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

Longda Wang, Xingcheng Wang, Zhao Sheng and Senkui Lu
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Longda Wang: School of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China
Xingcheng Wang: School of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China
Zhao Sheng: School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
Senkui Lu: School of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China

Energies, 2020, vol. 13, issue 3, 1-25

Abstract: 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.

Keywords: automatic train operation; multi-objective optimization; shark smell optimization algorithm; composite angle cosine; dual-population evolution mechanism; hardware-in-the-loop simulation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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