Multi-objective optimal path selection in electric vehicles

This work proposes a memory-efficient multiobjective optimization algorithm to perform optimal path selection (OPS) in electric vehicles (EVs). The proposed algorithm requires less computational time and executes efficiently on fast-processor-based embedded systems. It is a population-based simulated evolution algorithm that incorporates innovative functions for calculating the goodness of particles and performing the allocation operation. The goodness and allocation operations ensure the exploration of new paths and the preservation of Pareto-optimal solutions. We executed our algorithm on an Intel Celeron processor, which is also used in embedded systems and compared its performance with that of the non-dominated sorting genetic algorithm-II (NSGA-II). Our experiments used real road networks. The comparison shows that on an average, our algorithm found 5.5 % more Pareto-optimal solutions than NSGA-II. Therefore, our proposed algorithm is suitable for performing OPS in EVs.