Railway ticket amount pre-division method based on adaptive learning rate particle swarm algorithm

An adaptive learning rate and particle swarm algorithm technology, applied in the field of particle swarm algorithm, can solve the problem of inconsistency between the actual requirements of railway ticket pre-score strategy, and achieve the effect of easy implementation, fast convergence speed, and easy modeling.

Active Publication Date: 2021-06-11
WUHAN UNIV
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Problems solved by technology

[0007] The content of the present invention proposes a kind of particle swarm algorithm (Adam-PSO) railway fare pre-division strategy based on adaptive learning rate Adam, solves the difference between the railway fare pre-division strategy and actual demand under the passenger flow pattern under uncertainty and time-varying matching question

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  • Railway ticket amount pre-division method based on adaptive learning rate particle swarm algorithm
  • Railway ticket amount pre-division method based on adaptive learning rate particle swarm algorithm
  • Railway ticket amount pre-division method based on adaptive learning rate particle swarm algorithm

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[0047] The Adam-PSO algorithm is based on the traditional particle swarm optimization algorithm, and uses the adaptive optimization algorithm Adam to adaptively set the inertia weight w in the particle velocity update formula. From a macro point of view, the inertia weight w is iteratively updated with an overall decreasing trend; from a micro point of view, the inertia weight w sets different changing trends according to different particle information, and at the same time introduces the concept of momentum in physics and bias correction work to ensure self-adaptation Stability of the strategy. This strategy not only utilizes the characteristics of particles but also satisfies the setting strategy of decreasing inertia weight, so the ADAM-PSO algorithm can ensure the convergence, diversity and stability of the particle swarm algorithm, and can produce good results when dealing with optimization problems.

[0048] When using the Adam-PSO algorithm to implement the railway fare...

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Abstract

In order to realize intelligent distribution of ticket amounts based on railway passenger flow demands, the invention discloses a railway ticket amount pre-division method based on an adaptive learning rate particle swarm algorithm, a railway ticket amount distribution problem is modeled as a model of the particle swarm algorithm, and the railway ticket amounts are distributed by adaptively setting an inertia weight in the particle swarm algorithm, so the problem that the railway ticket amount pre-division strategy in the passenger flow form under uncertainty and time-varying characteristics does not conform to the actual demand can be well solved.

Description

technical field [0001] The present invention uses a particle swarm optimization (PSO) algorithm based on an adaptive learning rate (Adaptive Moment Estimation, Adam) to optimize the railway ticket pre-segmentation strategy. Background technique [0002] The traditional railway fare allocation strategy adopts a manual fixed allocation model for each station, which often causes a mismatch between the allocated fare and the actual demand, not only failing to meet the ticket purchase needs of users, but also affecting the revenue of one-way railway lines. With the rapid development of China's economy, the operating density of high-speed railway passenger trains has gradually increased, and the allocation of high-speed passenger train ticket quotas has canceled the traditional method of manual fixed allocation of tickets for each station. Take the centralized storage of all fares at the departure station, and solve the problem of transport capacity at stations along the way throu...

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Application Information

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IPC IPC(8): G06Q10/06G06Q50/30G06N3/00
CPCG06Q10/06315G06N3/006G06Q50/40
Inventor 李梦莹
Owner WUHAN UNIV
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