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Fusion Swarm-Intelligence-Based Decision Optimization for Energy-Efficient Train-Stopping Schemes.

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Bibliographic Details
Title: Fusion Swarm-Intelligence-Based Decision Optimization for Energy-Efficient Train-Stopping Schemes.
Authors: Jia, Xianguang, Zhou, Xinbo, Bao, Jing, Zhai, Guangyi, Yan, Rong
Source: Applied Sciences (2076-3417); Feb2023, Vol. 13 Issue 3, p1497, 12p
Subject Terms: GLOBAL optimization, SWARM intelligence, MATHEMATICAL optimization, ENERGY consumption, STATISTICAL decision making, GENETIC algorithms, CONSUMPTION (Economics)
Abstract: To solve the decision problem of train stopping schemes, this paper introduces the static game into the optimal configuration of stopping time to realize the rational decision of train operation. First, a train energy consumption model is constructed with the lowest energy consumption of train operation as the optimization objective. In addition, a Mustang optimization algorithm based on cubic chaos mapping, the population hierarchy mechanism, the golden sine strategy, and the Levy flight strategy was designed for solving the problem of it being easy for the traditional population intelligence algorithm to fall into a local optimum when solving complex problems. Lastly, simulation experiments were conducted to compare the designed algorithm with PSO, GA, WOA, GWO, and other cutting-edge optimization algorithms in cross-sectional simulations, and the results show that the algorithm had excellent global optimization finding and convergence capabilities. The simulation results show that the research in this paper can provide effective decisions for the dwell time of trains at multiple stations, and promote the intelligent operation of the train system. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
Description
Abstract:To solve the decision problem of train stopping schemes, this paper introduces the static game into the optimal configuration of stopping time to realize the rational decision of train operation. First, a train energy consumption model is constructed with the lowest energy consumption of train operation as the optimization objective. In addition, a Mustang optimization algorithm based on cubic chaos mapping, the population hierarchy mechanism, the golden sine strategy, and the Levy flight strategy was designed for solving the problem of it being easy for the traditional population intelligence algorithm to fall into a local optimum when solving complex problems. Lastly, simulation experiments were conducted to compare the designed algorithm with PSO, GA, WOA, GWO, and other cutting-edge optimization algorithms in cross-sectional simulations, and the results show that the algorithm had excellent global optimization finding and convergence capabilities. The simulation results show that the research in this paper can provide effective decisions for the dwell time of trains at multiple stations, and promote the intelligent operation of the train system. [ABSTRACT FROM AUTHOR]
ISSN:20763417
DOI:10.3390/app13031497