Application of Genetic Algorithm in Automatic Train Operation

Wang, Guiyang; Xiao, Shuo; Chen, Xi; Li, Xin

doi:10.1007/s11277-017-5228-6

Cited by 13 publications

(6 citation statements)

References 6 publications

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“…Again, a notch speed trajectory optimization method, based on Mixed Integer Linear Programming (MILP), is introduced in [12] to satisfy the traction/braking demands, which dynamically change with the selected notch by introducing a series of binary variables. Some works leverage the Genetic Algorithm (GA) technique to solve the optimization process of ATO speed curve [5], [13]. For example, [13] designs the optimization procedure by considering some performance indexes for ATO systems, such as speed protection, punctuality, accurate parking, comfort indexes and energy saving requirements.…”

Section: A Related Workmentioning

confidence: 99%

“…Some works leverage the Genetic Algorithm (GA) technique to solve the optimization process of ATO speed curve [5], [13]. For example, [13] designs the optimization procedure by considering some performance indexes for ATO systems, such as speed protection, punctuality, accurate parking, comfort indexes and energy saving requirements. Based on the same performance indexes, [5] suggests a multi-objective optimization strategy for the modified genetic algorithm, where its convergence speed has been increased by adding a penalty term into the fitness objective function.…”

Section: A Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A GoA4 Control Architecture for the Autonomous Driving of High-Speed Trains Over ETCS: Design and Experimental Validation

Barruffo,

Caiazzo,

Petrillo

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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Section: A Related Workmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

A GoA4 Control Architecture for the Autonomous Driving of High-Speed Trains Over ETCS: Design and Experimental Validation

Barruffo,

Caiazzo,

Petrillo

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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“…Railway transportation is an essential means of transportation; it cannot be replaced by others owing to its own superiorities such as safety, energy efficiency, comfortable nature, punctuality, large volume of transport, convenient, accurate parking, etc. [1]. Automatic train operation (ATO) target velocity trajectory optimization is a practical multiple optimization problem for railway transportation, the multiple performance indicators such as energy consumption, parking punctuality, comfort, accurate parking, and so on.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Shark Smell Optimization Algorithm Using Incorporated Composite Angle Cosine for Automatic Train Operation

Wang

Sheng

et al. 2020

Energies

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In this paper, an improved multi-objective shark smell optimization algorithm using composite angle cosine is proposed for automatic train operation (ATO). Specifically, when solving the problem that the automatic train operation velocity trajectory optimization easily falls into local optimum, the shark smell optimization algorithm with strong searching ability is adopted, and composite angle cosine is incorporated. In addition, the dual-population evolution mechanism is adopted to restrain the aggregation phenomenon in shark population at the end of the iteration to suppress the local convergence. Correspondingly, the composite angle cosine, considering the numerical difference and preference difference, is used as the evaluation index, which ameliorates the shortcoming that the traditional evaluation index is not objective and reasonable. Finally, the Matlab/simulation and hardware-in-the-loop simulation (HILS) results for automatic train operation show that the improved optimization algorithm proposed in this paper has better optimization performance.

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“…At present, many intelligent optimization algorithms and their improved algorithms have been applied in train operation strategy optimization, such as genetic algorithm (GA), particle swarm optimization (PSO), simulated annealing (SA) algorithm, differential evolution (DE) algorithm, hybrid evolutionary algorithm and so on. Wang et al [7] use GA with global search to optimize the speed curve of automatic train operation (ATO) to obtain an accurate train control sequence, which satisfies the speed protection index, punctuality index, accurate parking index, comfort index and energy saving index. The authors in [8], aiming at the ATO system, adopt the multi-objective optimization strategy of GA to optimize from five aspects: safety, accurate parking, punctuality, energy saving and comfort.…”

Section: Introductionmentioning

confidence: 99%

“…The authors in [7][8][9][10] have achieved good results in the multi-objective optimization of train operation strategy through some common optimization algorithms, and one common feature of these algorithms is that the single population search method is used for the optimal solution. If the single population search strategy is extended to the multi-population search strategy, better optimization effect may be obtained.…”

Section: Introductionmentioning

confidence: 99%

Train Operation Strategy Optimization Based on a Double-Population Genetic Particle Swarm Optimization Algorithm

Liu

Wang

2019

Energies

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Train operation strategy optimization is a multi-objective optimization problem affected by multiple conditions and parameters, and it is difficult to solve it by using general optimization methods. In this paper, the parallel structure and double-population strategy are used to improve the general optimization algorithm. One population evolves by genetic algorithm (GA), and the other population evolves by particle swarm optimization (PSO). In order to make these two populations complement each other, an immigrant strategy is proposed, which can give full play to the overall advantages of parallel structure. In addition, GA and PSO is also improved, respectively. For GA, its convergence speed is improved by adjusting the selection pressure adaptively based on the current iteration number. Elite retention strategy (ERS) is introduced into GA, so that the best individual in each iteration can be saved and enter the next iteration process. In addition, the opposition-based learning (OBL) can produce the opposition population to maintain the diversity of the population and avoid the algorithm falling into local convergence as much as possible. For PSO, linear decreasing inertia weight (LDIW) is presented to better balance the global search ability and local search ability. Both MATLAB simulation results and hardware-in-the-loop (HIL) simulation results show that the proposed double-population genetic particle swarm optimization (DP-GAPSO) algorithm can solve the train operation strategy optimization problem quickly and effectively.

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Application of Genetic Algorithm in Automatic Train Operation

Cited by 13 publications

References 6 publications

A GoA4 Control Architecture for the Autonomous Driving of High-Speed Trains Over ETCS: Design and Experimental Validation

A GoA4 Control Architecture for the Autonomous Driving of High-Speed Trains Over ETCS: Design and Experimental Validation

Multi-Objective Shark Smell Optimization Algorithm Using Incorporated Composite Angle Cosine for Automatic Train Operation

Train Operation Strategy Optimization Based on a Double-Population Genetic Particle Swarm Optimization Algorithm

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