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Vehicle position routing method based on non-dominated sorting particle swarm genetic algorithm

A technology of non-dominated sorting and genetic algorithm, applied in the field of artificial intelligence automatic driving, can solve problems such as slow speed and high resource consumption, and achieve the effect of increasing the proportion and increasing the diversity

Pending Publication Date: 2022-04-26
BEIJING INSTITUTE OF TECHNOLOGYGY
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Problems solved by technology

[0007] The purpose of the present invention is to overcome the defects existing in the prior art, in order to solve the technical problems of the vehicle location routing method such as slow speed and high resource consumption, and creatively propose a vehicle location routing method based on non-dominated sorting particle swarm genetic algorithm

Method used

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  • Vehicle position routing method based on non-dominated sorting particle swarm genetic algorithm
  • Vehicle position routing method based on non-dominated sorting particle swarm genetic algorithm
  • Vehicle position routing method based on non-dominated sorting particle swarm genetic algorithm

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Embodiment

[0107] A vehicle location routing method based on non-dominated sorting particle swarm genetic algorithm, comprising the following steps:

[0108] Step 1: Encoding, decoding and searching of genetic algorithm for feasible solution individuals in the population.

[0109] Take an instance of the LRP dataset provided by karaoglan as an example, such as figure 1 As shown, the coordinates of the warehouse and the user are given, determined by (X, Y), the fixed use cost F of the vehicle, the vehicle capacity q, and the user's demand Q.

[0110] figure 2A final solution obtained for the above example is given in , which is the solution individual according to the encoding method of this method. The individual is a Pareto optimal solution, further optimization of any one of the three objective functions will increase the value of the other two objective functions, and there is no other solution individual Pareto dominating the solution in the population. In the example given in th...

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Abstract

The invention relates to a vehicle position routing method based on a non-dominated sorting particle swarm genetic algorithm, and belongs to the technical field of artificial intelligence automatic driving. According to the non-dominated sorting particle swarm genetic algorithm mixed with global and local search, the change operator of the particle swarm algorithm and the change operator of the genetic algorithm are used for performing population evolution at the same time, the search capability is improved, and the convergence speed is increased. And individuals entering the next generation are selected by using a selection mechanism of an NSGA-III algorithm, so that population diversity is realized. Neighborhood search is used as local search, a better solution can be obtained with a higher probability, convergence is accelerated, and the algorithm is prevented from falling into a local optimal solution in the later period; the proportion of Pareto non-dominated solutions of the whole population can be improved through local search of the suboptimal individuals, the quality of the whole population solutions and the diversity of the Pareto non-dominated solutions are improved, and the convergence speed is further increased. The effectiveness of the method in solving the vehicle position routing problem is better than that of the existing method.

Description

technical field [0001] The invention relates to a vehicle position routing method, in particular to a vehicle position routing method based on a non-dominated sorting particle swarm genetic algorithm, and belongs to the technical field of artificial intelligence automatic driving. Background technique [0002] In the field of autonomous driving, the vehicle location routing problem (Location-routing problem, LRP) is one of the important problems of vehicle scheduling. The LRP problem is a collection of two NP-hard problems, namely the Location-allocation problem (LAP) and the Vehicles routing problem (VRP). [0003] Laporte described the classic LRP problem in 1986. This problem needs to select the optimal location from potential facilities (such as warehouses), assign users with delivery needs to warehouses for services, and need to meet the needs of users assigned to warehouses. Exceed the capacity of the warehouse and determine the vehicle routes that start from the ware...

Claims

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Application Information

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IPC IPC(8): G06N3/12G01C21/34G06N3/00
CPCG06N3/126G06N3/006G01C21/3407G01C21/3453G01C21/3469
Inventor 刘琼昕牛文涛王琰康王亚男马旺
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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