CN108537338B - Disaster rescue emergency resource scheduling method based on multi-agent genetic algorithm - Google Patents
Disaster rescue emergency resource scheduling method based on multi-agent genetic algorithm Download PDFInfo
- Publication number
- CN108537338B CN108537338B CN201810321384.5A CN201810321384A CN108537338B CN 108537338 B CN108537338 B CN 108537338B CN 201810321384 A CN201810321384 A CN 201810321384A CN 108537338 B CN108537338 B CN 108537338B
- Authority
- CN
- China
- Prior art keywords
- agent
- emergency
- energy
- grid
- intelligent
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Biophysics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Evolutionary Biology (AREA)
- Strategic Management (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Entrepreneurship & Innovation (AREA)
- Economics (AREA)
- General Physics & Mathematics (AREA)
- Marketing (AREA)
- Biomedical Technology (AREA)
- Tourism & Hospitality (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Educational Administration (AREA)
- Development Economics (AREA)
- Physiology (AREA)
- Genetics & Genomics (AREA)
- Artificial Intelligence (AREA)
- General Business, Economics & Management (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a disaster rescue emergency resource scheduling method based on a multi-agent genetic algorithm, which mainly solves the problems of high freight and long rescue delay time in emergency resource scheduling in the prior art. The scheme is as follows: 1) constructing an emergency resource scheduling network, using the emergency resource scheduling network as an intelligent agent, constructing an intelligent agent grid by using 25 intelligent agents, and calculating the energy of each intelligent agent in the intelligent agent grid; 2) setting the maximum iteration times T, and sequentially performing neighborhood competition, neighborhood crossing and mutation operations on the intelligent agent grids to obtain a local optimal intelligent agent; 3) self-learning operation is carried out on the local optimal intelligent agent; 4) and judging whether the cyclic algebra reaches the maximum iteration times, if so, outputting the optimal scheduling scheme of the emergency resource network, otherwise, adding 1 to T, and returning to the step 3). The invention reduces the transportation cost and the total delay time generated by emergency scheduling, improves the timeliness and the high efficiency of emergency resource scheduling, and can be used for safely processing natural disasters.
Description
Technical Field
The invention belongs to the technical field of computers, and particularly relates to a disaster rescue emergency resource scheduling method which can be used for safely processing natural disasters.
Background
In recent years, various natural disasters frequently occur, which bring serious influence to the production and life of people and huge property loss to the nation. With the improvement of the technological level, the ability of human beings to know the world and reform the world is continuously enhanced, and the early warning ability of natural disasters of a certain scale is obviously improved. But has certain defects for dealing with some emergencies, such as earthquakes, floods and the like. When the emergency events are processed, the operation of emergency resources is fragile, the standardization degree is not high, and the scheduling structure is unreasonable. The existing emergency resource scheduling measures are difficult to ensure the timeliness and high efficiency of rescue. In order to deal with the influence of various natural disasters and corresponding emergencies, China already sets up a national public emergency overall plan for emergencies, thereby ensuring the smooth development of emergency rescue and ensuring that the basic life of the masses in disaster areas can be met to a certain extent. However, in the face of complex situations and a plurality of uncertain factors, how to ensure that rescue goods and materials are reasonably scheduled so as to reduce the loss of lives and properties of people and ensure the timeliness and high efficiency of rescue remains an important subject of research.
Zhang L im et al disclose an adaptive mutation genetic algorithm based Emergency resource scheduling method in the published paper "Emergency resources scheduling based on adaptive mutation and resource scheduling" ("Computer in Human Behavior" article serial No. 1493-1498(2011) ".
Hubei Xin latitude Emergency science and technology Limited discloses a resource scheduling optimization method based on a genetic algorithm in a patent of 'a resource scheduling optimization method based on a genetic algorithm' (application No. 201410154950.X, application publication No. CN 103927584A). The method comprises the following steps: 1. randomly generating an initial population S: coding N resource individuals of various types in S, wherein all resource individuals form a group; 2. calculating the fitness value of each individual in the population according to a preset fitness function; 3. carrying out individual selection, crossing and mutation operations; 4. and (3) judging whether the current individual meets the termination condition, if so, decoding to obtain an optimal resource scheduling scheme, and if not, returning to the step (2) for next iteration processing based on the child population generated by the individual. The method has the disadvantages of low calculation speed, easy falling into local optimum, and insufficient timeliness and high efficiency for scheduling the urgent resources.
Disclosure of Invention
The invention aims to provide a disaster rescue emergency resource scheduling method based on a multi-agent genetic algorithm aiming at the defects of the prior art, so that the algorithm speed is improved, local optimization is prevented, and the scheduling of emergency resources is more timely and efficient.
The technical scheme of the invention for realizing the aim comprises the following steps:
(1) constructing an emergency resource scheduling network:
setting three marks of emergency material supply points as S1，S2，S3；
Setting three emergency material demand point marks as D1，D2，D3；
Setting the capability marks of three emergency material supply points for supplying emergency resources as SQ1，SQ2，SQ3；
Setting the emergency resource demand marks of three emergency material demand points to be DQ respectively1，DQ2，DQ3；
Setting target time marks of the dispatching units for the emergency materials to reach three demand points as t1，t2，t3；
Setting from emergency material supply point SiTo emergency material demand point DjThe quantity of emergency materials for distribution is marked as cij；
Setting the time value from the ith supply point transportation unit emergency material to the jth demand point as tijWherein i and j are each an integer from 1 to 3;
the three emergency material supply points provide required emergency resources for the three emergency material demand points, the material quantity provided by each emergency material supply point to the emergency material demand points cannot exceed the supply capacity of the emergency material supply point, the material quantity supplied by all the emergency material supply points should meet the demand quantity of each emergency material demand point, and the time required from unit emergency resources to the emergency material demand points is scheduled, so that an emergency resource scheduling network is formed;
(2) taking an emergency resource scheduling network as an agent, constructing an agent grid with the size of 5 × 5 by using 25 agents, namely, in the agent grid, each row has 5 agents, each column has 5 agents, and calculating the energy E of each agent in the agent grid according to a fitness function;
(3) based on a multi-agent genetic algorithm, performing neighborhood competition, neighborhood crossing and mutation operations on an agent grid with the size of 5 × 5 in sequence to obtain a local optimal agent in a 5 × 5 grid;
(4) self-learning operation is carried out on the local optimal intelligent agent:
(4a) constructing an intelligent agent grid with the size of 3 × 3 by using local optimal intelligent agents in a 5 × 5 grid, namely, each row of the intelligent agent grid is provided with three intelligent agents, and each column of the intelligent agent grid is provided with three intelligent agents;
(4b) calculating the energy e of each intelligent agent in the intelligent agent grid with the size of 3 × 3 according to the fitness function;
(4c) performing neighborhood competition and mutation operations on the intelligent agent grids with the size of 3 × 3 to obtain local optimal intelligent agents in 3 × 3 grids;
(4d) comparing the energy E of the 5 × 5 local optimal agent with the energy E of the 3 × 3 local optimal agent, if E is less than E, updating the 5 × 5 local optimal agent with the 3 × 3 local optimal agent, otherwise, not updating;
(4e) taking the updated 5 × 5 local optimal agent as an optimal emergency resource scheduling network;
(4f) setting the iteration number of the self-learning operation as t-20, judging whether the loop algebra of the current self-learning operation reaches the set iteration number, if so, executing the step (5), otherwise, adding 1 to the loop algebra of the self-learning operation, and returning to the step (4 c);
(5) setting the maximum iteration number of the multi-agent genetic algorithm as T1000; and (4) judging whether the cyclic algebra of the current multi-agent genetic algorithm reaches the maximum iteration times, if so, outputting the optimal scheduling scheme of the emergency resource network, otherwise, adding 1 to the cyclic algebra of the multi-agent genetic algorithm, and returning to the step (3).
Compared with the prior art, the invention has the following advantages:
firstly, because the intelligent agent grid is adopted as the initialization population, the invention reduces the iteration times of the optimization process, quickly finds the optimal emergency resource scheduling scheme and overcomes the defects of low convergence speed and more iteration times of the traditional method in the prior art. The invention accelerates the convergence speed of the optimized emergency resource scheduling and shortens the time consumed by the emergency resource scheduling.
Secondly, because the invention executes neighborhood competition operation, neighborhood cross operation and mutation operation on the intelligent grid, the search space for emergency resource scheduling network optimization is reduced, the optimization process of the emergency resource scheduling network is accelerated, and the defects of large search space and large calculation amount in the traditional method in the prior art are overcome. The invention reduces the search space and greatly reduces the calculation amount in the optimizing process.
Thirdly, the invention is suitable for the optimization problem of the large-scale emergency resource scheduling network because the self-learning operation is executed on the intelligent network, and overcomes the defects that the traditional method in the prior art is easy to fall into local optimum and has too low speed when solving the large-scale problem. When the method is used for solving the problem of optimization of a large-scale emergency resource scheduling network, the optimal solution can be quickly found, and the situation that the optimal solution falls into local optimization is prevented.
Drawings
FIG. 1 is a flow chart of an implementation of the present invention;
FIG. 2 is a schematic diagram of an emergency resource scheduling network.
Detailed Description
The invention is further described with reference to the accompanying drawings in which:
step 1, constructing an emergency resource scheduling network.
(1a) Setting network marks and parameters:
setting three marks of emergency material supply points as S1，S2，S3；
Setting three emergency material demand point marks as D1，D2，D3；
Setting the capability marks of three emergency material supply points for supplying emergency resources as SQ1，SQ2，SQ3；
Setting the emergency resource demand marks of three emergency material demand points to be DQ respectively1，DQ2，DQ3；
Setting target time marks of the dispatching units for the emergency materials to reach three emergency material demand points as t1，t2，t3；
Setting from emergency material supply point SiTo emergency material demand point DjThe main aspects of transportationThe quantity of emergency material is marked cij；
Setting the time value from the ith supply point transportation unit emergency material to the jth demand point as tijWhere i and j are each integers from 1 to 3.
(1b) Three emergency material supply points S are arranged1，S2，S3To three emergency material demand points D1，D2，D3Providing required emergency resources, each emergency material supply point SiTo emergency material demand point DjThe quantity of supplied material cannot exceed its own supply capacity SQiAll emergency material supply points S1，S2，S3The supplied material quantity should meet each emergency material demand point D1，D2，D3Required amount of (DQ)jFrom emergency material supply points SiTo emergency material demand point DjThe time required for dispatching the unit emergency supplies is tijFrom three emergency material supply points S1，S2，S3To three emergency material demand points D1，D2，D3The target time values of the emergency materials of the dispatching units are respectively t1，t2，t3Thereby forming an emergency resource scheduling network, as shown in fig. 2.
And 2, calculating the energy E of each agent in the agent grid.
(2a) Taking the emergency resource scheduling network constructed in the step 1 as an intelligent agent, and constructing an intelligent agent grid with the size of 5 × 5 by using 25 intelligent agents, namely in the intelligent agent grid, 5 intelligent agents are arranged in each row and 5 intelligent agents are arranged in each column;
(2b) computing 5 × 5 energy E of each agent in the agent grid:
(2b1) the objective function value f (a) for each agent in the 5 × 5 agent grid is calculated by the following objective function formula:
wherein a is the agent in the agent grid of 5 × 5, S is the search space, which represents all the network scheduling sequences of the emergency resources;
(2b2) the energy E of each agent in the agent grid of 5 × 5 is derived by the fitness function formula:
where energy (a) is a fitness function, i.e., the energy E of each agent in the agent grid of 5 × 5 is equal to its own fitness.
And 3, sequentially performing neighborhood competition, neighborhood crossing and mutation operations on the 5 × 5-size intelligent agent grid based on a multi-agent genetic algorithm to obtain the locally optimal intelligent agent in the 5 × 5 grid.
(3a) Performing neighborhood competition operation:
(3a1) selecting an agent, and finding out the agent with the maximum energy in the four neighborhoods from the upper, lower, left and right neighborhoods of the selected agent;
(3a2) comparing the energy of the agent with the maximum energy in the neighborhood with the energy of the selected agent, if the energy of the agent with the maximum energy in the neighborhood is larger than the energy of the selected agent, replacing the selected agent with the maximum energy in the neighborhood to obtain an updated agent, otherwise, not replacing, and keeping the selected agent to survive in an agent grid;
(3b) performing neighborhood crossing operation:
(3b1) selecting an agent from the agent grid after neighborhood competition is completed, and finding out the agent with the maximum energy in the four neighborhoods from the upper, lower, left and right neighborhoods of the selected agent;
(3b2) randomly generating two different intersection points point1 and point2, wherein the size of the two intersection points is smaller than the length of the intelligent agent code;
(3b3) exchanging genes between two intersections of the selected agent and the agent with the largest energy in the neighborhood;
(3c) carrying out mutation operation, namely selecting an agent from the agent grid after the neighborhood crossing is finished, and adding random disturbance conforming to Gaussian distribution to the agent so as to change the gene value on the agent and obtain a new agent after mutation;
(3d) the energy of each agent in the 5 × 5 agent grid is calculated, and the agent with the highest energy is determined by comparing the energy of each agent, i.e., the locally optimal agent in the 5 × 5 agent grid.
And 4, performing self-learning operation on the local optimal intelligent agent.
(4a) Constructing an agent grid with the size of 3 × 3 by using local optimal agents in a 5 × 5 grid, namely, each row of the agent grid is provided with three agents, and each column of the agent grid is provided with three agents;
(4b) calculate the energy e of each agent in the agent grid of size 3 × 3:
(4b1) the objective function value f (a) for each agent in the agent grid of 3 × 3 is calculated by the following objective function formula:
wherein a is the agent in the agent grid of 3 × 3, S is the search space, which represents all the network scheduling sequences of the emergency resources;
(4b2) the energy e of each agent in the agent grid of 3 × 3 is calculated by the following fitness function formula.
Where energy (a) is a fitness function, the energy e of each agent in the agent grid of 3 × 3 is equal to its own fitness.
(4c) Performing neighborhood competition and mutation operations on the intelligent agent grids with the size of 3 × 3 to obtain local optimal intelligent agents in 3 × 3 grids;
(4c1) performing neighborhood competition operation:
(4c11) selecting an agent, and finding out the agent with the maximum energy in the four neighborhoods from the upper, lower, left and right neighborhoods of the selected agent;
(4c12) comparing the energy of the agent with the maximum energy in the neighborhood with the energy of the selected agent, if the energy of the agent with the maximum energy in the neighborhood is larger than the energy of the selected agent, replacing the selected agent with the maximum energy in the neighborhood to obtain an updated agent, otherwise, not replacing, and keeping the selected agent to survive in an agent grid;
(4c2) carrying out mutation operation, namely selecting an agent from the agent grid after the neighborhood crossing is finished, and adding random disturbance conforming to Gaussian distribution to the agent so as to change the gene value on the agent and obtain a new agent after mutation;
(4c3) calculating the energy of each intelligent agent in the 3 × 3 intelligent agent grid, and comparing the energy of each intelligent agent to determine the intelligent agent with the highest energy, namely the intelligent agent with the highest energy which is the local optimal intelligent agent in the 3 × 3 intelligent agent grid;
(4d) comparing the energy E of the 5 × 5 local optimal agent with the energy E of the 3 × 3 local optimal agent, if E is less than E, updating the 5 × 5 local optimal agent with the 3 × 3 local optimal agent, otherwise, not updating;
(4e) taking the updated 5 × 5 local optimal agent as an optimal emergency resource scheduling network;
(4f) and (4) setting the iteration number of the self-learning operation as t-20, judging whether the loop algebra of the current self-learning operation reaches the set iteration number, if so, executing the step (5), and if not, adding 1 to the loop algebra of the self-learning operation, and returning to the step (4 c).
Step 5, setting the maximum iteration number of the multi-agent genetic algorithm as T1000; and judging whether the cyclic algebra of the current multi-agent genetic algorithm reaches the maximum iteration times, if so, outputting an optimal scheduling scheme of the emergency resource network, otherwise, adding 1 to the cyclic algebra of the multi-agent genetic algorithm, and returning to the step 3.
The effects of the invention can be further described by the following simulation results.
1. Simulation conditions are as follows:
the processor selected by the simulation experiment is Intel (R) core (TM) i5-2450M CPU @2.50GHz2.50GHz, the memory is 4G, the hard disk is 500G, the operating system is Microsoft windows7, and the programming environment is Visual studio 2010.
The data sets adopted by the inventive guidelines are as in tables 1, 2, 3, wherein:
table 1 shows emergency material supply points S without time delayiDispatching unit emergency material to emergency material demand point DjCost c ofij；
Table 2 is a test data set for supply points and demand points;
table 3 shows the emergency material supply points SiDispatching unit emergency material to emergency material demand point DjRequired time tij。
Target time t for dispatching unit emergency materials to reach three emergency material demand points1，t2，t3Set to 3h,4h,5h, respectively.
TABLE 1 cost of emergency materials for dispatching and transporting units without time delay
TABLE 2 test data sets for supply and demand points
TABLE 3 time required for transporting unit emergency supplies
2. Analysis of experimental contents and results:
the invention is used for optimizing 5 groups of data in the table 3 respectively, each group of data is simulated for 25 times, and then the optimal emergency resource scheduling scheme, the total transportation cost under the optimal emergency resource scheduling scheme and the total delay time under the optimal emergency resource scheduling scheme are calculated and compared with the existing memetic algorithm MA and the genetic algorithm GA, and the results are shown in the table 4.
Table 4 simulation results list
As can be seen from Table 4, the total transportation cost under the optimal emergency resource scheduling scheme calculated after each group of data is simulated for 25 times by the method is low, which shows that the method has high efficiency and high convergence rate when the emergency resource scheduling is optimized, and the optimal solution can be quickly found.
As can be seen from table 4, the total delay time under the optimal emergency resource scheduling scheme calculated after each group of data is simulated for 25 times by using the method is shorter, which shows that the method is fast in speed, good in optimization performance and more efficient in emergency resource scheduling optimization.
It can be seen from table 4 that the disaster rescue emergency material scheduling method based on the multi-agent genetic algorithm provided by the invention is effective, is obviously superior to the traditional memetic algorithm MA and genetic algorithm GA, and illustrates that the method designed by the invention can obtain a better emergency resource scheduling scheme and is faster and more efficient.
Claims (4)
1. A disaster rescue emergency resource scheduling method based on a multi-agent genetic algorithm is characterized by comprising the following steps:
(1) constructing an emergency resource scheduling network:
setting three marks of emergency material supply points as S1，S2，S3；
Setting three emergency material demand point marks as D1，D2，D3；
Setting the capability marks of three emergency material supply points for supplying emergency resources as SQ1，SQ2，SQ3；
Setting the emergency resource demand marks of three emergency material demand points to be DQ respectively1，DQ2，DQ3；
Setting target time marks of the dispatching units for the emergency materials to reach three demand points as t1，t2，t3；
Setting from emergency material supply point SiTo emergency material demand point DjThe emergency material quantity of allocation and transportation is marked as xij；
Setting the time value from the ith supply point transportation unit emergency material to the jth demand point as tijWherein i and j are each an integer from 1 to 3;
the three emergency material supply points provide required emergency resources for the three emergency material demand points, the material quantity provided by each emergency material supply point to the emergency material demand points cannot exceed the supply capacity of the emergency material supply point, the material quantity supplied by all the emergency material supply points should meet the demand quantity of each emergency material demand point, and the time required from unit emergency resources to the emergency material demand points is scheduled, so that an emergency resource scheduling network is formed;
(2) taking an emergency resource scheduling network as an agent, constructing an agent grid with the size of 5 × 5 by using 25 agents, namely, in the agent grid, each row has 5 agents, each column has 5 agents, and calculating the energy E of each agent in the agent grid according to a fitness function;
the method comprises the following steps:
first, the objective function value f (a) for each agent in the agent grid of 5 × 5 is calculated by the following objective function formula:
wherein a is an agent in an agent grid of 5 × 5, S is a search space representing all emergency resource network scheduling sequences, and xijIndicating from emergency material supply points SiTo emergency material demand point DjThe amount of emergency materials for allocation and transportation; c. CijFor from emergency material supply points SiDispatching unit emergency material to emergency material demand point DjThe cost of (a);
then, calculating the energy E of each agent through the following fitness function formula;
where energy (a) is a fitness function, i.e., the energy E of each agent is equal to its own fitness;
(3) based on a multi-agent genetic algorithm, performing neighborhood competition, neighborhood crossing and mutation operations on an agent grid with the size of 5 × 5 in sequence to obtain a local optimal agent in a 5 × 5 grid;
(4) self-learning operation is carried out on the local optimal intelligent agent:
(4a) constructing an intelligent agent grid with the size of 3 × 3 by using local optimal intelligent agents in a 5 × 5 grid, namely, each row of the intelligent agent grid is provided with three intelligent agents, and each column of the intelligent agent grid is provided with three intelligent agents;
(4b) calculating the energy e of each intelligent agent in the intelligent agent grid with the size of 3 × 3 according to the fitness function;
(4c) performing neighborhood competition and mutation operations on the intelligent agent grids with the size of 3 × 3 to obtain local optimal intelligent agents in 3 × 3 grids;
(4d) comparing the energy E of the 5 × 5 local optimal agent with the energy E of the 3 × 3 local optimal agent, if E is less than E, updating the 5 × 5 local optimal agent with the 3 × 3 local optimal agent, otherwise, not updating;
(4e) taking the updated 5 × 5 local optimal agent as an optimal emergency resource scheduling network;
(4f) setting the iteration number of the self-learning operation as t-20, judging whether the loop algebra of the current self-learning operation reaches the set iteration number, if so, executing the step (5), otherwise, adding 1 to the loop algebra of the self-learning operation, and returning to the step (4 c);
(5) setting the maximum iteration number of the multi-agent genetic algorithm as T1000; and (4) judging whether the cyclic algebra of the current multi-agent genetic algorithm reaches the maximum iteration times, if so, outputting the optimal scheduling scheme of the emergency resource network, otherwise, adding 1 to the cyclic algebra of the multi-agent genetic algorithm, and returning to the step (3).
2. The method of claim 1, wherein the neighborhood competition operation in step (3) is implemented as follows:
firstly, selecting an agent, and finding out the agent with the maximum energy in four neighborhoods from the upper, lower, left and right neighborhoods of the selected agent;
and then comparing the energy of the agent with the maximum energy in the neighborhood with the energy of the selected agent, if the energy of the agent with the maximum energy in the neighborhood is greater than the energy of the selected agent, replacing the selected agent with the maximum energy in the neighborhood to obtain an updated agent, otherwise, not replacing, and keeping the selected agent to survive in an agent grid.
3. The method of claim 1, wherein the neighborhood crossing operation in step (3) is to select an agent from the agent grid after completion of neighborhood competition, and find the agent with the largest energy in the four neighborhoods from the four neighborhoods, the upper, the lower, the left and the right of the selected agent; then randomly generating two different intersection points point1 and point2, wherein the size of the two intersection points is smaller than the length of the intelligent agent code; genes between the selected agent and the intersection of the agent with the greatest energy in its neighborhood are then swapped.
4. The method of claim 1, wherein the mutation operation of step (3) is to select an agent from the network of agents after the neighborhood crossing is completed, and to add a random perturbation conforming to the Gaussian distribution to the agent, thereby changing the gene values of the agent to obtain a new agent after mutation.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810321384.5A CN108537338B (en) | 2018-04-11 | 2018-04-11 | Disaster rescue emergency resource scheduling method based on multi-agent genetic algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810321384.5A CN108537338B (en) | 2018-04-11 | 2018-04-11 | Disaster rescue emergency resource scheduling method based on multi-agent genetic algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108537338A CN108537338A (en) | 2018-09-14 |
| CN108537338B true CN108537338B (en) | 2020-07-28 |
Family
ID=63479622
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810321384.5A Active CN108537338B (en) | 2018-04-11 | 2018-04-11 | Disaster rescue emergency resource scheduling method based on multi-agent genetic algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108537338B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109711046B (en) * | 2018-12-26 | 2019-09-20 | 电子科技大学 | Large Scale Sparse array synthetic method based on multi-Agent Genetic Algorithm |
| CN110020778A (en) * | 2019-02-21 | 2019-07-16 | 国网山东省电力公司临沂供电公司 | The power emergency distribution of materials and configuration system and method |
| CN109886584B (en) * | 2019-02-27 | 2022-09-02 | 安徽大学 | Heat supply network scheduling method and device based on intelligent agent |
| CN113111476B (en) * | 2021-04-29 | 2024-06-14 | 华北电力大学 | Human-vehicle-object emergency resource optimal scheduling method for improving toughness of power grid |
| CN117745042B (en) * | 2024-02-20 | 2024-05-24 | 广东省科技基础条件平台中心 | Multi-disaster-point emergency material scheduling method, device, medium and equipment |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105976030A (en) * | 2016-03-15 | 2016-09-28 | 武汉宝钢华中贸易有限公司 | Multi-agent-based platform scheduling intelligent sorting model structure |
| CN106910018A (en) * | 2017-02-23 | 2017-06-30 | 吉林大学 | One kind rescue resource regulating method and system |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7246075B1 (en) * | 2000-06-23 | 2007-07-17 | North Carolina A&T State University | System for scheduling multiple time dependent events |
| US8015127B2 (en) * | 2006-09-12 | 2011-09-06 | New York University | System, method, and computer-accessible medium for providing a multi-objective evolutionary optimization of agent-based models |
| CN107292450A (en) * | 2017-07-18 | 2017-10-24 | 西安电子科技大学 | Disaster assistance ambulance paths planning method based on multi-Agent Genetic Algorithm |
| CN107730056B (en) * | 2017-11-22 | 2020-04-14 | 合肥工业大学 | Emergency material modular scheduling method based on improved NSGA-II algorithm |
-
2018
- 2018-04-11 CN CN201810321384.5A patent/CN108537338B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105976030A (en) * | 2016-03-15 | 2016-09-28 | 武汉宝钢华中贸易有限公司 | Multi-agent-based platform scheduling intelligent sorting model structure |
| CN106910018A (en) * | 2017-02-23 | 2017-06-30 | 吉林大学 | One kind rescue resource regulating method and system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108537338A (en) | 2018-09-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108537338B (en) | Disaster rescue emergency resource scheduling method based on multi-agent genetic algorithm | |
| CN102063339B (en) | Resource load balancing method and equipment based on cloud computing system | |
| CN104572297B (en) | A kind of Hadoop job scheduling methods based on genetic algorithm | |
| Wan et al. | Optimal micro-siting of wind turbines by genetic algorithms based on improved wind and turbine models | |
| CN109491761A (en) | Cloud computing multiple target method for scheduling task based on EDA-GA hybrid algorithm | |
| CN113778654B (en) | Parallel task scheduling method based on particle swarm optimization algorithm | |
| CN103020730A (en) | Resource-constrained project scheduling method based on multi-agent evolutionary algorithm | |
| CN111027760A (en) | Power load prediction method based on least square vector machine | |
| Pooranian et al. | Independent task scheduling in grid computing based on queen bee algorithm | |
| CN104899101A (en) | Dynamic distributing method of software testing resources based on multi-object difference evolutionary algorithm | |
| CN112148446B (en) | Evolutionary strategy method for multi-skill resource limited project scheduling | |
| CN105205536A (en) | 1553B bus message transmission optimization method based on hybrid genetic algorithm | |
| Han et al. | Cost optimization problem of hybrid flow-shop based on PSO algorithm | |
| CN108710742B (en) | PGSA-GA hybrid algorithm-based fault section positioning method | |
| CN114676987B (en) | Intelligent flexible job shop active scheduling method based on hyper-heuristic algorithm | |
| CN115239028A (en) | Comprehensive energy scheduling method, device, equipment and storage medium | |
| Jin et al. | Scheduling strategy based on genetic algorithm for cloud computer energy optimization | |
| CN111080111B (en) | Power system economic dispatching method based on distributed non-convex optimization algorithm | |
| CN114519457A (en) | Provincial intelligent energy service platform task scheduling method and system based on particle swarm | |
| CN109741796B (en) | Parallel particle swarm alloy nanoparticle structure optimization method and system | |
| CN102289749A (en) | Method for sequencing tasks based on multi-agent concerted evolution | |
| Li | To solve the job shop scheduling problem with the improve quantum genetic algorithm | |
| CN108805288B (en) | One-dimensional nesting method and device and computer readable storage medium | |
| CN110490294A (en) | Forecast of solar irradiance Data Assimilation algorithm based on parallel double population PSO | |
| Huang et al. | Entropy-enhanced genetic algorithm with tabu search for job shop scheduling problems |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |