CN113630737A - Deployment method of mobile charger in wireless chargeable sensor network - Google Patents

Deployment method of mobile charger in wireless chargeable sensor network Download PDF

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CN113630737A
CN113630737A CN202110889666.7A CN202110889666A CN113630737A CN 113630737 A CN113630737 A CN 113630737A CN 202110889666 A CN202110889666 A CN 202110889666A CN 113630737 A CN113630737 A CN 113630737A
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mobile charger
sensor network
probability
individual
path
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胡昊楠
张恩铭
钱彤途
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Xidian University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/38—Services specially adapted for particular environments, situations or purposes for collecting sensor information
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • 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
    • H04W16/18—Network planning tools
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/24—Connectivity information management, e.g. connectivity discovery or connectivity update
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Abstract

The invention discloses a deployment method of a mobile charger in a wireless chargeable sensor network, which relates to the technical field of mobile charger deployment and comprises the following specific steps: obtaining basic data of a mobile charger and a node; constructing a weighted path graph of the sensor network nodes; searching a Hamiltonian graph with the minimum total weight; executing an optimized mobile charger number and path planning algorithm; whether the optimal solution set of the number of paths meets the balance of each path or not; the mobile charger obtains the lowest threshold allowed by the sensor; obtaining a final deployment scheme of the mobile charger; the invention has fast overall operation speed and good convergence effect; the efficiency is improved by balancing each path of each mobile charger; the charging path of the mobile charger is reasonably planned and deployed, so that the energy loss of the mobile charger on the road is effectively reduced, and the method has wide application value.

Description

Deployment method of mobile charger in wireless chargeable sensor network
Technical Field
The invention relates to the technical field of mobile charger deployment, in particular to a deployment method of a mobile charger in a wireless chargeable sensor network.
Background
The wireless sensor network is a distributed sensor network, the wireless sensor network comprises a plurality of sensors and a data center, and the novel network model is called as a wireless chargeable sensor network: the sensors collect information from the environment and send the collected information to the data center at intervals. The data center analyzes the data and returns control information, the sensor continuously consumes energy in the process, and the minimization of energy consumption has important significance on the service life and the service performance of the network; on the basis of the background that a mobile charging vehicle maintenance system in a wireless chargeable network normally works, the route planning and optimization of the mobile charging vehicle in the working process are discussed, starting from the optimal path planning, the problem is converted into a traveler problem and a multi-traveler problem to establish a mathematical model, an approximate optimal solution is planned by combining a simulated annealing algorithm, a graph theory and a genetic algorithm, and finally an equation set is listed through a numerical calculation method, and the minimum battery capacity meeting the network work is obtained according to a prediction result;
the existing method for deploying the mobile charger in the wireless rechargeable sensor network has the advantages that the charging work is completed by periodically dispatching the mobile charger, the excellent performance is realized in a non-scale network, but the energy consumption is high, a single mobile charger cannot meet the charging requirement of a large-scale network, the energy consumption is high, energy is not saved, and the charging efficiency is unstable.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides a deployment method of a mobile charger in a wireless chargeable sensor network.
In order to achieve the purpose, the invention adopts the following technical scheme:
a deployment method of a mobile charger in a wireless chargeable sensor network comprises the following specific steps:
the method comprises the following steps: obtaining basic data of a mobile charger and a node;
step two: constructing a weighted path graph of the sensor network nodes;
step three: searching a Hamiltonian graph with the minimum total weight;
step four: executing an optimized mobile charger number and path planning algorithm;
step five: whether the optimal solution set of the number of paths meets the balance of each path or not;
step six: the mobile charger obtains the lowest threshold allowed by the sensor;
step seven: and obtaining a final deployment scheme of the mobile charger.
As a further scheme of the invention: the path planning algorithm is specifically a genetic algorithm, and the specific steps are as follows:
A. firstly, initially generating a coding individual;
B. calculating an objective function for each individual;
C. selecting N individuals as next generation variant objects by using a wheel disc method;
D. carrying out cyclic variation and cross selection on the selected individuals according to probability to generate a new generation group;
E. comparing the existing records, and if the existing records are better than the existing records, recording the optimal individuals in the group;
and D, repeating the step B, converging to an approximately optimal solution after enough generations are obtained through inheritance, and ending the algorithm.
As a further scheme of the invention: the genetic algorithm is embodied in the following aspects:
(1) coding design of genetic individuals;
(2) an objective function of a genetic algorithm;
(3) selecting a wheel disc;
(4) partial match crossover and crossover variations.
As a further scheme of the invention: the coding design of the genetic individuals in the step (1) is mainly embodied in the following two aspects: the code design of the traveler problem and the code design of the multi-traveler problem;
the code design process of the traveler problem comprises the following specific steps: in genetic algorithms, it is necessary to embody the genetic characteristics of an individual, and therefore the design codes are as follows:
n positions to be reached in each network are set as A, the shortest distance matrix of the n positions is set as A, the number of rows and columns 1,2, 3, …,29 and … corresponding to the matrix A respectively represents the n positions, and the center point 1 represents the data center of the mobile charger; second, keeping the n positions with the data center at the first position, in order, represents one result: such as 30 positions:
1,28,23,27,16,3,15,26,10,25,22,13,21,24,8,5,12,30,9,4,19,2,7,6,20,29,11,14,17,18,1；
the path is represented as: 1-28-23-27-16-3-15-26-10-25-22-13-21-24-8-5-12-30-9-4-19-2-7-6-20-29-11-14-17-18-1;
the specific process of coding design of the multi-traveler problem is as follows: if there are m routes and there are n positions to be reached, then m-1 virtual points n +1, n +2, …, n + m-1 are inserted to represent the starting point of the route and form a new code, which can be used to represent the chromosome of the multi-traveler problem:
by setting the distances between the nodes 1, n +1, n +2, … and n + m-1 to be infinite, the distances to other points are consistent with 1 node, a new shortest distance matrix is obtained, and the corresponding row number and column number respectively represent n + m-1 nodes.
As a further scheme of the invention: the objective function in step (2) is considered from the following four aspects:
a. the total route is shortest: is the sum of the distances of the m route lines, i.e. is
Figure BDA0003195331470000041
Wherein liIndicating the length of the ith line;
b. and (3) balance degree: j ═ max (l)1，l2，l3，...，lm) (ii) a Namely, the longest one of the four lines is as short as possible, and compared with the traditional definition mode, the definition mode has the following characteristics:
i) dimension problem is easy to process
For the equilibrium degree of the definition, the method is closer to the total distance on the aspect of dimension problems compared with the traditional definition mode, and the processing is simpler;
ii) compliance with all the definition requirements
In fact, as long as the longest route can be kept shortest, the difference between the lengths of the four lines is very small, and therefore the definition of the equilibrium degree is met;
iii) the definition is very simple, and the operation amount is greatly reduced
c. Comprehensively considering the target function under the total distance and the equilibrium degree:
first unify the dimensions, S is the same as mJ, and by making J' ═ mJ, the total objective function is obtained as:
Z＝aS+bJ′，
a+b＝1；
wherein a and b are the weights of S and J', respectively,
when a is larger than b, the weight of the total distance is large, and the total distance accounts for the main factor;
when a is less than b, the weight of the balance degree is great, and the balance degree is a main factor at the moment;
in order to make the total distance and the degree of influence of the equilibrium degree on the total target the same, the weight of the total distance and the degree of influence of the equilibrium degree on the total target can be set to be 1: 1, then the objective function is obtained:
Figure BDA0003195331470000051
d、lidetermination of (1):
the matrix set is obtained by encoding of genetic algorithm
Figure BDA0003195331470000052
Wherein the content of the first and second substances,
Figure BDA0003195331470000053
obtaining:
Figure BDA0003195331470000054
Figure BDA0003195331470000055
Figure BDA0003195331470000056
…,
Figure BDA0003195331470000057
as a further scheme of the invention: the specific process of selecting the wheel disc in the step (3) is as follows: for the selection of the next generation population, the probability of each individual to be selected is firstly calculated, N populations of each generation are assumed, and the objective function value of the ith individual is set to be ZiThe smaller the required objective function value, the greater the probability of being selected:
by introducing the definition of the fitness function, Si＝1/ZiThe reciprocal of the objective function value is obtained; thereby defining the probability of the ith individual being selected as:
Figure BDA0003195331470000061
at this time, the process of the present invention,
Figure BDA0003195331470000062
it is apparent that the smaller the target value, the greater the probability of being selected.
The selected probability may also be defined as:
Figure BDA0003195331470000063
in the defined probabilities, if the N individual selection probabilities are not very different, the number of cycles will be many, and if the selection probabilities are very different, the number of cycles will decrease and will converge rapidly, so that P is obtainedmax-PminAs large as possible, thusThe following comparisons can be made:
Figure BDA0003195331470000064
Figure BDA0003195331470000065
obviously, the value of formula (I) is greater and more preferable than that of formula (II), so that P is preferablei＝Pi 1For other definition modes, the comparison analysis can be carried out;
then, the cumulative probability is calculated for each probability:
Figure BDA0003195331470000066
using a wheel disc selection method, generating a random number of 0-1 in each round, selecting an individual by using the random number as a pointer, and selecting an individual of the next generation;
as a further scheme of the invention: the processes of partial matching crossing and exchange variation in the step (4) are respectively as follows:
s1, partial matching crossover operator: the partial matching intersection operation requires that two intersections are randomly selected so as to determine a matching segment, and two children are generated according to a mapping relation given by a middle segment between the two intersections in two parents, which is specifically as follows:
two cross points "|" are randomly selected for the following representation of two parents:
P1：(1 2 3|4 5 6 7|8 9)
P2：(4 5 2|1 8 7 6|9 3)
first, the middle segment between two intersections is swapped, resulting in:
Q1：(x x x|1 8 7 6|x x)
Q2：(x x x|4 5 6 7|x x)
wherein, x represents a temporary undefined code, and the mapping relation of the middle section is obtained as follows:
Figure BDA0003195331470000071
the unselected city codes 2,3, 9 are then retained, thus yielding:
Q1：(x 2 3|1 8 7 6|x 9)
Q2：(x x 2|4 5 6 7|9 3)
and for Q1The first x in (A) can be composed of
Figure BDA0003195331470000072
This is 4, others are in turn available, with the following results:
Q1：(4 2 3|1 8 7 6|5 9)
Q2：(1 8 2|4 5 6 7|9 3)；
s2, exchange mutation: that is, genes at two random positions are exchanged, inversion mutation is adopted, that is, two points are randomly selected on a chromosome, and a substring between the two points is completely inverted, specifically:
1，6，5，2，4，11，7，8，3，12，9，10
and (3) carrying out reverse order on the nodes 2 to 7 to obtain a new individual:
1，7，11，4，2，5，6，8，3，12，9，10
and obtaining an algorithm of path planning.
As a further scheme of the invention: the network medium limit value calculation formula of the deployment scheme in the step seven is as follows:
Figure BDA0003195331470000081
compared with the prior art, the invention has the beneficial effects that:
1. the overall operation speed is high, and the convergence effect is good;
2. the efficiency is improved by balancing each path of each mobile charger;
3. the charging path of the mobile charger is reasonably planned and deployed, so that the energy loss of the mobile charger on the road is effectively reduced, and the method has wide application value.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention.
Fig. 1 is an overall flowchart of a deployment method of a mobile charger in a wirelessly rechargeable sensor network according to the present invention;
fig. 2 is a schematic diagram of a location-energy consumption matrix in a deployment method of a mobile charger in a wirelessly rechargeable sensor network according to the present invention;
fig. 3 is a schematic diagram of a projection coordinate table of latitude in a deployment method of a mobile charger in a wireless rechargeable sensor network according to the present invention;
fig. 4 is a diagram showing a result of an N-point algorithm in a deployment method of a mobile charger in a wirelessly rechargeable sensor network according to the present invention;
fig. 5 is a planning diagram of a limit value in a deployment method of a mobile charger in a wirelessly rechargeable sensor network according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
Example 1
Referring to fig. 1 to 5, the present embodiment discloses a deployment method of a mobile charger in a wirelessly rechargeable sensor network, which includes the following specific steps:
obtaining basic data of a mobile charger and a node; constructing a weighted path graph of the sensor network nodes; searching a Hamiltonian graph with the minimum total weight; executing an optimized mobile charger number and path planning algorithm;
specifically, the path planning algorithm is a genetic algorithm, and specifically comprises the following steps:
firstly, initially generating a coding individual; calculating an objective function for each individual; selecting N individuals as next generation variant objects by using a wheel disc method; carrying out cyclic variation and cross selection on the selected individuals according to probability to generate a new generation group; comparing the existing records, and if the existing records are better than the existing records, recording the optimal individuals in the group;
repeatedly calculating the target function of each individual, converging to an approximate optimal solution after sufficient generations of inheritance, and ending the algorithm;
the present example further discloses that the genetic algorithm is embodied in the following aspects:
(1) coding design of genetic individuals;
specifically, the coding design of genetic individuals is mainly embodied by the following two aspects: the code design of the traveler problem and the code design of the multi-traveler problem;
the embodiment specifically discloses a code design process of a traveler problem as follows: in genetic algorithms, genetic characteristics of individuals need to be embodied, so that codes need to be designed:
it should be noted that there are n locations to be reached in each network, let a be the shortest distance matrix of the n locations, and the number of rows and columns 1,2, 3, …,29, … corresponding to the matrix a respectively represents the n locations, where point 1 represents the data center of the mobile charger; second, keeping the n positions with the data center at the first position, in order, represents one result: such as 30 positions: 1,28,23,27,16,3,15,26,10,25,22,13,21,24,8,5,12,30,9,4,19,2,7,6,20,29,11,14,17,18, 1;
the path is represented as: 1-28-23-27-16-3-15-26-10-25-22-13-21-24-8-5-12-30-9-4-19-2-7-6-20-29-11-14-17-18-1;
the specific process of coding design of the multi-traveler problem is as follows: if there are m routes and there are n positions to be reached, then m-1 virtual points n +1, n +2, …, n + m-1 are inserted to represent the starting point of the route and form a new code, which can be used to represent the chromosome of the multi-traveler problem:
by setting the distances among the nodes 1, n +1, n +2, … and n + m-1 to be infinite and the distances to other points to be consistent with 1 node, a new shortest distance matrix is obtained, and the corresponding row number and column number respectively represent n + m-1 nodes.
The genetic algorithm in the embodiment is a search algorithm which is based on natural selection and genetic theory and combines survival rules of fittest in the biological evolution process with a random information transformation mechanism of a same group of chromosomes, and the genetic algorithm encodes solution vectors to form an initial population, and then carries out parallel iteration by using operators such as mutation, cross recombination, natural selection and the like to obtain an optimized solution.
(2) Objective function of genetic algorithm
Specifically, the objective function is considered from the following four aspects:
a. the total route is shortest: is the sum of the distances of the m route lines, i.e. is
Figure BDA0003195331470000111
Wherein liIndicating the length of the ith line;
b. and (3) balance degree: j ═ max (l)1，l2，l3，...，lm) (ii) a Namely, the longest one of the four lines is as short as possible, and compared with the traditional definition mode, the definition mode has the following characteristics:
i) dimension problem is easy to process
For the equilibrium degree of the definition, the method is closer to the total distance on the aspect of dimension problems compared with the traditional definition mode, and the processing is simpler;
ii) compliance with all the definition requirements
In fact, as long as the longest route can be kept shortest, the difference between the lengths of the four lines is very small, and therefore the definition of the equilibrium degree is met;
iii) the definition is very simple, and the operation amount is greatly reduced;
the embodiment discloses the reason for introducing the equalization degree specifically as follows: under the condition of ensuring that the total route is shortest, the problem of multiple travelers that one route is too long and the load is too heavy, and one route is too short and even directly returns to the original point without passing any point is very likely to occur, so the multi-traveler problem has no meaning, the concept of the balance degree needs to be introduced, the meaning of the balance degree is to ensure that each route is kept balanced as much as possible, and the constraint function which can meet the condition can be defined as the balance degree.
c. Comprehensively considering the target function under the total distance and the equilibrium degree:
first unify the dimensions, S is the same as mJ, and by making J' ═ mJ, the total objective function is obtained as:
Z＝aS+bJ′，
a+b＝1；
wherein a and b are the weights of S and J', respectively,
when a is larger than b, the weight of the total distance is large, and the total distance accounts for the main factor;
when a is less than b, the weight of the balance degree is great, and the balance degree is a main factor at the moment;
in order to make the total distance and the degree of influence of the equilibrium degree on the total target the same, the weight of the total distance and the degree of influence of the equilibrium degree on the total target can be set to be 1: 1, and then an objective function is obtained:
Figure BDA0003195331470000121
d、lidetermination of (1):
the matrix set is obtained by encoding of genetic algorithm
Figure BDA0003195331470000122
Wherein the content of the first and second substances,
Figure BDA0003195331470000123
obtaining:
Figure BDA0003195331470000131
Figure BDA0003195331470000132
Figure BDA0003195331470000133
…，
Figure BDA0003195331470000134
whether the optimal solution set of the number of paths meets the balance of each path or not; the mobile charger obtains the lowest threshold allowed by the sensor; obtaining a final deployment scheme of the mobile charger;
specifically, the network medium limit value calculation formula of the deployment scheme is as follows:
Figure BDA0003195331470000135
the limit value planning can refer to fig. 5.
Example 2
Referring to fig. 1 to 4, this embodiment discloses a deployment method of a mobile charger in a wirelessly rechargeable sensor network, and the specific processes of roulette selection and partial matching intersection and exchange variation are mainly described in this embodiment except for the steps as described above:
specifically, the wheel disc selection process comprises the following steps: for the selection of the next generation population, the probability of each individual to be selected is firstly calculated, N populations of each generation are assumed, and the objective function value of the ith individual is set to be ZiThe smaller the required objective function value, the greater the probability of being selected:
by introducing the definition of the fitness function, Si＝1/ZiThe reciprocal of the objective function value is obtained; thereby defining the probability of the ith individual being selected as:
Figure BDA0003195331470000136
at this time, the process of the present invention,
Figure BDA0003195331470000141
it is apparent that the smaller the target value, the greater the probability of being selected.
In addition, the selection probability can be defined as:
Figure BDA0003195331470000142
in the defined probabilities, if the N individual selection probabilities are not very different, the number of cycles will be many, and if the selection probabilities are very different, the number of cycles will decrease and will converge rapidly, so that P is obtainedmax-PminAs large as possible, then the following can be compared:
Figure BDA0003195331470000143
Figure BDA0003195331470000144
obviously, the value of formula (I) is greater and more preferable than that of formula (II), so that P is preferablei＝Pi 1For other definition modes, the comparison analysis can be carried out;
then, the cumulative probability is calculated for each probability:
Figure BDA0003195331470000145
by using a wheel selection method, a random number of 0-1 is generated in each round, and the random number is used as a pointer to select an individual and select an individual of the next generation.
The embodiment further discloses that the processes of partial matching crossing and exchange mutation are respectively as follows:
s1, partial matching crossover operator: the partial matching intersection operation requires that two intersections are randomly selected so as to determine a matching segment, and two children are generated according to a mapping relation given by a middle segment between the two intersections in two parents, which is specifically as follows:
two cross points "|" are randomly selected for the following representation of two parents:
P1：(1 2 3|4 5 6 7|8 9)
P2：(4 5 2|1 8 7 6|9 3)
first, the middle segment between two intersections is swapped, resulting in:
Q1：(x x x|1 8 7 6|x x)
Q2：(x x x|4 5 6 7|x x)
wherein, x represents a temporary undefined code, and the mapping relation of the middle section is obtained as follows:
Figure BDA0003195331470000151
the unselected city codes 2,3, 9 are then retained, thus yielding:
Q1：(x 2 3|1 8 7 6|x 9)
Q2：(x x 2|4 5 6 7|9 3)
and for Q1The first x in (A) can be composed of
Figure BDA0003195331470000152
This is 4, others are in turn available, with the following results:
Q1：(4 2 3|1 8 7 6|5 9)
Q2：(1 8 2|4 5 6 7|9 3)；
s2, exchange mutation: that is, genes at two random positions are exchanged, inversion mutation is adopted, that is, two points are randomly selected on a chromosome, and a substring between the two points is completely inverted, specifically:
1，6，5，2，4，11，7，8，3，12，9，10
and (3) carrying out reverse order on the nodes 2 to 7 to obtain a new individual:
1，7，11，4，2，5，6，8，3，12，9，10
and obtaining an algorithm of path planning.
In the embodiment, the path planning algorithm can be replaced by any other relatively intelligent algorithm such as an ant colony algorithm, a greedy algorithm and the like, and only an extremely short path for traversal is searched.
In conclusion, the invention has the advantages of high overall operation speed and good convergence effect; the efficiency is improved by balancing each path of each mobile charger; the charging path of the mobile charger is reasonably planned and deployed, so that the energy loss of the mobile charger on the road is effectively reduced, and the method has wide application value.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be able to cover the technical scope of the present invention and the equivalent alternatives or modifications according to the technical solution and the inventive concept of the present invention within the technical scope of the present invention.

Claims (8)

1. A deployment method of a mobile charger in a wireless chargeable sensor network is characterized by comprising the following specific steps:
the method comprises the following steps: obtaining basic data of a mobile charger and a node;
step two: constructing a weighted path graph of the sensor network nodes;
step three: searching a Hamiltonian graph with the minimum total weight;
step four: executing an optimized mobile charger number and path planning algorithm;
step five: whether the optimal solution set of the number of paths meets the balance of each path or not;
step six: the mobile charger obtains the lowest threshold allowed by the sensor;
step seven: and obtaining a final deployment scheme of the mobile charger.
2. The deployment method of the mobile charger in the wirelessly chargeable sensor network according to claim 1, wherein the path planning algorithm of step four is specifically a genetic algorithm, and the specific steps are as follows:
A. firstly, initially generating a coding individual;
B. calculating an objective function for each individual;
C. selecting N individuals as next generation variant objects by using a wheel disc method;
D. carrying out cyclic variation and cross selection on the selected individuals according to probability to generate a new generation group;
E. comparing the existing records, and if the existing records are better than the existing records, recording the optimal individuals in the group;
and D, repeating the step B, converging to an approximately optimal solution after enough generations are obtained through inheritance, and ending the algorithm.
3. The method of claim 2, wherein the genetic algorithm is embodied as:
(1) coding design of genetic individuals;
(2) an objective function of a genetic algorithm;
(3) selecting a wheel disc;
(4) partial match crossover and crossover variations.
4. The deployment method of mobile chargers in a wirelessly chargeable sensor network according to claim 3, wherein the encoding design of the genetic individuals in step (1) is mainly embodied by the following two aspects: the code design of the traveler problem and the code design of the multi-traveler problem;
the code design process of the traveler problem comprises the following specific steps: in genetic algorithms, it is necessary to embody the genetic characteristics of an individual, and therefore the design codes are as follows:
n positions to be reached in each network are set as A, the shortest distance matrix of the n positions is set as A, the number of rows and columns 1,2, 3, …,29 and … corresponding to the matrix A respectively represents the n positions, and the center point 1 represents the data center of the mobile charger; second, keeping the n positions with the data center at the first position, in order, represents one result: such as 30 positions: 1,28,23,27,16,3,15,26,10,25,22,13,21,24,8,5,12,30,9,4,19,2,7,6,20,29,11,14,17,18, 1;
the path is represented as: 1-28-23-27-16-3-15-26-10-25-22-13-21-24-8-5-12-30-9-4-19-2-7-6-20-29-11-14-17-18-1;
the specific process of coding design of the multi-traveler problem is as follows: if there are m routes and there are n positions to be reached, then m-1 virtual points n +1, n +2, …, n + m-1 are inserted to represent the starting point of the route and form a new code, which can be used to represent the chromosome of the multi-traveler problem:
by setting the distances among the nodes 1, n +1, n +2, … and n + m-1 to be infinite and the distances to other points to be consistent with 1 node, a new shortest distance matrix is obtained, and the corresponding row number and column number respectively represent n + m-1 nodes.
5. The deployment method of mobile chargers in a wirelessly rechargeable sensor network according to claim 3, wherein the objective function in step (2) is considered from the following four aspects:
a. the total route is shortest: is the sum of the distances of the m route lines, i.e. is
Figure FDA0003195331460000031
Wherein liIndicating the length of the ith line;
b. and (3) balance degree: j ═ max (l)1,l2,l3,…,lm) (ii) a Namely, the longest one of the four lines is as short as possible, and compared with the traditional definition mode, the definition mode has the following characteristics:
i) dimension problem is easy to process
For the equilibrium degree of the definition, the method is closer to the total distance on the aspect of dimension problems compared with the traditional definition mode, and the processing is simpler;
ii) compliance with all the definition requirements
In fact, as long as the longest route can be kept shortest, the difference between the lengths of the four lines is very small, and therefore the definition of the equilibrium degree is met;
iii) the definition is very simple, and the operation amount is greatly reduced
c. Comprehensively considering the target function under the total distance and the equilibrium degree:
first unify the dimensions, S is the same as mJ, and by making J' ═ mJ, the total objective function is obtained as:
Z＝aS+bJ′，
a+b＝1；
wherein a and b are the weights of S and J', respectively,
when a is larger than b, the weight of the total distance is large, and the total distance accounts for the main factor;
when a is less than b, the weight of the balance degree is great, and the balance degree is a main factor at the moment;
in order to make the total distance and the degree of influence of the equilibrium degree on the total target the same, the weight of the total distance and the degree of influence of the equilibrium degree on the total target can be set to be 1: 1, then the objective function is obtained:
Figure FDA0003195331460000041
d、lidetermination of (1):
the matrix set is obtained by encoding of genetic algorithm
Figure FDA0003195331460000042
Wherein the content of the first and second substances,
Figure FDA0003195331460000043
obtaining:
Figure FDA0003195331460000044
Figure FDA0003195331460000045
Figure FDA0003195331460000046
…，
Figure FDA0003195331460000047
6. the deployment method of the mobile charger in the wirelessly chargeable sensor network according to claim 3, wherein the specific procedure of the roulette selection in the step (3) is as follows: for the selection of the next generation population, the probability of each individual to be selected is firstly calculated, N populations of each generation are assumed, and the objective function value of the ith individual is set to be ZiThe smaller the required objective function value, the greater the probability of being selected:
by introducing the definition of the fitness function, Si＝1/ZiThe reciprocal of the objective function value is obtained; thereby defining the probability of the ith individual being selected as:
Figure FDA0003195331460000051
at this time, the process of the present invention,
Figure FDA0003195331460000052
obviously, the condition that the smaller the target value is, the greater the selection probability is satisfied;
the selected probability may also be defined as:
Figure FDA0003195331460000053
within the defined probabilities, if the N individuals are selected with different probabilitiesIf the number of the loops is not large, the number of the loops is large, the selection probability is very different, the number of the loops is reduced, and the loops are converged rapidly, so that P is obtainedmax-PminAs large as possible, then the following can be compared:
Figure FDA0003195331460000054
Figure FDA0003195331460000055
obviously, the value of formula (I) is greater and more preferable than that of formula (II), so that P is preferablei＝Pi 1For other definition modes, the comparison analysis can be carried out;
then, the cumulative probability is calculated for each probability:
Figure FDA0003195331460000061
by using a wheel selection method, a random number of 0-1 is generated in each round, and the random number is used as a pointer to select an individual and select an individual of the next generation.
7. The method for deploying mobile chargers in a wirelessly chargeable sensor network according to claim 3, wherein the processes of the partial matching crossing and the exchange mutation in the step (4) are respectively as follows:
s1, partial matching crossover operator: the partial matching intersection operation requires that two intersections are randomly selected so as to determine a matching segment, and two children are generated according to a mapping relation given by a middle segment between the two intersections in two parents, which is specifically as follows:
two cross points "|" are randomly selected for the following representation of two parents:
P1：(1 2 3|4 5 6 7|8 9)
P2：(4 5 2|1 8 7 6|9 3)
first, the middle segment between two intersections is swapped, resulting in:
Q1：(x x x|1 8 7 6|x x)
Q2：(x x x|4 5 6 7|x x)
wherein, x represents a temporary undefined code, and the mapping relation of the middle section is obtained as follows:
Figure FDA0003195331460000062
the unselected city codes 2,3, 9 are then retained, thus yielding:
Q1：(x 2 3|1 8 7 6|x 9)
Q2：(x x 2|4 5 6 7|9 3)
and for Q1The first x in (A) can be composed of
Figure FDA0003195331460000063
This is 4, others are in turn available, with the following results:
Q1：(4 2 3|1 8 7 6|5 9)
Q2：(1 8 2|4 5 6 7|9 3)；
s2, exchange mutation: that is, genes at two random positions are exchanged, inversion mutation is adopted, that is, two points are randomly selected on a chromosome, and a substring between the two points is completely inverted, specifically:
1，6，5，2，4，11，7，8，3，12，9，10
and (3) carrying out reverse order on the nodes 2 to 7 to obtain a new individual:
1，7，11，4，2，5，6，8，3，12，9，10
and obtaining an algorithm of path planning.
8. The method according to claim 1, wherein the network medium limit calculation formula of the deployment scenario in step seven is as follows:
Figure FDA0003195331460000071
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