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Disaster assistance ambulance path planning method based on multi-agent genetic algorithm

A genetic algorithm and multi-agent technology, applied in computing, instrumentation, data processing applications, etc., can solve problems such as easy to fall into local optimum and slow operation speed, improve global search ability, and improve the results of operation convergence. The effect of algorithm performance

Inactive Publication Date: 2017-10-24
XIDIAN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The genetic algorithm continuously searches the population through iterations, and can search for the global optimal solution without any initialization information, but it also has the disadvantages of slow operation speed and easy to fall into local optimum

Method used

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  • Disaster assistance ambulance path planning method based on multi-agent genetic algorithm
  • Disaster assistance ambulance path planning method based on multi-agent genetic algorithm
  • Disaster assistance ambulance path planning method based on multi-agent genetic algorithm

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Effect test

Embodiment 1

[0035] With the development of the economy, various sudden disasters, including natural disasters, frequently occur and constantly invade our lives, threatening people's lives. A large number of patients appear at the same time in a disaster, and they all need medical assistance. A reasonable method of ambulance route planning can rescue more patients faster, which is very meaningful in disaster rescue.

[0036] Multi-agent genetic algorithm is an optimization algorithm based on the agent's ability to perceive and react to the environment. It replaces the evolution of the population with the grid of agents. Each individual can only perceive its local environment and can only communicate with its neighbors. domain interactions. The main advantages of multi-agent genetic algorithm are small population size and high algorithm stability, but there are also disadvantages that it is easy to fall into local optimum.

[0037]Aiming at the above-mentioned problem of easily falling int...

Embodiment 2

[0059] The disaster rescue ambulance path planning method based on the multi-agent genetic algorithm is the same as embodiment 1, and the path of the ambulance is initialized by encoding in step (2a), including the following steps:

[0060] (2a-1) The basic requirements of the agent: the population size of the agent grid L of the ambulance initial planning scheme is L size × L size , each grid point in the agent grid represents an agent, the agent cannot move, and can only perceive the information of its neighbors, that is to say, it can only interact with its adjacent agents. An agent contains a path plan sequence, and each path plan sequence in the agent grid is a chromosome, but a chromosome is not equal to an agent, it is only a path plan sequence for an ambulance, and a path plan for an ambulance The scenario sequence contains multiple ambulance routes.

[0061] (2a-2) Initialize the chromosome information in the agent: use 0 to m–1 to represent ambulances, m is the tot...

Embodiment 3

[0063] The disaster rescue ambulance path planning method based on the multi-agent genetic algorithm is the same as embodiment 1, in step (4), find the contemporary optimal path scheme and the globally optimal path scheme in the agent grid by iteration, specifically including have:

[0064] (4a) Using the fitness function formula, calculate the weighted sum of the latest service time of the red and green marked patients of each path scheme sequence; select the best path scheme found in this generation;

[0065] (4b) Execute the local search operator to optimize the contemporary optimal path; the local search operator of the present invention can also be called a self-learning operator, including 2-opt operator, 2-swap operator and 1-Insertion operator3 operator; the local search operator searches for as many excellent path schemes as possible through its own optimization learning; if the newly generated scheme is better than the contemporary optimal path scheme, the new indivi...

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Abstract

The present invention discloses a disaster assistance ambulance path planning method based on a multi-agent genetic algorithm. The problem is solved that the ambulance path planning in disaster assistance is easy to fall in local optimum. The implementation steps of the method comprises: setting of an ambulance path; generating initial path planning through adoption of the multi-agent genetic algorithm; generating a new path planning through adoption of a genetic operator on an original scheme basis; searching a current global optimal path scheme in an agent grid through iteration; and if the iteration condition is satisfied, outputting the global optimal path scheme, or else, performing new iteration optimization until obtaining the global optimization, and outputting the path planning of the disaster assistance ambulance. The disaster assistance ambulance path planning method based on the multi-agent genetic algorithm is used for vehicle path planning of an ambulance in the disaster assistance, and employs the multi-agent genetic algorithm to take the latest service time as individual evaluation standard, design operations such as an effective coding mode and a local search operator and design an ambulance path planning scheme with high efficiency so as to improve the efficiency of the ambulance path planning.

Description

technical field [0001] The invention belongs to the technical field of computer applications, and relates to a disaster rescue ambulance route planning method, in particular to a disaster rescue ambulance route planning method based on a multi-agent genetic algorithm. It can be used to design the path scheme of ambulances in the process of disaster rescue, so as to save people's lives to a greater extent. Background technique [0002] In recent years, with the intensification of industrialization, various emergencies including natural disasters have occurred more and more frequently, affecting people's lives and even threatening people's lives. There are more and more incentives for public emergencies, the difficulty of forecasting is increasing, and it is becoming more and more difficult to deal with emergencies. As an emerging discipline, emergency rescue requires the emergency response subject to respond in a timely and rapid manner when information is highly missing, ta...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/30
CPCG06Q10/047G06Q50/40
Inventor 刘静焦李成赵义爱
Owner XIDIAN UNIV
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