CN113935556B - Temperature sensor optimal arrangement method based on DNA genetic algorithm - Google Patents

Temperature sensor optimal arrangement method based on DNA genetic algorithm Download PDF

Info

Publication number
CN113935556B
CN113935556B CN202111536202.4A CN202111536202A CN113935556B CN 113935556 B CN113935556 B CN 113935556B CN 202111536202 A CN202111536202 A CN 202111536202A CN 113935556 B CN113935556 B CN 113935556B
Authority
CN
China
Prior art keywords
optimization
target
temperature
temperature sensor
optimal
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
Application number
CN202111536202.4A
Other languages
Chinese (zh)
Other versions
CN113935556A (en
Inventor
刘鑫
皮辉
郭朝霞
许雷
范俊甫
蔡烨彬
程佳斌
杨志祥
谢倩
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Csic Wuhan Lingjiu Hi Tech Co ltd
Original Assignee
Csic Wuhan Lingjiu Hi Tech Co ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Csic Wuhan Lingjiu Hi Tech Co ltd filed Critical Csic Wuhan Lingjiu Hi Tech Co ltd
Priority to CN202111536202.4A priority Critical patent/CN113935556B/en
Publication of CN113935556A publication Critical patent/CN113935556A/en
Application granted granted Critical
Publication of CN113935556B publication Critical patent/CN113935556B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • 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

Abstract

The invention is suitable for the technical field of artificial intelligence and intelligent optimization calculation, and provides a temperature sensor optimal arrangement method based on a DNA genetic algorithm, which comprises the following steps: selecting point positions possibly arranged by the temperature sensor, and establishing a multi-objective optimization function by taking energy consumption measurement precision and energy consumption change sensitivity as optimization targets; establishing a multi-target expected value model, carrying out random simulation solving, and combining the multi-target optimization function to obtain a multi-target optimization model; randomly simulating by using a DNA genetic algorithm, and calculating the multi-objective optimization model; and obtaining target Pareto optimal solutions of the two optimization targets, repeatedly calculating to obtain a group of optimal solution sets, using the optimal solution sets as an input set of a deep learning algorithm, and searching for a better solution through the deep learning algorithm.

Description

Temperature sensor optimal arrangement method based on DNA genetic algorithm
Technical Field
The invention belongs to the technical field of artificial intelligence and intelligent optimization calculation, and particularly relates to a temperature sensor optimal arrangement method based on a DNA genetic algorithm.
Background
Under the background of smart cities, energy consumption supervision and energy-saving optimization of large buildings are leading to intelligent roads. By means of information technologies such as the Internet of things, cloud computing and big data, a more comprehensive and accurate data base can be provided, and the method has extremely important guiding significance for development of building energy-saving work in China. The key to obtaining a comprehensive and accurate data base is sensor acquisition, which involves a core problem, namely how to use the minimum number of sensors to optimize the arrangement of the sensors since the sensors acquire enough information.
The point location capable of arranging the sensors on the actual building structure of the sensor optimal arrangement problem is a research object, belongs to a discrete model, and is an integer programming combined optimization problem. If the number of the sensors is s and the number of the candidate positions is m, the total number is
Figure 18457DEST_PATH_IMAGE001
An arrangement scheme, which is calculated if the enumeration method is adopted to calculate the optimized objective function
Figure 957594DEST_PATH_IMAGE002
Secondly, for a structure with a small scale, the calculation method is acceptable, and the calculation method is difficult to solve for a large building structure. Especially, the temperature sensor is typical, the arrangement positions of the temperature sensor are many, and the temperature sensor is an important index for energy saving and consumption reduction, for example, the indoor comfort can be kept without cold summer and hot winter through intelligent and accurate temperature control, which is the key of energy saving. The solving of the problems is to establish a random integer programming model, in order to avoid dimension explosion, intelligent algorithms such as a genetic algorithm and the like are needed to be adopted for solving, and a DNA genetic algorithm is a genetic algorithm which is good in integer programming solving performance.
Temperature spatial distribution is difficult to measure directly, and usually, a plurality of single-point or ultrasonic temperature sensors are researched to form a sensor temperature measurement array, so that temperature field information is obtained by adopting an interpolation or fitting mode. The data redundancy of the sensors is caused by the excessive arrangement of the sensors, so the cost is increased; the insufficient number of the arranged sensors cannot sufficiently describe the spatial distribution information of the temperature field, and has a great influence on the accuracy of temperature control. Therefore, to obtain the temperature field distribution, the number of sensors and the measuring point positions are optimized, and the temperature field reconstruction is carried out by using the number and position information. In addition, the temperature change sensitivity of the sensor is also a key measure for the rational arrangement of the sensor.
The prior art is provided with a DNA genetic algorithm and a deep learning method aiming at an optimal arrangement method of a sensor, and the deep learning method is a tool for solving big data reasoning along with the development of artificial intelligence. However, for the optimal arrangement of the temperature sensors, the optimal objective function for performing the optimal arrangement of the sensors cannot be obtained by the current DNA genetic algorithm and deep learning, so that the improvement of the temperature measurement accuracy and the temperature change sensitivity of the temperature sensors is not accurate enough, and the energy saving and consumption reduction are not facilitated.
Disclosure of Invention
In view of the above problems, the present invention aims to provide a deep learning temperature sensor optimal arrangement method based on a DNA genetic algorithm, and aims to solve the technical problem that the existing optimal arrangement model of the temperature sensor is not accurate enough.
The invention adopts the following technical scheme:
the deep learning temperature sensor optimal arrangement method based on the DNA genetic algorithm comprises the following steps:
s1, selecting possible point positions of the temperature sensors, and establishing a multi-objective optimization function by taking the energy consumption measurement precision and the energy consumption change sensitivity as optimization targets;
s2, establishing a multi-target expected value model, carrying out random simulation solving, and combining the multi-target optimization function to obtain a multi-target optimization model;
s3, calculating the multi-objective optimization model by random simulation of a DNA genetic algorithm;
and step S4, obtaining target Pareto optimal solutions of two optimization targets, repeatedly calculating to obtain a group of optimal solution sets, using the optimal solution sets as an input set of a deep learning algorithm, and searching for a better solution through the deep learning algorithm.
The invention has the beneficial effects that: the invention provides a more reasonable temperature sensor arrangement measure function, a multi-objective optimization model taking energy consumption measurement precision and energy consumption change sensitivity as two optimization targets is formed, then a DNA genetic algorithm suitable for solving the integer programming problem is adopted for solving, the solved result provides a sample for deep learning to obtain a better solution, and the deep learning precision and efficiency can be further improved; the scheme of the invention can improve the temperature measurement precision and the temperature change sensitivity of the temperature sensor, and provides a new alternative for intelligently exploring the optimal arrangement of the temperature sensor and saving energy and reducing consumption.
Drawings
FIG. 1 is a flow chart of a deep learning temperature sensor optimal arrangement method based on a DNA genetic algorithm provided by an embodiment of the invention;
FIG. 2 is a flowchart illustrating an implementation of step S1 according to an embodiment of the present invention;
fig. 3 is a flowchart of an implementation of step S2 according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In order to explain the technical means of the present invention, the following description will be given by way of specific examples.
Fig. 1 shows a flow of a deep learning temperature sensor optimal arrangement method based on a DNA genetic algorithm according to an embodiment of the present invention, and only the parts related to the embodiment of the present invention are shown for convenience of description.
As shown in fig. 1, the deep learning temperature sensor optimal arrangement method based on the DNA genetic algorithm provided in this embodiment includes the following steps:
and S1, selecting possible point positions of the temperature sensors, and establishing a multi-objective optimization function by taking the energy consumption measurement precision and the energy consumption change sensitivity as optimization targets.
The step realizes the establishment of a multi-objective optimization function, the multi-objective optimization function considers the energy consumption measurement precision and the energy consumption change sensitivity, and the specific realization process is as follows:
and S11, selecting a point where the temperature sensor can be arranged.
Generally, a floor plan is established according to an actual arrangement situation, positions limited by physical conditions need to be removed, and the positions are uniformly distributed on the floor plan according to a minimum distance to generate N suitable positions, so that the final problem is that N temperature sensors are arranged at the N positions, and the purposes of good effect, energy conservation and money saving are achieved.
And S12, reconstructing the temperature field.
RBF (radial basis function) is generally used to solve the problem of multivariate interpolation and has been shown to approximate arbitrary functions with arbitrary accuracy, suitable for reconstruction of temperature fields.
N temperature sensors are arranged in a two-dimensional space, wherein the temperature of the jth measuring point
Figure 123871DEST_PATH_IMAGE003
In (1),
Figure 792750DEST_PATH_IMAGE004
and
Figure 520535DEST_PATH_IMAGE005
respectively possible sensor placements are plotted on the abscissa and ordinate. The jth sensor may be centered on a radial basis interpolation, where the radial basis function may be defined as:
Figure 478126DEST_PATH_IMAGE006
wherein, in the step (A),
Figure 683980DEST_PATH_IMAGE007
is a shape parameter of the radial basis function, is related to the arrangement position of the temperature sensor,
Figure 472944DEST_PATH_IMAGE008
in the formula
Figure 106051DEST_PATH_IMAGE009
The maximum distance between the points is measured by the sensor.
In order to obtain the distribution characteristics of the temperature in the two-dimensional space, the measuring points are interpolated by adopting a radial basis function, namely
Figure 550939DEST_PATH_IMAGE010
In the formula (I), wherein,
Figure 357221DEST_PATH_IMAGE011
is a temperature field reconstructed using RBF;
Figure 469533DEST_PATH_IMAGE012
are parameters to be determined and can be derived from this formula:
Figure 7962DEST_PATH_IMAGE013
wherein, in the step (A),
Figure 2463DEST_PATH_IMAGE014
o represents a 0 matrix.
S13, constructing a precision objective function
Figure 48654DEST_PATH_IMAGE015
To describe the accuracy of the reconstructed temperature field; n is the total number of temperature sensors, j is the jth temperature sensor,
Figure 15473DEST_PATH_IMAGE016
the measured point temperature of the jth temperature sensor,
Figure 724803DEST_PATH_IMAGE017
is the temperature field of the jth temperature sensor, f1Is a precision objective function.
In order to improve the accuracy of temperature field reconstruction as much as possible, an accuracy objective function is required to describe the accuracy of the reconstructed temperature field, and the smaller the objective value, the better.
S14, constructing a sensitivity objective function
Figure 409862DEST_PATH_IMAGE018
For the description ofReconstructing the sensitivity of the temperature field to the temperature difference change, n being the total number of temperature sensors, j being the j temperature sensor, f2In order to be the objective function of the sensitivity,
Figure 761209DEST_PATH_IMAGE019
is composed of
Figure 379272DEST_PATH_IMAGE020
The smaller the target value is, the better, and
Figure 525083DEST_PATH_IMAGE021
。
in order to improve the sensitivity of the temperature field reconstruction to temperature difference changes as much as possible, an objective function is required to be constructed to describe the sensitivity of the reconstructed temperature field to temperature difference changes, and the smaller the objective value, the better.
S15, obtaining a multi-objective optimization function
Figure 431859DEST_PATH_IMAGE022
And min represents taking the minimum value.
Finally f is obtained according to the above1、f2The multi-objective optimization function is to take f1、f2Is measured.
And S2, establishing a multi-target expected value model, performing random simulation solution, and combining the multi-target optimization function to obtain a multi-target optimization model.
The method specifically comprises the following steps:
and S21, establishing a multi-target expected value model.
In the random-integer programming expectation-value model, if each decision variable is required to be an integer, it can be regarded as a random-integer programming expectation-value model, let the decision vector be x,
Figure 321317DEST_PATH_IMAGE023
is a random vector of the number of bits,
Figure 59466DEST_PATH_IMAGE024
is an objective function, and
Figure 898151DEST_PATH_IMAGE025
is a set of random constraint functions, p is the number of constraint functions,
Figure 26644DEST_PATH_IMAGE026
e denotes the expectation operator, if and only if
Figure 516531DEST_PATH_IMAGE027
To solve
Figure 312449DEST_PATH_IMAGE028
Is feasible. If for each feasible solution
Figure 800062DEST_PATH_IMAGE029
Satisfy the requirement of
Figure 478168DEST_PATH_IMAGE030
This means a feasible solution
Figure 443850DEST_PATH_IMAGE031
Is the optimal solution for this expectation model.
In many cases, when many objective functions are to be considered, in this embodiment two objective functions. If the decision maker wishes to maximize the expectation of these objective functions. The present embodiment can establish the following multi-target expectation value model.
Figure 94274DEST_PATH_IMAGE032
And S22, solving the multi-target expected value model through random simulation to obtain a final multi-target optimization model.
Is provided with
Figure 815105DEST_PATH_IMAGE033
Is in a probability space
Figure 183770DEST_PATH_IMAGE034
Is/are as follows
Figure 953143DEST_PATH_IMAGE035
The dimensions of the random vector are then calculated,
Figure 254811DEST_PATH_IMAGE036
is a sample space, A is an event field, is
Figure 582762DEST_PATH_IMAGE037
The number of generations is determined by the number of generations,
Figure 438722DEST_PATH_IMAGE038
is a probability measure.
Figure 808524DEST_PATH_IMAGE039
Is a measurable function, then
Figure 167961DEST_PATH_IMAGE040
Is a random variable. The detailed steps are as follows: is provided with
Figure 433857DEST_PATH_IMAGE041
(ii) a According to probability measure
Figure 308272DEST_PATH_IMAGE042
From
Figure 685027DEST_PATH_IMAGE036
In the production of samples
Figure 961288DEST_PATH_IMAGE043
；③
Figure 398085DEST_PATH_IMAGE044
(ii) a Fourthly, repeating the step III and the step III for N times; fifthly
Figure 697480DEST_PATH_IMAGE045
。
Can be calculated according to the method
Figure 409084DEST_PATH_IMAGE046
And taking the maximum value.
Thus, finallyThe obtained multi-objective optimization model is
Figure 743113DEST_PATH_IMAGE047
。
And S3, calculating the multi-objective optimization model by random simulation of a DNA genetic algorithm.
The specific process using the DNA genetic algorithm is as follows:
s31, inputting parameters, wherein the parameters comprise population scale, cross probability, mutation probability and inversion probability;
inputting parameters
Figure 849347DEST_PATH_IMAGE048
Wherein
Figure 698354DEST_PATH_IMAGE049
In order to be of the population scale,
Figure 151332DEST_PATH_IMAGE050
in order to be a cross-over probability,
Figure 605447DEST_PATH_IMAGE051
the probability of the variation is the probability of the variation,
Figure 180785DEST_PATH_IMAGE052
is the probability of inversion;
s32, initial Generation
Figure 454772DEST_PATH_IMAGE053
The DNA strands forming the initial population
Figure 773758DEST_PATH_IMAGE054
And verifying the feasibility of the DNA chains by using a random simulation technology;
s33, calculating the fitness: translating the codon of each DNA chain in the population into a parameter value according to a cipher table, and then calculating the fitness of the individual by using a random simulation technology;
s34, selecting: from DNA strand populations with a certain probability
Figure 82379DEST_PATH_IMAGE054
Selecting m DNA chain individuals as parents for breeding offspring, verifying the feasibility of the offspring by using a random simulation technology, and adding new individuals meeting the feasibility into the next generation
Figure 766302DEST_PATH_IMAGE055
；
S35, carrying out crossing, mutation and inversion operation on the DNA chain, wherein the feasibility of the offspring is still tested by using random simulation;
s36, New Generation DNA population
Figure 324322DEST_PATH_IMAGE056
And repeating S33-S35 until a convergence condition is met.
The multi-objective optimization problem does not generally have a so-called absolute optimal solution, but 'compromises' among all objective functions and gives a solution of the problem in the form of a Pareto optimal solution set, and a final solution is selected from the Pareto optimal solution set by a decision maker according to actual conditions. The objective function vector corresponding to the Pareto optimal solution set forms a space curved surface, and the optimal solution can be found according to the Pareto front surface.
And step S4, obtaining target Pareto optimal solutions of two optimization targets, repeatedly calculating to obtain a group of optimal solution sets, using the optimal solution sets as an input set of a deep learning algorithm, and searching for a better solution through the deep learning algorithm.
S41, calculating to obtain target Pareto optimal solutions of the two optimization targets through the step S3, and repeatedly calculating the target Pareto optimal solutions of the two optimization targets to form a group of optimal solution sets;
s42, taking the optimal solution set as a training set, a verification set and a test set of a deep learning algorithm;
s43, deep learning training and verification are carried out;
and S44, reasoning the test set by using the trained deep learning model to obtain a more optimal solution arrangement mode.
The deep learning method is the prior art, and the detailed description of the specific calculation process is omitted here.
In conclusion, the energy consumption measurement precision and the energy consumption change sensitivity are used as two optimization targets to establish the multi-target optimization model, then the DNA genetic algorithm is adopted to solve, the solved result provides a sample for deep learning to obtain a better solution, the deep learning precision and efficiency can be further improved, and the temperature measurement precision and the temperature change sensitivity of the temperature sensor can be improved.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.

Claims (3)

1. A temperature sensor optimal arrangement method based on a DNA genetic algorithm is characterized by comprising the following steps:
s1, selecting possible point positions of the temperature sensors, and establishing a multi-objective optimization function by taking the energy consumption measurement precision and the energy consumption change sensitivity as optimization targets;
s2, establishing a multi-target expected value model, carrying out random simulation solving, and combining the multi-target optimization function to obtain a multi-target optimization model;
s3, calculating the multi-objective optimization model by random simulation of a DNA genetic algorithm;
s4, obtaining target Pareto optimal solutions of two optimization targets, repeatedly calculating to obtain a group of optimal solution sets serving as an input set of a deep learning algorithm, and searching for a better solution through the deep learning algorithm;
the step S1 specifically includes:
s11, selecting possible arrangement points of the temperature sensors;
s12, reconstructing a temperature field;
s13, constructing a precision objective function
Figure 806011DEST_PATH_IMAGE001
To describe the accuracy of the reconstructed temperature field; n is the total number of temperature sensors, j is the jth temperature sensor,
Figure 519889DEST_PATH_IMAGE002
the measured point temperature of the jth temperature sensor,
Figure 830785DEST_PATH_IMAGE003
is the temperature field of the jth temperature sensor, f1Is a precision objective function;
s14, constructing a sensitivity objective function
Figure 773464DEST_PATH_IMAGE004
Describing the sensitivity of the reconstructed temperature field to the temperature difference change, n is the total number of temperature sensors, j is the jth temperature sensor, f2In order to be the objective function of the sensitivity,
Figure 384574DEST_PATH_IMAGE005
is composed of
Figure 3775DEST_PATH_IMAGE006
The mean value of (a);
s15, obtaining a multi-objective optimization function
Figure 313883DEST_PATH_IMAGE007
Min represents taking the minimum value;
the step S2 specifically includes:
s21, establishing a multi-target expected value model
Figure 309521DEST_PATH_IMAGE008
Wherein the content of the first and second substances,
Figure 775138DEST_PATH_IMAGE009
is a set of random constraint functions, p is the number of constraint functions, x is the decision vector,
Figure 50392DEST_PATH_IMAGE010
is a random vector, E represents the expectation operator;
s22, solving the multi-target expected value model through stochastic simulation to obtain a final multi-target optimization model
Figure 601459DEST_PATH_IMAGE011
。
2. The method for optimal placement of temperature sensors based on DNA genetic algorithm as claimed in claim 1, wherein said step S3 specifically comprises:
s31, inputting parameters, wherein the parameters comprise population scale, cross probability, mutation probability and inversion probability;
s32, initially generating a DNA chain to form an initial population, and verifying the feasibility of the DNA chain by random simulation;
s33, translating the codon of each DNA chain in the population into a parameter value according to a cipher table, and then calculating the fitness of the individual by using random simulation;
s34, selecting DNA chain individuals from the DNA chain population according to a certain probability, using the DNA chain individuals as parents for breeding offspring, verifying the feasibility of the offspring by using random simulation, and adding new individuals meeting the feasibility into the next generation;
s35, carrying out crossing, mutation and inversion operation on the DNA chain, wherein the feasibility of the offspring is still tested by using random simulation;
s36, repeating S33-S35 until meeting the convergence condition.
3. The method for optimal placement of temperature sensors based on DNA genetic algorithm as claimed in claim 2, wherein said step S4 specifically comprises:
s41, calculating to obtain target Pareto optimal solutions of the two optimization targets through the step S3, and repeatedly calculating the target Pareto optimal solutions of the two optimization targets to form a group of optimal solution sets;
s42, taking the optimal solution set as a training set, a verification set and a test set of a deep learning algorithm;
s43, deep learning training and verification are carried out;
and S44, reasoning the test set by using the trained deep learning model to obtain a more optimal solution arrangement mode.
CN202111536202.4A 2021-12-16 2021-12-16 Temperature sensor optimal arrangement method based on DNA genetic algorithm Active CN113935556B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111536202.4A CN113935556B (en) 2021-12-16 2021-12-16 Temperature sensor optimal arrangement method based on DNA genetic algorithm

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111536202.4A CN113935556B (en) 2021-12-16 2021-12-16 Temperature sensor optimal arrangement method based on DNA genetic algorithm

Publications (2)

Publication Number Publication Date
CN113935556A CN113935556A (en) 2022-01-14
CN113935556B true CN113935556B (en) 2022-03-22

Family

ID=79289071

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111536202.4A Active CN113935556B (en) 2021-12-16 2021-12-16 Temperature sensor optimal arrangement method based on DNA genetic algorithm

Country Status (1)

Country Link
CN (1) CN113935556B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116992780B (en) * 2023-09-25 2024-01-02 中南大学 Temperature sensor arrangement method for digital electrolytic tank

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6006604A (en) * 1997-12-23 1999-12-28 Simmonds Precision Products, Inc. Probe placement using genetic algorithm analysis
CN107391789A (en) * 2017-06-12 2017-11-24 西安理工大学 Silicon melt reconstructing temperature field method based on free surface measured temperature and characteristic function interpolation
CN111368970A (en) * 2020-02-17 2020-07-03 哈尔滨工业大学 Sensor optimal arrangement method based on deep reinforcement learning
CN112989529A (en) * 2021-02-05 2021-06-18 河北农业大学 Henhouse temperature sensor arrangement optimization method based on genetic algorithm

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106875071A (en) * 2017-03-31 2017-06-20 华南理工大学 A kind of workshop appliance multiple-objection optimization layout method of self-adapted genetic algorithm
CN112181867B (en) * 2020-09-29 2022-07-26 西安电子科技大学 On-chip network memory controller layout method based on multi-target genetic algorithm

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6006604A (en) * 1997-12-23 1999-12-28 Simmonds Precision Products, Inc. Probe placement using genetic algorithm analysis
CN107391789A (en) * 2017-06-12 2017-11-24 西安理工大学 Silicon melt reconstructing temperature field method based on free surface measured temperature and characteristic function interpolation
CN111368970A (en) * 2020-02-17 2020-07-03 哈尔滨工业大学 Sensor optimal arrangement method based on deep reinforcement learning
CN112989529A (en) * 2021-02-05 2021-06-18 河北农业大学 Henhouse temperature sensor arrangement optimization method based on genetic algorithm

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
基于多目标遗传算法的传感器优化布点研究;张连振等;《工程力学》;20070430;第24卷(第4期);第168-173页 *
基于改进DNA遗传算法的微电网DG选址定容研究;宋雷震等;《黑龙江工业学院学报》;20201031;第20卷(第10期);第81-87页 *
水稻浸种催芽箱温度传感器优化配置——基于遗传算的发;李纯思等;《农机化研究》;20170630(第6期);第27-32页 *

Also Published As

Publication number Publication date
CN113935556A (en) 2022-01-14

Similar Documents

Publication Publication Date Title
CN103077288B (en) Towards hard measurement and the formula decision-making technique thereof of the multicomponent alloy material of small sample test figure
CN109684714B (en) Building design method based on machine learning and BIM technology
CN104636985A (en) Method for predicting radio disturbance of electric transmission line by using improved BP (back propagation) neural network
CN108564205A (en) A kind of load model and parameter identification optimization method based on measured data
CN110232461A (en) More interconnection vector machine water quality prediction methods based on quantum genetic algorithm optimization
CN110083125A (en) A kind of machine tool thermal error modeling method based on deep learning
CN108805346A (en) A kind of hot continuous rolling force forecasting method based on more hidden layer extreme learning machines
Yan Research on green suppliers' evaluation based on AHP & genetic algorithm
CN113935556B (en) Temperature sensor optimal arrangement method based on DNA genetic algorithm
CN103530700B (en) Urban distribution network saturation loading Comprehensive Prediction Method
CN103473465B (en) Land resource spatial configuration optimal method based on multiple target artificial immune system
Liang et al. An enhanced slime mould algorithm and its application for digital IIR filter design
CN113591368A (en) Comprehensive energy system multi-energy load prediction method and system
CN115310348A (en) Stacking-based grouting amount integrated agent prediction model and prediction method
CN106295877B (en) Method for predicting electric energy consumption of smart power grid
CN109408896B (en) Multi-element intelligent real-time monitoring method for anaerobic sewage treatment gas production
Fan Research on deep learning energy consumption prediction based on generating confrontation network
Ping et al. A mixed isomorphism approach for kinematic structure enumeration graphs based on intelligent design and manufacturing
CN110033118A (en) Elastomeric network modeling and the blower multiobjective optimization control method based on genetic algorithm
CN111126827A (en) Input-output accounting model construction method based on BP artificial neural network
CN113762591B (en) Short-term electric quantity prediction method and system based on GRU and multi-core SVM countermeasure learning
CN103778327A (en) Curve fitting method
Du et al. A modified niche genetic algorithm based on evolution gradient and its simulation analysis
Gu Innovative Sensor Design Method of Industrial Ceramic Product Modeling Based on Immune Optimization Algorithm
Lu et al. Application of GA optimized wavelet neural networks for carrying capacity of water resources prediction

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