained optimization problem. The unconstrained optimization is performed by using a Particle Swarm Optimization (PSO) based algorithm. The proposed algorithm has innovative methods for finding the velocity of the particles and updating their positions. The performance of the proposed algorithm is compared with two previous heuristics: H-MCOP and Genetic Algorithm (GA). The time of optimization is varied between 1 second (s) and 5s. The proposed algorithm has obtained the minimum value of the objective function in at-least 9.375% more test instances than the GA and H-MCOP.
AB - Route optimization (RO) is an important feature of the Electric Vehicles (EVs) which is responsible for finding optimized paths between any source and destination nodes in the road network. In this paper, the RO problem of EVs is solved by using the Multi Constrained Optimal Path (MCOP) approach. The proposed MCOP problem aims to minimize the length of the path and meets constraints on total travelling time, total time delay due to signals, total recharging time, and total recharging cost. The Penalty Function method is used to transform the MCOP problem into unconstrained optimization problem. The unconstrained optimization is performed by using a Particle Swarm Optimization (PSO) based algorithm. The proposed algorithm has innovative methods for finding the velocity of the particles and updating their positions. The performance of the proposed algorithm is compared with two previous heuristics: H-MCOP and Genetic Algorithm (GA). The time of optimization is varied between 1 second (s) and 5s. The proposed algorithm has obtained the minimum value of the objective function in at-least 9.375% more test instances than the GA and H-MCOP.
KW - Electric Vehicles (EVs)
KW - Multi Constrained Optimal Path
KW - Route Optimization
KW - Simulated Evolution (SimE)
UR - http://www.scopus.com/inward/record.url?scp=84857606986&partnerID=8YFLogxK
U2 - 10.1109/ISDA.2011.6121687
DO - 10.1109/ISDA.2011.6121687
M3 - Conference contribution
AN - SCOPUS:84857606986
SN - 9781457716751
T3 - International Conference on Intelligent Systems Design and Applications, ISDA
SP - 391
EP - 396
BT - Proceedings of the 2011 11th International Conference on Intelligent Systems Design and Applications, ISDA'11
ER -
