Iterative Computer Algorithms with Applications in Engineering: Solving Combinatorial Optimization Problems
Computers and Intractability; A Guide to the Theory of NP-Completeness
Computers and Intractability; A Guide to the Theory of NP-Completeness
International Journal of Applied Mathematics and Computer Science
A non-dominated sorting particle swarm optimizer for multiobjective optimization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Efficient Routing on Large Road Networks Using Hierarchical Communities
IEEE Transactions on Intelligent Transportation Systems
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
A genetic algorithm for shortest path routing problem and the sizing of populations
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
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This work proposes a memory-efficient multi-objective 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.