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An ant colony optimization processing method for large-scale multi-objective intelligent mobile path selection

A path selection and moving path technology, applied in computing models, biological models, road network navigators, etc., can solve problems such as performance degradation

Inactive Publication Date: 2011-12-14
SOUTHWEST JIAOTONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At present, in the method of using ant colony optimization to solve MTSP, the representative ones are MACS, BIANT and UNBI, but the performance of these methods is seriously degraded when solving large-scale MTSP

Method used

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  • An ant colony optimization processing method for large-scale multi-objective intelligent mobile path selection
  • An ant colony optimization processing method for large-scale multi-objective intelligent mobile path selection
  • An ant colony optimization processing method for large-scale multi-objective intelligent mobile path selection

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Embodiment

[0062] The invention is adopted to solve the double-objective traveling salesman problem composed of 200 cities, which is constructed from two single-objective traveling salesman problems KroA200 and KroB200 in the standard database TSPLIB. The experiment uses Matlab as the implementation tool, and solves the problem on a computer with a CPU of AMD Sempron 1.6GHz, a memory of 1GB, and an operating system of Windows XP.

[0063] The specific implementation steps of a kind of ant colony optimization method for solving large-scale multi-objective traveling salesman problem provided by the present invention are as follows:

[0064] Step 1: Parameter initialization: set the parameters of the algorithm α=1, β=2, ρ=0.1, q 0 =0.9, ξ=0.1, K=5, N=20, T=100, Φ= , the weight vector is randomly generated, and the weight vector in this embodiment is λ 1 =[0.0170 0.9830],λ 2 =[0.0541 0.9459],λ 3 =[0.2177 0.7823],λ 4 =[0.2444 0.7556],λ 5 =[0.2551 0.7449],λ 6 =[0.2579 0.7421],λ 7 =[0....

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Abstract

The invention discloses an ant colony optimization processing method of large-scale multi-target intelligent moving route selection; after data of NTSP target logistics delivery addresses, distances between every two addresses, and M price of cost for passing through each route are obtained, a route planning unit is solved by ant colony optimization technology so as to obtain a specific walking route for intelligent mobile-agent delivery, and the route is outputted to an executive mechanism for realization. When the method is used to solve the problem of large-scale multi-target intelligent moving route selection, the invention has good optimization performance, and has the advantages of parallelism, self-organization, strong robustness, and the like, and the obtained solutions are large in quantity, high in quality, and have strong approximation capability to the real Pareto solution set; the obtained solution set has uniform distribution; the calculation speed is high. The inventioncan be used in intelligent processing units of route planning systems in fields of logistics distribution, intelligent traffic, internet, robots, etc.

Description

Technical field [0001] The invention relates to an intelligent mobile device, in particular to the technical field of optimizing and selecting a reasonable path for an intelligent mobile subject. Background technique [0002] Path planning is a key issue in the field of logistics distribution, especially in intelligent logistics supported by Internet of Things technology. The intelligent mobile agent who executes logistics distribution wants to complete the delivery of n customers' orders, and minimize M contradictory costs such as cost consumption and route distance as much as possible at the same time. From the perspective of mathematical processing methods, the above problem can be abstracted into a typical mathematical problem, the so-called multi-objective traveling salesman problem (Multi-objective Traveling Salesman Problem, MTSP), the general description of MTSP is: Given n cities and There are M costs between them, and each cost is contradictory. A traveler wants t...

Claims

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Application Information

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IPC IPC(8): G01C21/34G06N3/00
Inventor 张葛祥程吉祥
Owner SOUTHWEST JIAOTONG UNIV
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