CN116233866A - Method and system for optimizing distribution control of wireless sensor - Google Patents
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Abstract
The invention relates to the technical field of electronic information, and discloses a cloth control optimization method and a cloth control optimization system of a wireless sensor. The distributed control optimization method comprises the following steps: constructing a network coverage model for a plurality of wireless sensors; and optimizing the network coverage model based on an improved chimpanzee optimization algorithm to obtain the positions of the plurality of wireless sensors under the maximum network coverage of the network coverage model, wherein the improved chimpanzee optimization algorithm is based on a dynamic weight strategy, and therefore the network model of the wireless sensors is optimized by adopting the improved chimpanzee optimization algorithm, so that the coverage rate of the wireless sensor network reaches the maximum under the limited number of sensors and the fixed model, and the flow pressure and the cost of a processing center are reduced.
Description
Technical Field
The invention relates to the technical field of electronic information, in particular to a cloth control optimization method and a cloth control optimization system of a wireless sensor.
Background
The wireless sensor plays the roles of health monitoring and information transmission in the construction and development of the intelligent power grid, and the dispatching, fault checking and other instructions of the power grid center are sent according to the information provided by the sensor. As the smart grid scale increases, the network structure becomes more and more complex, the number of equipment parameters increases exponentially, and huge sensor monitoring data can cause great flow pressure and cost to the data processing center. The selection of the wireless sensor node position directly affects the data processing efficiency, so that the wireless sensor control is an important research problem. In order to enhance the coverage rate of the wireless sensor network and reduce the cost of data processing, the wireless sensor network is generally optimized in the directions of routing protocols, node research and development, path planning, data aggregation and the like of the sensor.
The wireless sensor routing protocol and node research and development technology at the present stage can reduce the power consumption of the wireless sensor from the technical level and increase the network coverage rate, but the implementation is complex, the research cost investment is huge, and meanwhile, the safety problem and the service quality caused by the upgrading protocol are required to be considered. For the data aggregation technology, although the power consumption of the wireless sensor can be reduced to a high degree, the problems of node redundancy, low coverage rate and the like still exist. For the path planning technology, the path planning technology is often used together with an intelligent optimization algorithm, but the final result is not ideal due to the defects of the convergence speed and the optimizing precision of the intelligent optimization algorithm.
Disclosure of Invention
The invention aims to provide a wireless sensor distribution optimization method and a wireless sensor distribution optimization system, which aim to solve the problems of redundancy, low coverage rate and large overall power consumption of wireless sensor nodes in the current smart grid, and adopt an improved chimpanzee optimization algorithm to optimize a network model of the wireless sensor, so that the coverage rate of the wireless sensor network reaches the maximum value under the condition of limited sensor quantity and fixed model, and the flow pressure and the cost of a processing center are reduced.
In order to achieve the above object, an aspect of the present invention provides a method for optimizing a wireless sensor, the method comprising: constructing a network coverage model for a plurality of wireless sensors; and optimizing the network coverage model based on an improved chimpanzee optimization algorithm to obtain the positions of the plurality of wireless sensors at the maximum network coverage of the network coverage model, wherein the improved chimpanzee optimization algorithm is a chimpanzee optimization algorithm based on a dynamic weight strategy.
Preferably, the optimizing the network coverage model based on the improved chimpanzee optimization algorithm comprises: initializing a coordinate parameter set in the network coverage model to obtain a plurality of coordinate parameter sets; determining network coverage rate of a network coverage model formed by the coordinate parameter sets; iteratively optimizing the set of coordinate parameters in the network coverage model by adopting the improved chimpanzee optimization algorithm, and determining the network coverage rate of the network coverage model as an objective function of the improved chimpanzee optimization algorithm; and taking the iteratively optimized coordinate parameter set as the positions of the plurality of wireless sensors under the maximum network coverage rate.
Preferably, the iteratively optimizing the set of coordinate parameters in the network coverage model using the modified chimpanzee optimization algorithm and determining the network coverage of the network coverage model as an objective function of the modified chimpanzee optimization algorithm includes: determining initial position vectors of the first, second, third, fourth and other rank orangutans in the improved chimpanzee optimization algorithm, respectively, based on a set of coordinate parameters corresponding to network coverage rates of the network coverage models ranking the first, second, third, fourth and other ranks orangutans; determining a first round of iteratively updated position vectors for the first, second, third, fourth and other rank orangutans based on the initial position vectors for the first, second, third, fourth and other rank orangutans and the improved chimpanzee optimization algorithm; determining the position vectors of the first, second, third and fourth class orangutans and other class orangutans after the iterative updating of the second to the preset number of rounds based on the position vectors of the first, second, third and fourth class orangutans and the other class orangutans after the iterative updating of the first to the third rounds and the improved chimpanzee optimization algorithm; and determining the position vector of the first class gorilla after iterative updating of the first preset number of rounds as the coordinate parameter set after iterative optimization.
Preferably, the first is determinedtThe position vectors of the first-class gorilla, the second-class gorilla, the third-class gorilla, the fourth-class gorilla and other class gorilla after the round iteration update comprise: based on the firsttFirst and second convergence factor groups in round iteration, determining the firsttOptimizing path parameter groups in round iteration; chimpanzee-based optimization algorithm and the firsttDetermining the optimal path parameter group in round iterationtEach chimpanzee in the round of iterations is respectively relative to four position vectors of the first, second, third and fourth class orangutans; based on the four position vectors, dynamic weights and the tht-determining the position vector of each chimpanzee after 1 round of iterative updatingtBits for each chimpanzee in a round iterative updateSetting vectors to obtain the firsttA plurality of updated network coverage models in a round of iteration, wherein,tis a positive integer and the set of coordinate parameters of the updated network coverage model corresponds to the firsttA position vector for each chimpanzee in the iterative updating of the round; determining a network coverage rate of the plurality of updated network coverage models; network coverage and the first network coverage of the network coverage model currently ranked firstt-1 comparing the network coverage of the first ranked network coverage model in the iterative update round; and network coverage of the network coverage model at the first bit of the current rank is greater than the first bitt-in case of network coverage of the network coverage model ranked first in the iterative updating of 1 round, the network coverage of the network coverage model currently ranked first, second, third, fourth and other bits will correspond totThe chimpanzee position vector in the round iteration update is updated to be the firsttAnd iterating the position vectors of the updated first-class orangutan, second-class orangutan, third-class orangutan, fourth-class orangutan and other hierarchical orangutan in turn.
Preferably, the determining of the first
The optimizing path parameter group in the round iteration comprises: based on->
First convergence factor in round iteration +.>
Second Convergence factor group {>
The } and the following formula determine +.>
Optimizing path parameter group { about in round iteration>
}：
Wherein the second convergence factor set {
Each of the amounts in is a random number between 0 and 1.
Preferably, said determining four position vectors for each chimpanzee in the t-th round of iteration relative to the first, second, third and fourth rank gorillas, respectively, comprises: based on the first
Optimizing path parameter group { about in round iteration>
[ and the following formula, determine ]>
Each chimpanzee in a round of iterations is referenced to four position vectors X of the first, second, third and fourth rank gorillas, respectively 1 (t)、X 2 (t)、X 3 (t)、X 4 (t)：
wherein ,
、/>
、/>
、/>
respectively +.>
The positions of the first-class gorilla, the second-class gorilla, the third-class gorilla and the fourth-class gorilla after iterative updating in turn, and the ∈>
Is->
The position of each chimpanzee after iterative updating of the round, < >>
And->
Is a random number between 0 and 1, < >>
And->
Is a random number between 0 and 1, < >>
And->
Is a random number between 0 and 1, < >>
And->
A random number between 0 and 1m 1 、m 2 、m 3 、m 4 The first chaotic vector, the second chaotic vector, the third chaotic vector and the fourth chaotic vector are respectively.
Preferably, the determining of the first
The position vector of each chimpanzee in the round of iterative updating comprises: based on the four position vectors X 1 (t)、X 2 (t)、X 3 (t)、X 4 (t) Dynamic weight->
First, at-1 round iteration updated position vector per chimpanzee +.>
And the following formula, determining said ++>
Position vector for each chimpanzee in a round iterative update
：
wherein ,
Is the firstt-velocity vector in 1 round iteration, < ->
And->
Is a random number between 0 and 1, < >>
And->
Is a random number between 0 and 1, < >>
And->
A random number between 0 and 1
And->
Is a random number between 0 and 1,r 1 、r 2 、r 3 、r 4 are random numbers between 0 and 1.
Preferably, it is determined by the following formula
，
wherein ,
and->
Respectively->
Upper and lower limit values of>
, and maxIs a preset number.
Through the technical scheme, the network coverage model of a plurality of wireless sensors is creatively built firstly, then the network coverage model is optimized based on an improved chimpanzee optimization algorithm, so that the positions of the plurality of wireless sensors under the maximum network coverage rate of the network coverage model are obtained, the problems of redundancy, low coverage rate and high overall power consumption of wireless sensor nodes in a current smart grid are solved, the network model of the wireless sensors is optimized by adopting the improved chimpanzee optimization algorithm, the maximum coverage rate of the wireless sensor network under the limited number of sensors and the fixed model is achieved, and therefore the flow pressure and the cost of a processing center are reduced.
A second aspect of the present invention provides a deployment optimization system for a wireless sensor, the deployment optimization system comprising: construction means for constructing a network coverage model for a plurality of wireless sensors; and optimizing means for optimizing the network coverage model based on an improved chimpanzee optimization algorithm to obtain the positions of the plurality of wireless sensors at a maximum network coverage of the network coverage model, wherein the improved chimpanzee optimization algorithm is a chimpanzee optimization algorithm based on a dynamic weight strategy.
Specific details and benefits of the wireless sensor control optimization system provided in the embodiments of the present invention can be found in the above description of the wireless sensor control optimization method, and are not repeated here.
A third aspect of the present invention provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of optimizing the deployment of wireless sensors.
Additional features and advantages of embodiments of the invention will be set forth in the detailed description which follows.
Drawings
The accompanying drawings are included to provide a further understanding of embodiments 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, without limitation, the embodiments of the invention. In the drawings:
FIG. 1 is a flow chart of a method for optimizing the deployment of wireless sensors according to an embodiment of the present invention;
figure 2 is a flow chart of optimizing the network coverage model based on an improved chimpanzee optimization algorithm provided by an embodiment of the present invention;
FIG. 3 is a flow chart of iterative optimization of feature parameter sets in a depth forest model provided by an embodiment of the present invention;
fig. 4 is a flowchart of wireless sensor deployment optimization of a smart grid based on a PSO-ChOA algorithm according to an embodiment of the present invention.
Detailed Description
The following describes specific embodiments of the present invention in detail with reference to the drawings. It should be understood that the detailed description and specific examples, while indicating and illustrating the invention, are not intended to limit the invention.
Fig. 1 is a flowchart of a method for optimizing the control of a wireless sensor according to an embodiment of the present invention. As shown in fig. 1, the method for optimizing the control of the wireless sensor may include: step S101, constructing a network coverage model of a plurality of wireless sensors; and step S102, optimizing the network coverage model based on an improved chimpanzee optimization algorithm to obtain the positions of the plurality of wireless sensors under the maximum network coverage of the network coverage model. Wherein the improved chimpanzee optimization algorithm is a chimpanzee optimization algorithm based on a dynamic weight strategy.
The following explanation and explanation are made with respect to the above steps S101 to S102, respectively.
Step S101, constructing a network coverage model for a plurality of wireless sensors.
Is arranged in a regionAInternal sharing ofNEach sensor has the same sensing radius and the node parameters of each sensor are consistentr，ASensor set formed in areaS={s 1 ,s 2 ,…,s N First (V)iThe coordinates of the individual sensors are defined ass i =(x i ,y i ) Wherein any point coordinate is defined asp j = (x j ,y j ) Thens i To the point ofp j Is defined as:
nodes i For the targetp j Is defined as:
in the formula ,ris the perceived radius of the wireless sensor,r e is a reliability parameter measured by the wireless sensor node,λ 1 、λ 2 、β 1 、β 2 respectively the measured parameters related to the node characteristics,
Obtained according to the above formulas
Based on the above, each sensor node can be obtained by the following calculationp j The joint perceived radius of (2) is:
next, the region is divided intoADivided intom×nNetwork coverage of wireless sensor
Is defined as the ratio of the number of grids covered by the sensor network to the total number of grids,
network coverage of the wireless sensor represented by the above formula (1)
Namely, a network coverage model.
In this embodiment, an fitness function (as shown in formula (1)) of the comprehensive coverage key index is constructed according to the network coverage model.
Step S102, optimizing the network coverage model based on an improved chimpanzee optimization algorithm to obtain the positions of the plurality of wireless sensors under the maximum network coverage of the network coverage model.
As shown in fig. 2, the optimizing the network coverage model based on the improved chimpanzee optimization algorithm may include: step S201, initializing a coordinate parameter set in the network coverage model to obtain a plurality of coordinate parameter sets; step S202, determining the network coverage rate of a network coverage model formed by the coordinate parameter sets; step S203, adopting the improved chimpanzee optimization algorithm to carry out iterative optimization on the coordinate parameter set in the network coverage model, and determining the network coverage rate of the network coverage model as an objective function of the improved chimpanzee optimization algorithm; and step S204, taking the coordinate parameter set after iterative optimization as the positions of the wireless sensors under the maximum network coverage rate.
The respective steps in steps S201 to S204 are explained below.
Step S201, initializing a set of coordinate parameters in the network coverage model to obtain a plurality of sets of coordinate parameters.
And carrying out multiple assignment on the coordinate parameter sets in the network coverage model to obtain multiple coordinate parameter sets. Wherein each set of coordinate parameters corresponds to a set of sensorsS={s 1 ,s 2 ,…,s N }。
Step S202, determining the network coverage rate of the network coverage model formed by the coordinate parameter groups.
And inputting each coordinate parameter set into the network coverage model, and acquiring the network coverage rate of the network coverage model with the corresponding coordinate parameter set.
Step S203, performing iterative optimization on the coordinate parameter set in the network coverage model by using the improved chimpanzee optimization algorithm, and determining the network coverage rate of the network coverage model as an objective function of the improved chimpanzee optimization algorithm.
As shown in fig. 3, the iteratively optimizing the set of coordinate parameters in the network coverage model using the modified chimpanzee optimization algorithm and determining the network coverage of the network coverage model as an objective function of the modified chimpanzee optimization algorithm may include: step S301, determining initial position vectors of the first, second, third, fourth and other class orangutans in the improved chimpanzee optimization algorithm, respectively, based on the set of coordinate parameters corresponding to the network coverage rates of the network coverage models ranking the first, second, third, fourth and other bits; step S302, determining the position vectors of the first-order iteratively updated first-order orangutan, the second-order orangutan, the third-order orangutan, the fourth-order orangutan and other-order orangutan based on the initial position vectors of the first-order orangutan, the second-order orangutan, the third-order orangutan, the fourth-order orangutan and the other-order orangutan and the improved chimpanzee optimization algorithm; step S303, determining the position vectors of the first class of orangutan, the second class of orangutan, the third class of orangutan, the fourth class of orangutan and other classes of orangutan after the iterative updating of the second round to the preset number of rounds based on the position vectors of the first class of orangutan, the second class of orangutan, the third class of orangutan, the fourth class of orangutan and the other classes of orangutan after the iterative updating of the first round to the preset number of rounds and the improved chimpanzee optimization algorithm; and step S304, determining the position vector of the first class gorilla after iterative updating of the first preset number of rounds as the coordinate parameter set after iterative optimization.
The respective steps in steps S301 to S304 are explained below.
Step S301, determining initial position vectors of the first, second, third, fourth and other rank orangutans in the improved chimpanzee optimization algorithm, respectively, based on the set of coordinate parameters corresponding to the network coverage rates of the network coverage models ranking the first, second, third, fourth and other respective ranks.
After acquiring a plurality of coordinate parameter sets of the network coverage model and network coverage rates of the plurality of network coverage models through steps S201 and S202, a network coverage rate of the top three network coverage models and three coordinate parameter sets corresponding to the three network coverage rates are selected therefrom. Then, determining a set of coordinate parameters corresponding to the network coverage of the first ranked network coverage model as two components of the initial position vector of the first ranked gorilla; determining a set of coordinate parameters corresponding to the network coverage of the network coverage model ranked second-order as two components of the initial position vector of the second-order gorilla; a set of coordinate parameters corresponding to the network coverage of the third ranked network coverage model is determined as two components of the initial position vector of the third ranked gorilla. Similarly, respective sets of coordinate parameters corresponding to network coverage of the network coverage models of the other bits may be selected therefrom, and then determined as two components of the initial position vectors of the other rank gorillas.
Step S302, determining the position vectors of the first, second, third, fourth and other class orangutans after the first round of iterative updating based on the initial position vectors of the first, second, third, fourth and other class orangutans and the improved chimpanzee optimization algorithm.
After the chimpanzee position is updated by adopting a dynamic weight strategy method, comparing the optimal values of the chimpanzee positions, finding the position of the chimpanzee with the current first ranking (best), the second ranking and the third ranking of the network coverage rate (namely the fitness value), and assigning the position to X 1 (t)、X 2 (t)、X 3 (t)、X 4 (t) This step is called lateral optimization. And (3) performing transverse multidimensional optimization on the chimpanzee population, comparing fitness values of the chimpanzees in the parent of the chimpanzee population, and sequentially comparing fitness values of the chimpanzees in the offspring (namely, comparing fitness values of the chimpanzees in each iteration round) to obtain a forward candidate optimal solution in a transverse search space.
For step S302 (or step S303 described below), the first is determinedtThe iteratively updated round of position vectors of the first, second, third, fourth and other rank orangutans may comprise: based on the firsttFirst and second convergence factor groups in round iteration, determining the firsttOptimizing path parameter groups in round iteration; chimpanzee-based optimization algorithm and the firsttDetermining the optimal path parameter group in round iterationtEach chimpanzee in the round of iterations is respectively relative to four position vectors of the first, second, third and fourth class orangutans; based on the four position vectors, dynamic weights and the tht-determining the position vector of each chimpanzee after 1 round of iterative updatingtThe position vector of each chimpanzee in the iterative updating of the round to obtain the thtA plurality of updated network coverage models in a round of iteration, wherein,tis a positive integer and the set of coordinate parameters of the updated network coverage model corresponds to the firsttA position vector for each chimpanzee in the iterative updating of the round; determining network coverage of the plurality of updated network coverage modelsA rate; network coverage and the first network coverage of the network coverage model currently ranked firstt-1 comparing the network coverage of the first ranked network coverage model in the iterative update round; and network coverage of the network coverage model at the first bit of the current rank is greater than the first bitt-in case of network coverage of the network coverage model ranked first in the iterative updating of 1 round, the network coverage of the network coverage model currently ranked first, second, third, fourth and other bits will correspond totThe chimpanzee position vector in the round iteration update is updated to be the firsttAnd iterating the position vectors of the updated first-class orangutan, second-class orangutan, third-class orangutan, fourth-class orangutan and other hierarchical orangutan in turn.
The following are directed to determining the firsttThe steps in the position vector of each chimpanzee after the iterative update of the round are illustrated.
First, the determination is madetThe optimizing path parameter group in the round iteration comprises: based on the firsttFirst convergence factor in round iteration
Second Convergence factor group {>
The following formula is used for determining the firsttOptimizing path parameter group { about in round iteration>
}：
Wherein the second convergence factor set {
Each of the amounts in is a random number between 0 and 1.
Wherein the first convergence factor
Along with ittThe proportional relationship changes. For example, the first convergence factor +.Can be determined by the following formula>
：
wherein ,maxfor the preset number (i.e., the maximum number of iterations), it may be reasonably set according to actual needs. In an iterative processfLinearly decreasing from 2 to 0.
Next, the determining four position vectors for each chimpanzee in the t-th round iteration relative to the first, second, third and fourth rank gorillas, respectively, comprises: based on the first
Optimizing path parameter group { about in round iteration>
[ and the following formula, determine ]>
Each chimpanzee in a round of iterations is referenced to four position vectors X of the first, second, third and fourth rank gorillas, respectively 1 (t)、X 2 (t)、X 3 (t)、X 4 (t)：
wherein ,
、/>
、/>
、/>
respectively +.>
The positions of the first-class gorilla, the second-class gorilla, the third-class gorilla and the fourth-class gorilla after iterative updating in turn, and the ∈>
Is->
The position of each chimpanzee after iterative updating of the round, < >>
And->
Is a random number between 0 and 1, < >>
And->
Is a random number between 0 and 1, < >>
And->
Is a random number between 0 and 1, < >>
And->
A random number between 0 and 1m 1 、m 2 、m 3 、m 4 The first chaotic vector, the second chaotic vector, the third chaotic vector and the fourth chaotic vector are respectively. Wherein the method comprises the steps ofm 1 、m 2 、m 3 、m 4 May be calculated based on some sort of chaotic map.
The attack mode of the chimpanzee is as follows: chimpanzees are finally surrounded by a search for the location of the prey. Typically, the hunting process is performed by an attacker, while the chaser, interceptor, and chaser only participate in the hunting process, with the 4 chimpanzees updating their own positions, respectively, and the other chimpanzees updating according to the positions of the four.
It should be noted that in various embodiments of the present invention, whentWhen=1, the firsttThe amount after 1 round of iterative updating (e.g. the position of each chimpanzee) refers to the initial amount (e.g. the initial position of each chimpanzee).
Then, the determination is made
The position vector for each chimpanzee in the round of iterative updating may include: based on the four position vectors X 1 (t)、X 2 (t)、X 3 (t)、X 4 (t) Dynamic weight->
First, at-1 round iteration updated position vector per chimpanzee +.>
And the following formula, determining said ++>
Position vector for each chimpanzee in a round iterative update
：
wherein ,
is the firstt-velocity vector in 1 round iteration, < ->
And->
Is a random number between 0 and 1, < >>
And->
Is a random number between 0 and 1, < >>
And->
A random number between 0 and 1
And->
Is a random number between 0 and 1,r 1 、r 2 、r 3 、r 4 are random numbers between 0 and 1.
Specifically, it is determined by the following formula
，
wherein ,
and->
Respectively->
Upper and lower limit values of>
, and maxIs a preset number.
In order to improve the defect of the chimpanzee algorithm, the embodiment fuses the particle updating mode of the particle swarm algorithm, combines the updating principle of the particle swarm algorithm (PSO, for example, the formulas (2) - (4)) with the chimpanzee optimization algorithm (ChOA), and finally obtains the PSO-ChOA algorithm. That is, the improved chimpanzee optimization algorithm is a chimpanzee optimization algorithm based on a dynamic weight strategy, and the key of the dynamic weight strategy method is to introduce a position determination factor
For dynamically updating the chimpanzee position. />
In the process of adopting dynamic weightAfter the chimpanzee position is updated by the heavy strategy method, the chimpanzee is updatedtSubstituting the position vector of each chimpanzee in the round iteration update into the coordinate parameter set in the network coverage model to obtain the firsttMultiple updated network coverage models in a round iteration, i.e. the set of coordinate parameters of the updated network coverage model corresponds to the firsttThe position vector of each chimpanzee in the round of iterative updating. Then, network coverage of the plurality of updated network coverage models is obtained. And, the network coverage rate of the network coverage model currently ranked first is matched with the firstt-comparing the network coverage of the first ranked network coverage model in the 1-round iterative update. The network coverage of the network coverage model at the first place of the current rank is greater than the first placet-1 in case of network coverage of the network coverage model ranked first in the iterative update of the round, the network coverage of the network coverage model to be arranged in descending order corresponds to the firsttThe position vector of each chimpanzee in the round iterative updating is updated to be the firsttAnd iterating the position vectors of the updated first-class orangutan, second-class orangutan, third-class orangutan, fourth-class orangutan and other hierarchical orangutan in turn.
Step S303, determining the position vectors of the first class of orangutan, the second class of orangutan, the third class of orangutan, the fourth class of orangutan and other classes of orangutan after the second round to the preset number of rounds of iterative updating based on the position vectors of the first class of orangutan, the second class of orangutan, the third class of orangutan, the fourth class of orangutan and other classes of orangutan after the first round to the preset number of rounds of iterative updating and the improved chimpanzee optimization algorithm.
The position vectors of the first-class orangutan, the second-class orangutan, the third-class orangutan and the other-class orangutan after the first-round iteration update obtained in the step S302 may be used as the initial position vector of each chimpanzee in the second-round iteration, and the process of specifically determining the position vectors of the first-class orangutan, the second-class orangutan, the third-class orangutan, the fourth-class orangutan and the other-class orangutan after the first-round iteration update may be described in the above description about the step S302.
Step S304, determining the position vector of the first class gorilla after iterative updating of the first preset number of rounds as the coordinate parameter set after iterative optimization.
After the position vectors of the chimpanzees of each class after the iterative updating of the first preset number of rounds are obtained through step S303, the position vector of the chimpanzee of the first class after the iterative updating of the first preset number of rounds is used as the coordinate parameter set after iterative optimization (i.e. the optimal solution of the coordinate parameter set).
And step S204, taking the coordinate parameter set after iterative optimization as the positions of the plurality of wireless sensors under the maximum network coverage rate.
The coordinate parameter set after iterative optimization can be obtained through the chimpanzee optimization algorithm (i.e. the improved chimpanzee optimization algorithm) based on the dynamic weight strategy, and then the network coverage rate of the network coverage model can be determined based on the obtained characteristic parameter set. Because the determined network coverage rate is the maximum network coverage rate, the iteratively optimized coordinate parameter set is the positions of the plurality of wireless sensors under the maximum network coverage rate.
The PSO-CHOA algorithm in the embodiment introduces a position updating mode and dynamic weight strategy factors of the particle swarmwThe problem that the original chimpanzee algorithm is easy to fall into a local optimal solution is solved, the global optimizing capability of the original algorithm is enhanced, and the optimizing precision of the algorithm is improved. The improved chimpanzee optimization algorithm is firstly put forward to be used in the wireless network sensor control optimization of the smart grid, so that a new thought is provided for the control optimization scheme of the wireless sensor of the smart grid.
The wireless sensor control optimization flow of the smart grid based on the PSO-ChOA algorithm will be described by taking fig. 4 as an example.
The wireless sensor control optimization process of the smart grid based on the PSO-ChOA algorithm may include the following steps S41-S412.
Step S41: network coverage model parameters of the wireless sensor are initialized.
Selecting intelligent electric net control areaADividing the regionAIs thatm×nGrid, initializing the number of sensorsNSensor sensing radiusr, and α 1 ，α 2 ，λ 1 ，λ 2 ，β 1 ，β 2 The sensors overlay parameters of the model.
Step S42: and constructing a network coverage model.
An expression of network coverage (i.e., an fitness function of network coverage) is constructed.
Step S43: parameters of the improved chimpanzee optimization algorithm are initialized.
Initializing the number, dimensions (i.e., two-dimensional coordinates x number of chimpanzees) of the improved chimpanzee optimization algorithm based on the network coverage model of the wireless network sensor, and for each chimpanzee (corresponding to a set of sensors) in a regionACoordinate values corresponding to the generated coordinates are generated (i.e., coordinate initialization is performed). The coordinate parameter sets are in one-to-one correspondence with the positions of the chimpanzees.
Step S44: the network coverage of each chimpanzee was calculated.
Substituting the set of coordinate parameters corresponding to each chimpanzee into the network coverage model to determine the network coverage of each network coverage model (the network coverage is the fitness value of the corresponding chimpanzee).
Step S45: and adopting a PSO-CHOA algorithm to transversely optimize the chimpanzee population.
After the positions of the chimpanzees are updated by adopting a dynamic weight strategy method, the fitness values of the chimpanzees in the father of the chimpanzee population are compared, and then the fitness values of the chimpanzees in the offspring are sequentially compared (namely, the fitness values of the chimpanzees are compared in each iteration round), so that the forward candidate optimal solution (namely, the position vector) in the transverse search space is obtained. This corresponds to obtaining a plurality of updated network coverage models in a t-th iteration, wherein the set of coordinate parameters of the updated network coverage models corresponds to the position vector of each chimpanzee in the t-th iteration update. Then, each set of coordinate parameters is input into a respective updated network coverage model to obtain network coverage of the plurality of updated network coverage models.
Step S46: updating the coordinate positions of the first four-class orangutans and other class orangutans.
Ordering the network coverage rates of the plurality of updated network coverage models in order from large to small, and determining a current optimal solutionX attacker And assign sub-optimal solutions to the following values in turnX barrier 、X chaser 、X drive . According toX attacker 、X barrier 、X chaser 、X drive And the positions of the four optimal solutions are updated by adopting a particle updating mode of a particle swarm algorithm.
Step S47: judging whether the maximum network coverage rate in the current iteration is greater than the maximum network coverage rate in the previous iteration, if so, executing the step S48; otherwise, step S49 is continued.
Step S48: the updated coordinate positions of the first four-rank gorilla and the other rank gorilla are determined as the coordinate positions of the first four-rank gorilla and the other rank gorilla of the current round iteration.
Step S49: judgingt=maxIf yes, executing step S410; otherwise, step S411 is performed.
Step S410: outputting the maximum coverage rate and the coordinate position of the corresponding wireless sensor.
And outputting a space optimal solution when the optimizing iteration number threshold is reached.
Step S411: updatingf、w、V。
Step S412:t= t + 1。
the intelligent power grid wireless sensor distribution control optimization method provided by the embodiment can realize the maximized wireless sensor network coverage rate; meanwhile, an improved chimpanzee optimization algorithm is provided, and the improved chimpanzee optimization algorithm has higher optimizing precision and convergence rate.
In summary, the invention creatively builds a network coverage model of a plurality of wireless sensors at first, and then optimizes the network coverage model based on an improved chimpanzee optimization algorithm to obtain the positions of the plurality of wireless sensors under the maximum network coverage of the network coverage model, which aims to solve the problems of redundancy, low coverage and large overall power consumption of wireless sensor nodes in the current smart grid, and adopts the improved chimpanzee optimization algorithm to optimize the network model of the wireless sensors, so that the coverage of the wireless sensor network reaches the maximum under the limited number of sensors and the fixed model, thereby reducing the flow pressure and the cost of a processing center.
An embodiment of the present invention further provides a system for optimizing the deployment of a wireless sensor, where the system for optimizing the deployment includes: construction means for constructing a network coverage model for a plurality of wireless sensors; and optimizing means for optimizing the network coverage model based on an improved chimpanzee optimization algorithm to obtain the positions of the plurality of wireless sensors at a maximum network coverage of the network coverage model, wherein the improved chimpanzee optimization algorithm is a chimpanzee optimization algorithm based on a dynamic weight strategy.
Specific details and benefits of the wireless sensor control optimization system provided in the embodiments of the present invention can be found in the above description of the wireless sensor control optimization method, and are not repeated here.
An embodiment of the present invention further provides a computer readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the method for optimizing the control of the wireless sensor is implemented.
The machine-readable storage medium includes, but is not limited to, phase-change Memory (abbreviation for phase-change random access Memory, phase Change Random Access Memory, PRAM, also known as RCM/PCRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash Memory (Flash Memory) or other Memory technology, compact disc read only Memory (CD-ROM), digital Versatile Disc (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, and the like.
The preferred embodiments of the present invention have been described in detail above with reference to the accompanying drawings, but the present invention is not limited to the specific details of the above embodiments, and various simple modifications can be made to the technical solution of the present invention within the scope of the technical concept of the present invention, and all the simple modifications belong to the protection scope of the present invention.
In addition, the specific features described in the above embodiments may be combined in any suitable manner without contradiction. The various possible combinations of the invention are not described in detail in order to avoid unnecessary repetition.
Moreover, any combination of the various embodiments of the invention can be made without departing from the spirit of the invention, which should also be considered as disclosed herein.
Claims (10)
1. The method for optimizing the control of the wireless sensor is characterized by comprising the following steps of:
constructing a network coverage model for a plurality of wireless sensors; and
optimizing the network coverage model based on a modified chimpanzee optimization algorithm to obtain the locations of the plurality of wireless sensors at a maximum network coverage of the network coverage model,
wherein the improved chimpanzee optimization algorithm is a chimpanzee optimization algorithm based on a dynamic weight strategy.
2. The method of distributed optimization according to claim 1, wherein the optimizing the network coverage model based on the improved chimpanzee optimization algorithm comprises:
initializing a coordinate parameter set in the network coverage model to obtain a plurality of coordinate parameter sets;
determining network coverage rate of a network coverage model formed by the coordinate parameter sets;
iteratively optimizing the set of coordinate parameters in the network coverage model by adopting the improved chimpanzee optimization algorithm, and determining the network coverage rate of the network coverage model as an objective function of the improved chimpanzee optimization algorithm; and
and taking the coordinate parameter set after iterative optimization as the positions of the plurality of wireless sensors under the maximum network coverage rate.
3. The method of distributed optimization according to claim 2, wherein iteratively optimizing the set of coordinate parameters in the network coverage model using the modified chimpanzee optimization algorithm and determining the network coverage of the network coverage model as an objective function of the modified chimpanzee optimization algorithm comprises:
determining initial position vectors of the first, second, third, fourth and other rank orangutans in the improved chimpanzee optimization algorithm, respectively, based on a set of coordinate parameters corresponding to network coverage rates of the network coverage models ranking the first, second, third, fourth and other ranks orangutans;
determining a first round of iteratively updated position vectors for the first, second, third, fourth and other rank orangutans based on the initial position vectors for the first, second, third, fourth and other rank orangutans and the improved chimpanzee optimization algorithm;
determining the position vectors of the first, second, third and fourth class orangutans and other class orangutans after the iterative updating of the second to the preset number of rounds based on the position vectors of the first, second, third and fourth class orangutans and the other class orangutans after the iterative updating of the first to the third rounds and the improved chimpanzee optimization algorithm; and
and determining the position vector of the first class gorilla after iteration updating of the first preset number of rounds as the coordinate parameter set after iteration optimization.
4. A method of optimizing a fabric according to claim 3, wherein the first is determinedtThe position vectors of the first-class gorilla, the second-class gorilla, the third-class gorilla, the fourth-class gorilla and other class gorilla after the round iteration update comprise:
based on the firsttFirst and second convergence factor groups in round iteration, determining the firsttOptimizing path parameter groups in round iteration;
chimpanzee-based optimization algorithm and the firsttDetermining the optimal path parameter group in round iterationtEach chimpanzee in the round of iterations is respectively relative to four position vectors of the first, second, third and fourth class orangutans;
based on the four position vectors, dynamic weights and the tht-determining the position vector of each chimpanzee after 1 round of iterative updatingtThe position vector of each chimpanzee in the iterative updating of the round to obtain the thtA plurality of updated network coverage models in a round of iteration, wherein,tis a positive integer and the set of coordinate parameters of the updated network coverage model corresponds to the firsttA position vector for each chimpanzee in the iterative updating of the round;
determining a network coverage rate of the plurality of updated network coverage models;
network coverage and the first network coverage of the network coverage model currently ranked firstt-1 comparing the network coverage of the first ranked network coverage model in the iterative update round; and
the network coverage of the network coverage model at the first place of the current rank is greater than the first placetIn the case of network coverage of the network coverage model ranked first in the iterative update of 1 round, this will be relative to the network coverage of the network coverage model currently ranked first, second, third, fourth and other bitsShall firsttThe chimpanzee position vector in the round iteration update is updated to be the firsttAnd iterating the position vectors of the updated first-class orangutan, second-class orangutan, third-class orangutan, fourth-class orangutan and other hierarchical orangutan in turn.
5. The method of optimizing control according to claim 4, wherein the determining a first
The optimizing path parameter group in the round iteration comprises:
based on the first
First convergence factor in round iteration +.>
Second Convergence factor group {>
The } and the following formula determine +.>
Optimizing path parameter group { about in round iteration>
}：
Wherein the second convergence factor set {
Each of the amounts in is a random number between 0 and 1.
6. The method of distributed optimization according to claim 4 or 5, wherein said determining four position vectors for each chimpanzee in the t-th round of iteration relative to the first, second, third and fourth rank gorillas, respectively, comprises:
based on the first
Optimizing path parameter group { about in round iteration>
[ and the following formula, determine ]>
Each chimpanzee in a round of iterations is referenced to four position vectors X of the first, second, third and fourth rank gorillas, respectively 1 (t)、X 2 (t)、X 3 (t)、X 4 (t)：
wherein ,
、/>
、/>
、/>
respectively +.>
The positions of the first-class gorilla, the second-class gorilla, the third-class gorilla and the fourth-class gorilla after iterative updating in turn, and the ∈>
Is->
The position of each chimpanzee after iterative updating of the round, < >>
And->
Is a random number between 0 and 1, < >>
And->
Is a random number between 0 and 1, < >>
And->
Is a random number between 0 and 1, < >>
And->
A random number between 0 and 1m 1 、m 2 、m 3 、m 4 The first chaotic vector, the second chaotic vector, the third chaotic vector and the fourth chaotic vector are respectively. />
7. The method of optimizing control according to claim 4, wherein the determining a first
The position vector of each chimpanzee in the round of iterative updating comprises:
based on the four position vectors X 1 (t)、X 2 (t)、X 3 (t)、X 4 (t) Dynamic weights
First, at-1 round iteration updated position vector per chimpanzee +.>
And the following formula, determining said ++>
Position vector for each chimpanzee in round iterative update +.>
：
wherein ,
is the firstt-velocity vector in 1 round iteration, < ->
And->
Is a random number between 0 and 1,
and->
Is a random number between 0 and 1, < >>
And->
A random number between 0 and 1
And->
Is a random number between 0 and 1,r 1 、r 2 、r 3 、r 4 are random numbers between 0 and 1.
8. The method of optimizing the control according to claim 7, wherein the determination is made by the following formula
，
wherein ,
and->
Respectively->
Upper and lower limit values of>
, and maxIs a preset number.
9. A distributed optimization system for a wireless sensor, the distributed optimization system comprising:
construction means for constructing a network coverage model for a plurality of wireless sensors; and
optimizing means for optimizing the network coverage model based on a modified chimpanzee optimization algorithm to obtain the locations of the plurality of wireless sensors at a maximum network coverage of the network coverage model,
wherein the improved chimpanzee optimization algorithm is a chimpanzee optimization algorithm based on a dynamic weight strategy.
10. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the method of controlling and optimizing a wireless sensor according to any of claims 1-8.
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