Distributed intelligent railway traffic control based on fuzzy decisionmaking
Fuzzy Sets and Systems
Particle swarm optimization method in multiobjective problems
Proceedings of the 2002 ACM symposium on Applied computing
Dynamic coast control of train movement with genetic algorithm
International Journal of Systems Science
An effective use of crowding distance in multiobjective particle swarm optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
MOPSO: a proposal for multiple objective particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Distributed search in railway scheduling problems
Engineering Applications of Artificial Intelligence
Block-layout design using MAX-MIN ant system for saving energy on mass rapid transit systems
IEEE Transactions on Intelligent Transportation Systems
A non-dominated sorting particle swarm optimizer for multiobjective optimization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
On convergence of the multi-objective particle swarm optimizers
Information Sciences: an International Journal
A MOPSO algorithm based exclusively on pareto dominance concepts
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Solving a periodic single-track train timetabling problem by an efficient hybrid algorithm
Engineering Applications of Artificial Intelligence
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
Metro Traffic Regulation by Adaptive Optimal Control
IEEE Transactions on Intelligent Transportation Systems
Engineering Applications of Artificial Intelligence
Multi-objective optimization using teaching-learning-based optimization algorithm
Engineering Applications of Artificial Intelligence
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One of the strategies for the reduction of energy consumption in railways systems is to execute efficient drivings (eco-driving). This eco-driving is the speed profile that requires the minimum energy consumption without degrading commercial running times or passenger comfort. When the trains are equipped with Automatic Train Operation systems (ATO) additional difficulties are involved. Their particular features make it necessary to develop accurate models that optimize the combination of the ATO commands of each speed profile to be used by the traffic regulation system. These commands are transmitted to the train via encoded balises on the track with little channel capacity (bandwidth). Thus, only a few and discrete values of the commands can be sent and the solution space of every interstation is made up of a relatively small set of speed profiles. However, the new state-of-the-art of signalling technologies permit a better bandwidth resulting in an exponential solution space. This calls for new methods for the optimal design of the ATO speed profiles without an exhaustive simulation of all the combinations. A MOPSO algorithm (Multi Objective Particle Swarm Optimization) to obtain the consumption/time Pareto front based on the simulation of a train with a real ATO system is proposed. The algorithm is able even to take into account only the comfortable speed profiles of the solution space. The fitness of the Pareto front is verified by comparing it with a NSGA-II algorithm (non-dominated sorting genetic algorithm II) and with the real Pareto front. Further, it has been used to obtain the optimal speed profiles in a real line of the Madrid Underground.