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A Path Finding Method Based on Particle Swarm Optimization

A particle swarm optimization and particle swarm technology, applied in digital transmission systems, electrical components, transmission systems, etc., can solve problems such as easy to fall into local optimum, achieve high solution space exploration ability, efficient shortest path selection method, and fast convergence speed and the effect on the ability to solve

Active Publication Date: 2018-08-10
SUN YAT SEN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The former converges quickly, but it is also easy to fall into local optimum, while the latter takes the optimal value in the particle neighborhood as social cognition, avoids premature convergence, and is difficult to fall into local optimum

Method used

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  • A Path Finding Method Based on Particle Swarm Optimization
  • A Path Finding Method Based on Particle Swarm Optimization
  • A Path Finding Method Based on Particle Swarm Optimization

Examples

Experimental program
Comparison scheme
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Embodiment 1

[0073] The road network of this embodiment experiment is as follows figure 2 As shown in , it contains 28 nodes and 46 arcs. It is an example of step-updating particle swarms, and introduces how social cognition, self-cognition and inertia affect the update of particle paths. Suppose you need to find an optimal path from point S to point D in the road network.

[0074] Update particle paths based on social or self-awareness. Assume that the dotted line path is the initial solution of a certain particle, and the dotted line path represents the optimal solution of the particle or the optimal solution of the particle neighborhood, that is, the reference solution. Now update the particles according to the reference solution, as follows:

[0075] ·Cluster the original solution and the reference solution, such as image 3 Dashed lines are divided into 5 categories.

[0076] • Randomly select the kth class, assuming k=1.

[0077] Get the original solution node n in the first ca...

Embodiment 2

[0089] The road network of this embodiment experiment is the self-made Guangzhou University City road network, with 333 road network nodes and 406 arc segments, such as Figure 8 shown.

[0090] The values ​​of various parameters in this embodiment are as follows: the number of particles in the initial particle swarm is 10, that is, 10 initial paths, the maximum number of iterations is 100, the range of random factors is [1,4], and the particle neighborhood The number is 6, and the cluster density radius is 400 meters.

[0091] The starting point and end point of the path and the shortest path obtained (the distance is about 4405 meters) such as Figure 9 shown. Figure 10 10 groups of initial solutions for one experiment. Figure 11 Indicates the results of 8 random experiments, the abscissa indicates the number of iterations, and the ordinate indicates the length of the path. The number of iterations ranges from 6 to 37, with an average of 25 times. The iteration time ran...

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Abstract

The present invention discloses an optimal path searching method based on particle swarm optimization. The method comprises a step of reading road network data and building a road network model, a step of randomly generating a plurality of feasible paths corresponding to particle one to one, and forming the particle swarm in an initial state, a step of taking the total distance of the feasible paths as a fitness function, and summing the road length of each arc in each particle, a step of according to the self experience of each particle, obtaining the feasible path of the particle which experiences an optimal fitness function so far as the optimal solution of the particle, a step of combining with a social experience, comparing the optimal solution of each particle in the adjacent domain of the particle, and obtaining a particle adjacent domain optimal solution, and a step of updating the feasible path of each particle to obtain a new particle solution set according to the optimal solution of the particle, the particle adjacent domain optimal solution and particle motion inertia if the number of iterations does not exceed a set maximum iteration number, otherwise comparing the particle adjacent domain optimal solution in a final time of iteration, selecting the optimal solution of the fitness function, and recording the optimal solution as a particle group optimal solution which is a preferred path.

Description

technical field [0001] The present invention relates to the field of path optimization of road networks, and more specifically, relates to a path optimization method based on particle swarm optimization. Background technique [0002] Path optimization is one of the research hotspots in computer science, operations research, geographic information science, etc. A fast and effective path optimization algorithm can enable the application system to get feedback quickly, provide valuable resource information, and meet user needs. [0003] The current path optimization research is based on the effective combination of classical graph theory and the ever-developing computer data structure and algorithm. The road network is abstracted as a network model, the actual path is abstracted as an edge of the network, and the path length is represented as an edge. Finding the shortest path between two points on the network is a non-deterministic problem. Among them, the most classic metho...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04L12/721
Inventor 胡继华梁嘉贤高立晓
Owner SUN YAT SEN UNIV