Method for solving multi-target shortest path in time-varying environment based on neural network

Abstract

A method for solving a multi-target shortest path in a time-varying environment based on a neural network comprises the following steps: monitoring and collecting data of an urban traffic network in real time by using map software, collecting a cost function of each edge in the network, and constructing a traffic network model; in combination with the topological structure of the network, designing a brand-new neuron structure and constructing a neural network model. Topological information of a traffic network is loaded into a neural network model, a neuron wave generator and a neuron filter are used for sending automatic waves to subsequent neuron, other neuron in the network is activated, and each neuron ignores response to part of waves through the filter. According to the method, non-optimal sub-paths are pruned by designing the neural network, so that the calculation complexity is greatly reduced, and the problem of accurately solving the multi-target shortest path in the network optimization field in a time-varying environment is solved. In addition, the problem solving speed is increased through parallel computing of the neural network.

Description

technical field [0001] The invention belongs to the combination of the technical field of network optimization and the technical field of artificial intelligence, and specifically relates to designing a method for solving multi-objective shortest paths in a time-varying environment based on a neural network. Background technique [0002] The classic multi-objective shortest path problem is a routing problem that optimizes multiple objectives. For example, in the process of transportation, if you want to find a path with a short distance and good road conditions, you need to optimize two objective functions at the same time, the shortest travel time and distance. The weight function value of each edge in the network does not change with time. However, for example, in the transportation process, the transportation time changes with the congestion of the road conditions, that is, the edge weight function is time-dependent, which also expands the problem of solving the multi-obj...

AI Technical Summary

Problems solved by technology

[0004] In terms of solving methods, from the research results at home and abroad, the computational complexity of solving multi-objective problems in a time-varying environment is high. Traditional graph theory solving methods such as dynamic programming and label setting algorithms are difficult to break through, while intelligent algorithms such as genetic algorithms and Ant colony algorithm, etc. cannot find an exact solution

[0005] In terms of solution speed, domestic and foreign research papers are based on traditional algorithms such as dynamic programming and label setting algorithms. Due to the large amount of calculation and low algorithm efficiency, the operating environment mostly relies on the support of high-performance computers. On popular PCs, unable to meet actual needs

[0006] In terms of solution accuracy, the traditional routing algorithm used by general systems ignores the time-varying environment to find the shortest path for multiple targets. In other words, the network is regarded as static, and it is difficult to find an accurate solution

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