Multiobjective Optimization for Train Speed Trajectory in CTCS High-Speed Railway With Hybrid Evolutionary Algorithm

Shangguan, Wei; Xiao, Yan; Cai, Baigen; Wang, Jian

doi:10.1109/tits.2015.2402160

Cited by 107 publications

(54 citation statements)

References 26 publications

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“…Wei et al . proposed a multi‐objective optimization approach for high‐speed train speed trajectory to provide more solutions based on the hybrid evolutionary algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Train operation optimization with adaptive differential evolution algorithm based on decomposition

Liu

Zhu

et al. 2019

IEEJ Transactions Elec Engng

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The train operation is a multi‐objective optimization process including several operating indexes, such as safety, punctuality, precision stop, comfort, and low‐energy consumption. The speed profile and timetable for trains should be adjusted correspondingly to satisfy the passengers' demands while minimizing energy consumption. To solve this problem, we propose a multi‐objective optimization approach for train operation. First, we develop a single‐particle model of the train and a multi‐objective optimization model for the train operation, which is subject to the constraints such as safety requirement, passenger comfort, and low energy consumption. Second, to obtain the Pareto frontier of train operation, a uniform design multi‐objective adaptive differential evolution algorithm based on decomposition (UMADE/D) is studied and applied to solve the multi‐objective optimization model for the train operation. Finally, we present numerical examples based on the real‐life operation data from the Nanjing Metro Line 1 in Nanjing, China. Three searching methods, namely, the proposed UMADE/D, Non‐Dominated Sorting Genetic Algorithm, and Multi‐Objective Evolutionary Algorithm based on Decomposition, are implemented to find the Pareto frontier of train operation quickly and efficiently. The simulation results show the efficiency of the proposed UMADE/D algorithm, which can provide corresponding operation strategies and satisfy the multi‐objective demands when the train is in different operation state. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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“…Wei et al . proposed a multi‐objective optimization approach for high‐speed train speed trajectory to provide more solutions based on the hybrid evolutionary algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…[9] proposed a method to design robust and efficient speed profiles of a metro line, considering the objectives of running time and energy consumption. Wei et al [10] proposed a multi-objective optimization approach for high-speed train speed trajectory to provide more solutions based on the hybrid evolutionary algorithm.…”

Section: Introductionmentioning

confidence: 99%

Train operation optimization with adaptive differential evolution algorithm based on decomposition

Liu

Zhu

et al. 2019

IEEJ Transactions Elec Engng

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show abstract

“…There are many algorithm used to solve this problem, such as genetic algorithm, particle swarm optimization algorithm, Hybrid Evolutionary Algorithm, etc. (Domínguez et al, 2014;Miyatake and Ko, 2010;ShangGuan et al, 2015). As for speed tracking, fuzzy control is the most popular control method (Sicre et al, 2014;Yang et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Automatic Train Operation Speed Profile Optimization and Tracking with Multi-Objective in Urban Railway

Zhu

Zhang

et al. 2019

Period. Polytech. Transp. Eng.

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Besides energy-efficiency, people also want train operation to be comfortable, punctual and parking precise. In this paper, a multiobjective model for automatic train operation in urban railway is proposed by unifying dimensions of different objectives firstly. This model is built by applying multi-objective decision with the penalty function, based on the analysis of train performance and its operation environment. Then a genetic algorithm is developed to solve this model and obtain the optimal recommended speed profiles. Thirdly, fuzzy controller is designed to achieve track recommended speed profiles. Finally, with the help of Matlab software, control effect is verified based on simulation. From the simulation results, it can be seen this strategy can meet the requirement of multi-objective, which are energy-saving, parking precisely, running punctually and comfort.

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“…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.Energies 2020, 13, 714 2 of 25 of the speed trajectory for the high-speed train is established and an improved algorithm based on differential evolution and simulated annealing algorithms is designed [7]. A genetic algorithm with the binary encoding method is designed for obtain the high-quality timetables of urban rail transit systems based on two-objective (energy-saving strategies and service quality levels) model formulated [8].…”

mentioning

confidence: 99%

“…Energies 2020, 13, 714 2 of 25 of the speed trajectory for the high-speed train is established and an improved algorithm based on differential evolution and simulated annealing algorithms is designed [7]. A genetic algorithm with the binary encoding method is designed for obtain the high-quality timetables of urban rail transit systems based on two-objective (energy-saving strategies and service quality levels) model formulated [8].…”

mentioning

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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Multiobjective Optimization for Train Speed Trajectory in CTCS High-Speed Railway With Hybrid Evolutionary Algorithm

Cited by 107 publications

References 26 publications

Train operation optimization with adaptive differential evolution algorithm based on decomposition

Train operation optimization with adaptive differential evolution algorithm based on decomposition

Automatic Train Operation Speed Profile Optimization and Tracking with Multi-Objective in Urban Railway

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

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