Abstract
A parallel multipopulation genetic algorithm (PMPGA) is proposed to optimize the train control strategy, which reduces the energy consumption at a specified running time. The paper considered not only energy consumption, but also running time, security, and riding comfort. Also an actual railway line (Beijing-Shanghai High-Speed Railway) parameter including the slop, tunnel, and curve was applied for simulation. Train traction property and braking property was explored detailed to ensure the accuracy of running. The PMPGA was also compared with the standard genetic algorithm (SGA); the influence of the fitness function representation on the search results was also explored. By running a series of simulations, energy savings were found, both qualitatively and quantitatively, which were affected by applying cursing and coasting running status. The paper compared the PMPGA with the multiobjective fuzzy optimization algorithm and differential evolution based algorithm and showed that PMPGA has achieved better result. The method can be widely applied to related high-speed train.
Highlights
Since October 1964 the world’s first high-speed railway, Japan Tokaido Shinkansen, was born; high-speed railways started the rapid development
The result showed that the method can significantly reduce the maximum traction power. These methods and algorithms were effective, they can only be applied in mass rapid transit (MRT) and light rapid transit (LRT) systems
In order to verify the efficiency of the PMPGA, we compared it with another optimal algorithm; one is from YanXH who proposed an algorithm based on differential evolution [18] and the other one is from WangDC who proposed a multiobjective fuzzy optimization [19]
Summary
Since October 1964 the world’s first high-speed railway, Japan Tokaido Shinkansen, was born; high-speed railways started the rapid development. Research will lead to a decrease of huge energy consumption in everyday running of high-speed trains. The result showed that the method can significantly reduce the maximum traction power These methods and algorithms were effective, they can only be applied in mass rapid transit (MRT) and light rapid transit (LRT) systems. Due to the difference between MRT, LRT, and Journal of Applied Mathematics high-speed trains, these methods cannot be applied in highspeed trains for energy optimization. For high-speed trains, energy saving and trains control optimization were studied by scholars. Hwang [12] presented an approach to identify a fuzzy control model for determining an economic running pattern for a high-speed railway through an optimal compromise between trip time and energy consumption. The result demonstrates that the PMPGA improved algorithm was better with the SGA and it has achieved conspicuous energy reduction
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