CN111447627B - WSN node positioning method based on differential evolution genetic algorithm - Google Patents
WSN node positioning method based on differential evolution genetic algorithm Download PDFInfo
- Publication number
- CN111447627B CN111447627B CN202010180046.1A CN202010180046A CN111447627B CN 111447627 B CN111447627 B CN 111447627B CN 202010180046 A CN202010180046 A CN 202010180046A CN 111447627 B CN111447627 B CN 111447627B
- Authority
- CN
- China
- Prior art keywords
- vector
- positioning
- steps
- formula
- population
- 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
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/22—Traffic simulation tools or models
- H04W16/225—Traffic simulation tools or models for indoor or short range network
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Feedback Control In General (AREA)
Abstract
The invention discloses a WSN node positioning method based on a differential evolution genetic algorithm. The method comprises the following steps: step 1, establishing a node positioning optimization model and designing a positioning optimization objective function; step 2, initializing a population; step 3, generating a variation vector by utilizing differential variation operation after the population is subjected to initialization assignment; step 4, after the mutation operation is carried out, a target vector and the mutation vector need to be subjected to binomial intersection to generate a final test vector; step 5, one-to-one tournament selection is executed between the test vector and the corresponding target vector; step 6, updating the individuals by using the success probability; and 7, repeating the steps 3 to 6, and outputting the optimal position value when the maximum iteration times are reached. On the basis of the basic GA algorithm, a differential evolution mechanism is provided, so that the iteration times and the positioning error of the algorithm are considered; the method can give consideration to both network real-time performance and high-precision positioning performance, and has effectiveness and practicability.
Description
Technical Field
The invention belongs to the field of node positioning in a wireless sensor network, and particularly relates to a WSN node positioning method using a differential evolution genetic algorithm.
Background
At present, algorithms for solving WSN network positioning are endless, and the existing algorithms can be divided into 2 types according to whether the algorithms measure the distance or not, and the positioning algorithms are based on ranging and do not need ranging. The node location algorithm based on ranging mainly includes received signal strength indication (rssi), time difference of arrival (TDOA), and angle of arrival (AOA). The positioning algorithm without distance measurement mainly includes a centroid algorithm, an (approximate point-in-three-alignment, APIT) algorithm, a (DV-Houpo optimization) DV-Hop algorithm and an Amorphous algorithm. The former is to measure the actual position of the distance estimation node by using some tools, and the distance is not required to be measured when the position of the positioning node is estimated without distance measurement, so that the positioning error based on the distance measurement is far smaller than the positioning algorithm of the node without the distance measurement. Since the former has higher positioning accuracy, the node positioning method based on ranging is mainly considered herein. In the method based on the distance measurement, the RSSI algorithm is widely used because it does not need an additional large amount of hardware and has high positioning accuracy. However, due to the existence of errors in the ranging, a plurality of experts and scholars model the positioning problem of the wireless sensor into an optimization problem for solving the minimum ranging error to reduce the errors, and an intelligent optimization algorithm is adopted to solve the optimization problem. However, single intelligent optimization such as genetic algorithm, particle swarm, differential evolution algorithm and the like often has the defects of poor global optimization capability, low iteration speed, high positioning error and the like, so that the research of an intelligent algorithm with low positioning error and high iteration speed is very important.
Disclosure of Invention
Aiming at the defects that a single intelligent algorithm is easy to fall into a local minimum value, low in optimizing speed, low in precision and the like, a difference optimization mechanism is provided on the basis of a basic genetic algorithm, so that the iteration rate and the positioning performance of the algorithm are considered.
The technical scheme adopted by the invention for solving the technical problem comprises the following steps:
step 1, establishing a node positioning optimization model and designing a positioning optimization objective function;
step 2, initializing a population;
step 3, generating a variation vector by utilizing differential variation operation after the population is subjected to initialized assignment;
step 4, after performing mutation operation, the target vector x needs to be transformed i,g And the variation vector v i,g Generating final test vector u by carrying out binomial intersection i,g ＝[u i1,g , u i2,g ,…,u iD,g ]；
Step 5, one-to-one tournament selection is executed between the test vectors and the corresponding target vectors;
step 6, updating the individuals by using the success probability;
and 7, repeating the steps 3 to 6, and outputting the optimal position value when the maximum iteration times are reached.
The invention has the beneficial effects that:
1. a WSN node positioning method based on a differential evolution genetic algorithm is provided. The method can give consideration to both network real-time performance and high-precision positioning performance, and has effectiveness and practicability.
2. On the basis of a basic GA algorithm, a differential evolution mechanism is provided, so that the iteration times and the positioning errors of the algorithm are considered.
Drawings
FIG. 1 is a schematic view of the present invention.
Detailed Description
The following describes the implementation steps of the present invention in further detail with reference to the attached drawings.
As shown in fig. 1, a method for positioning a WSN node based on a differential evolution genetic algorithm specifically includes the following steps:
step 1, establishing a node positioning optimization model and designing a positioning optimization objective function;
step 2, initializing a population;
step 3, generating a variation vector by utilizing differential variation operation after the population is subjected to initialization assignment;
step 4, after performing mutation operation, the target vector x needs to be transformed i,g And the variation vector v i,g Generating final test vector u by carrying out binomial intersection i,g ＝[u i1,g , u i2,g ,…,u iD,g ]；
Step 5, one-to-one tournament selection is executed between the test vector and the corresponding target vector;
step 6, updating the individuals by using the success probability;
and 7, repeating the steps 3 to 6, and outputting the optimal position value when the maximum iteration times are reached.
Establishing a node positioning optimization model and designing a positioning optimization objective function in the step 1, specifically, the following steps are performed:
setting anchor P 1 (x 1 ,y 1 ),P 2 (x 2 ,y 2 ),…,P n (x n ,y n ) And the actual true lengths of the unknown nodes P (x, y) are r 1 ,r 2 ,…,r n The difference of the distance measurement errors is epsilon 1 ,ε 2 ,…,ε n Then, | r is satisfied i -d i |＜ε i Where i =1,2, ..., n, then the unknown node P (x, y) constraint:
solving for (x, y), such that:
the node location problem is converted into an optimization problem for solving the minimum value of the ranging error.
The initialization population in step 2 is specifically as follows:
default values are given to the chromosome individuals within the limit range, function values Fitness (i) of all chromosomes in the total group are calculated according to a formula (2), and the individual p with the highest Fitness value in the current algebraic individuals is determined g And the position p with the highest personal cutoff algebraic fitness value h 。
After the population in the step 3 is subjected to initialization assignment, generating a variation vector v by utilizing differential variation operation i,g The method comprises the following steps:
v i,g ＝x r1,g +F*(x r2,g -x r3,g ) (3)
as shown in FIG. 1, in the formula (3), r 1 ，r 2 ，r 3 ∈[1,2,....NP]The scaling factor F is a constant in the interval of (0, 1) and is used for controlling the size of the differential vector; x is the number of r1,g 、x r2,g And x r3,g Are a vector randomly selected from the population.
After performing mutation operation as described in step 4, the target vector x is required to be transformed i,g And the variation vector v i,g Generating final test vector u by carrying out binomial intersection i,g ＝[u i1,g , u i2,g ,…,u iD,g ]The method comprises the following steps:
in the formula, j rand Is an integer randomly selected from the set {1,2, \ 8230;, D }, to ensure the variation vector v i,g At least one dimension of information is preserved; the crossover probability CR is a constant within the interval (0, 1).
Step 5, one-to-one tournament selection is performed between the test vector and the corresponding target vector, which specifically comprises the following steps:
for test vector u i,g And a target vector x i,g Comparing the target function values of the test vectors, and if the test vectors have more optimal target functions, replacing the target vectors with the test vectors; otherwise, the target vector remains unchanged; taking the objective function minimization as an example, the following formula is used for representation:
updating the individuals by using the success probability in the step 6 specifically comprises the following steps:
7-1, calculating chromosomes of each individual by using the formula (6);
x′ i ＝x i +N(0,σ) (6)
where N (0, σ) represents the mean value of 0, σ is a variable, and x i Denotes the individual before update, x' i Representing the updated individual;
7-2 updating the adaptive mutation probability P s
P s The updating method comprises the following steps:
P s ＝(1-β)P s +β (7)
wherein beta is-1/N;
7-3 update sigma, and three update modes are provided;
if P s Less than 1/3, update σ using equation (8) if P s If the ratio is more than 1/3, updating by using the formula (9), otherwise, keeping unchanged;
σ＝C*σ 0 (8)
σ＝C/σ 0 (9)。
and 7, repeating the steps 3 to 6, and outputting the optimal position value when the maximum iteration number is reached, wherein the method specifically comprises the following steps:
and (3) judging whether the maximum iteration number is reached, if the maximum iteration number is not reached, executing the step 3-step 6, guiding chromosome evolution by using the formula (2) as a fitness function, and obtaining a code with the maximum function value as the best estimation of the position when the maximum iteration number is reached.
Claims (2)
1. A WSN node positioning method based on a differential evolution genetic algorithm is characterized by comprising the following steps:
step 1, establishing a node positioning optimization model and designing a positioning optimization objective function;
step 2, initializing a population;
step 3, generating a variation vector by utilizing differential variation operation after the population is subjected to initialization assignment;
step 4, after performing mutation operation, the target vector x needs to be transformed i,g And the variation vector v i,g Generating final test vector u by carrying out binomial intersection i,g ＝[u i1,g ,u i2,g ,…,u iD,g ]；
Step 5, one-to-one tournament selection is executed between the test vector and the corresponding target vector;
step 6, updating the individuals by using the success probability;
step 7, repeating the steps 3 to 6, and outputting an optimal position value when the maximum iteration times are reached;
establishing a node positioning optimization model and designing a positioning optimization objective function in the step 1, specifically, the following steps are performed:
anchor node P 1 (x 1 ,y 1 ),P 2 (x 2 ,y 2 ),…,P n (x n ,y n ) And the actual true lengths of the unknown nodes P (x, y) are r 1 ,r 2 ,…,r n The difference of the distance measurement errors is epsilon 1 ,ε 2 ,…,ε n Then | r is satisfied i -d i |＜ε i Where i =1,2, \8230, n, then unknown node P (x, y) constraints:
(x, y) is solved such that:
converting the node positioning problem into an optimization problem for solving the minimum value of the ranging error, and taking a formula (2) as a fitness function;
the initialization population in step 2 is specifically as follows:
assigning default values to the chromosome individuals within the limit range, calculating function values Fitness (i) of all chromosomes in the total group according to a formula (2), and determining the individual p with the highest Fitness value in the current algebraic individuals g And the position p with the highest individual cutoff algebraic fitness value h ；
After the population stated in step 3 is subjected to initialization assignment, generating a variation vector v by utilizing differential variation operation i,g The method comprises the following steps:
v i,g ＝x r1,g +F*(x r2,g -x r3,g ) (3)
in the formula (3), r 1 ，r 2 ，r 3∈[1,2,…NP] The scaling factor F is a constant in the interval of (0, 1) and is used for controlling the size of the differential vector; x is the number of r1,g 、x r2,g And x r3,g All are a vector randomly selected from the population; after performing mutation operation as described in step 4, the target vector x is required to be transformed i,g And the variation vector v i,g Generating final test vector u by carrying out binomial intersection i,g ＝[u i1,g ,u i2,g ,…,u iD,g ]The method comprises the following steps:
in the formula, j rand Is randomly selected from the set {1,2, ..., D }An integer is selected to ensure the variation vector v i,g At least one dimension of information is preserved; the crossover probability CR is a constant within the interval (0, 1);
step 5, one-to-one tournament selection is performed between the test vectors and the corresponding target vectors, which is specifically as follows:
for test vector u i,g And a target vector x i,g Respectively solving the fitness values by using a formula (2) for comparison, and replacing the target vector with the test vector if the test vector has a better value; otherwise, the target vector remains unchanged; expressed by the following formula:
updating the individuals by using the success probability in the step 6 specifically comprises the following steps:
7-1, calculating chromosomes of each individual by using the formula (6);
x′ i ＝x i +N′(0,σ) (6)
where N' (0, σ) represents mean 0, σ as variable, x i Denotes the individual before update, x' i Representing the updated individual;
7-2 updating the adaptive mutation probability P s
P s The updating method comprises the following steps:
P s ＝(1-β)P s +β (7)
wherein beta is-1/N;
7-3 update sigma, and three update modes are provided;
if P s Less than 1/3, update σ using equation (8) if P s If the ratio is more than 1/3, updating by using a formula (9), otherwise, keeping unchanged;
σ＝C*σ 0 (8)
σ＝C/σ 0 (9)。
2. the method for positioning a WSN node based on a differential evolution genetic algorithm as claimed in claim 1, wherein the step 7 is repeated from step 3 to step 6, and when the maximum number of iterations is reached, the optimal position value is output, specifically as follows:
and (3) judging whether the maximum iteration number is reached, if the maximum iteration number is not reached, executing the step 3-step 6, guiding chromosome evolution by using the formula (2) as a fitness function, and obtaining a code with the maximum function value as the best estimation of the position when the maximum iteration number is reached.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010180046.1A CN111447627B (en) | 2020-03-16 | 2020-03-16 | WSN node positioning method based on differential evolution genetic algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010180046.1A CN111447627B (en) | 2020-03-16 | 2020-03-16 | WSN node positioning method based on differential evolution genetic algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111447627A CN111447627A (en) | 2020-07-24 |
| CN111447627B true CN111447627B (en) | 2023-04-18 |
Family
ID=71648700
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010180046.1A Active CN111447627B (en) | 2020-03-16 | 2020-03-16 | WSN node positioning method based on differential evolution genetic algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111447627B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115372903B (en) * | 2022-07-28 | 2024-03-08 | 国网江苏省电力有限公司电力科学研究院 | Near-ultrasonic-based long and narrow pipeline personnel positioning method and device |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB201717125D0 (en) * | 2016-11-28 | 2017-11-29 | National Univ Of Defense Technology | Differential evolution method oriented to agile satellite multi-target task planning |
| CN109379780A (en) * | 2018-10-23 | 2019-02-22 | 华南理工大学 | Wireless sensor network locating method based on adaptive differential evolution algorithm |
| CN109461471A (en) * | 2018-08-30 | 2019-03-12 | 浙江工业大学 | A kind of adaptive Advances in protein structure prediction based on championship mechanism |
| CN109640261A (en) * | 2018-11-22 | 2019-04-16 | 天津理工大学 | A kind of location algorithm of the heterogeneous wireless sensor network based on support vector regression |
| CN109688537A (en) * | 2018-12-21 | 2019-04-26 | 延安大学 | Forest fire monitoring Internet of things node localization method based on differential evolution algorithm |
| CN110839245A (en) * | 2019-10-31 | 2020-02-25 | 杭州电子科技大学 | Wireless sensor network node deployment method applied to indoor positioning |
-
2020
- 2020-03-16 CN CN202010180046.1A patent/CN111447627B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB201717125D0 (en) * | 2016-11-28 | 2017-11-29 | National Univ Of Defense Technology | Differential evolution method oriented to agile satellite multi-target task planning |
| CN109461471A (en) * | 2018-08-30 | 2019-03-12 | 浙江工业大学 | A kind of adaptive Advances in protein structure prediction based on championship mechanism |
| CN109379780A (en) * | 2018-10-23 | 2019-02-22 | 华南理工大学 | Wireless sensor network locating method based on adaptive differential evolution algorithm |
| CN109640261A (en) * | 2018-11-22 | 2019-04-16 | 天津理工大学 | A kind of location algorithm of the heterogeneous wireless sensor network based on support vector regression |
| CN109688537A (en) * | 2018-12-21 | 2019-04-26 | 延安大学 | Forest fire monitoring Internet of things node localization method based on differential evolution algorithm |
| CN110839245A (en) * | 2019-10-31 | 2020-02-25 | 杭州电子科技大学 | Wireless sensor network node deployment method applied to indoor positioning |
Non-Patent Citations (1)
| Title |
|---|
| 郑建国等.混合粒子群和差分进化的定位算法.计算机测量与控制.2019,第第27卷卷(第第27卷期),192-195. * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111447627A (en) | 2020-07-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109597864B (en) | Method and system for real-time positioning and map construction of ellipsoid boundary Kalman filtering | |
| CN109581281B (en) | Moving target positioning method based on arrival time difference and arrival frequency difference | |
| CN109379780B (en) | Wireless sensor network positioning method based on self-adaptive differential evolution algorithm | |
| CN105203106A (en) | WMPS network layout optimizing method based on simulated annealing particle swarm optimization | |
| CN110225454B (en) | Confidence transfer distributed type volume Kalman filtering cooperative positioning method | |
| CN109039720B (en) | Parameter estimation method for binary sensor network | |
| CN111447627B (en) | WSN node positioning method based on differential evolution genetic algorithm | |
| CN113326912A (en) | Information sharing Harris eagle optimization-based ultra-wideband positioning method | |
| CN101917762A (en) | Node positioning method of particle swarm optimization sensor with penalty function | |
| CN115907304A (en) | Evaluation index weight optimization method based on inter-zone analytic method | |
| Wang et al. | Research on hybrid PSODE with three populations based on multiple differential evolutionary models | |
| CN115775038A (en) | Short-term load prediction method based on IGWO optimization LSSVM | |
| CN109253727B (en) | Positioning method based on improved iteration volume particle filter algorithm | |
| CN113993152A (en) | Communication base station flow prediction method based on improved Husky algorithm optimization support vector machine regression machine | |
| CN113432608A (en) | Generalized high-order CKF algorithm based on maximum correlation entropy and suitable for INS/CNS integrated navigation system | |
| CN112529328A (en) | Product performance prediction method and system | |
| CN115550837B (en) | DV-Hop positioning method based on chaotic mapping and gray wolf algorithm optimization | |
| CN116865255A (en) | Short-term wind power prediction method based on improved entropy weight method and SECEEMD | |
| CN111259340A (en) | Saturated load prediction method based on logistic regression | |
| CN113361146B (en) | Improved particle swarm optimization-based manganese-copper shunt structure parameter optimization method | |
| Chen et al. | Improved DV-Hop Node location Optimization Algorithm Based on Adaptive Particle Swarm | |
| CN114637956A (en) | Novel double-Kalman filtering method | |
| CN109753695B (en) | Method for expanding characteristics of engine parts | |
| CN106961656B (en) | Wireless sensor network data prediction method | |
| CN112037289A (en) | Off-line parameter calibration method based on genetic algorithm |
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 |