CN116261149A - Deployment method and system of sensor nodes in wireless sensor network - Google Patents
Deployment method and system of sensor nodes in wireless sensor network Download PDFInfo
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Abstract
The invention discloses a deployment method and a deployment system of sensor nodes in a wireless sensor network, wherein the coverage optimization problem is defined as the deployment problem of the sensor nodes which can maximize the network coverage rate in a monitoring area; and initializing a multi-strategy artificial bee colony algorithm, and then iteratively updating feasible solutions of population individuals by adopting the multi-strategy artificial bee colony algorithm until an approximately optimal solution is obtained, wherein in an employment stage, each employment bee randomly selects a search strategy in a multi-strategy pool, and updates the corresponding feasible solution by using the search strategy, and then accumulates the corresponding improvement amount when each search strategy successfully updates the feasible solution, and in an observation bee stage, updates the current feasible solution by using the search strategy with the largest improvement amount in the employment stage. Finally, deploying the sensor nodes with reference to the near optimal solution. According to the technical scheme, the network coverage rate is effectively improved through a strategy complementation mechanism, and a more reliable sensor node deployment scheme is obtained.
Description
Technical Field
The invention belongs to the technical field of wireless sensor networks, and particularly relates to a deployment method and a deployment system for sensor nodes in a wireless sensor network.
Background
A wireless sensor network (Wireless Sensor Network, WSN) is an emerging computing and network model that can be defined as a network of tiny, small, expensive, and highly intelligent devices called sensor nodes. The wireless sensor network is a network structure formed by constructing a plurality of sensor nodes through a wireless communication technology, is mainly applied to detection and monitoring of a target area, and is widely applied in the industry, such as application fields of city monitoring, environment detection, military monitoring, moving target tracking, intelligent home and the like. However, the sensor node itself has some limitations, such as higher network cost and weaker sensing range. When the sensor nodes are deployed, the redundancy of the sensor nodes should be reduced as much as possible, and the coverage rate of the WSN is improved.
Sensor node coverage optimization is an important problem in wireless sensor networks, and the coverage degree has a great influence on network quality. The purpose of sensor node coverage optimization is to maximize the area within the network that can be monitored with a limited number of sensors, while minimizing detection blind spots as much as possible. Typically, the sensor nodes are deployed randomly in the target area to be detected. However, the scheme of random deployment can lead to high node density and high redundancy, so that the overall coverage rate is low, and the monitoring quality of the wireless sensor network is further affected. Therefore, a reasonable sensor node deployment scheme is required to be designed, so that the service quality and the energy utilization rate of the wireless sensor network are improved, and the load balance of transmission in the wireless sensor network can be realized.
Sensor node coverage optimization is essentially a typical NP-hard problem, affected by network resources and coverage characteristics, and many classical mathematical optimization methods are difficult to solve, such as gradient descent methods. In recent years, many scholars have studied the node coverage problem of wireless sensor networks, and among them, genetic algorithms (Genetic Algorithm, GA), particle swarm algorithms (Particle Swarm Optimization Algorithm, PSO), artificial bee colony algorithms (Artificial Bee Colony Algorithm, ABC), simulated annealing algorithms (Simulated Annealing Algorithm, SA) and the like are most popular to solve the problem. The optimization algorithm has little requirement on mathematical properties of the problems and has strong adaptability.
Although some of the heuristic algorithms described above have been successful in optimizing coverage of a wireless sensor network, they are actually all solutions that are approximately optimal, rather than the best possible solutions to the problem. In addition, the search strategies of the algorithms are too greedy, when the iteration reaches the middle and late stages, the solution is extremely easy to fall into the locally optimal solution of the problem, and the solution with higher quality is difficult to obtain.
Disclosure of Invention
The invention aims to solve the problem that the solving precision of a deployment optimization method of a sensor node in the existing wireless sensor network is to be improved, and provides a deployment method and a deployment system of the sensor node in the wireless sensor network. Specifically, the deployment method provided by the technical scheme of the invention introduces the artificial bee colony algorithm to optimize the sensor node deployment of the wireless sensor network, optimizes the artificial bee colony algorithm, sets a multi-strategy pool on one hand, provides a plurality of search strategies, fully exerts the good and bad complementary effects of different strategies, and on the other hand, selects the maximum delta of the improvement amount greedily by accumulating the corresponding improvement amount when each search strategy successfully updates the feasible solution in the employment stage and observing the bee stage i The search strategy of the method is used as the search strategy of the stage to update a feasible solution, the optimization performance of the algorithm is enhanced through the principle of good and bad complementation among the strategies, the characteristic of coverage optimization problem is combined with the working principle of the optimization algorithm, the precision of the feasible solution is further improved, and the coverage rate under the wireless sensor network is maximized.
In one aspect, the invention provides a deployment method of sensor nodes in a wireless sensor network, which comprises the following steps:
step 1: solving a coverage optimization problem of the wireless sensor network corresponding to the monitoring area by adopting a multi-strategy artificial bee colony algorithm to obtain an approximate optimal solution;
step 2: deploying sensor nodes in the monitoring area by using the approximate optimal solution;
the coverage optimization problem is as follows: the sensor node deployment problem that the network coverage rate in the monitoring area reaches the maximum is solved; discretizing the monitoring area into M multiplied by N monitoring points, wherein the number of sensor nodes to be deployed is D, and D is a positive integer;
in the process of solving the coverage optimization problem by adopting the multi-strategy artificial bee colony algorithm, the feasible solution corresponding to each population individual is a sensor node deployment result of the coverage optimization problem; firstly, randomly generating an initial population to obtain an initial feasible solution, and then iteratively updating the feasible solution corresponding to the population individuals to take the optimal feasible solution after the iteration termination condition is met as the approximate optimal solution;
Each round of iterative updating is to sequentially execute feasible solution updating of hiring bees, observing bees and reconnaissance bees; in the hiring period, each hiring bee randomly selects a search strategy in a multi-strategy pool, and updates a corresponding feasible solution by using the search strategy, and then accumulates the corresponding improvement amount when each search strategy successfully updates the feasible solution; when the adaptability of the new feasible solution is better than that of the current feasible solution, the feasible solution is successfully updated as the corresponding searching strategy, and the new feasible solution is used for replacing the current feasible solution;
in the observing bee stage, the current feasible solution is updated by the searching strategy with the greatest improvement in the hiring bee stage.
Specifically, the procedure of step 1 is as follows:
step 1-1: initializing a multi-strategy artificial bee colony algorithm, wherein the method at least comprises the steps of setting initial values of population size, maximum evaluation times and improvement quantity and randomly generating initial feasible solutions of population individuals in a solution search space;
step 1-2: iteratively updating the feasible solutions of the population individuals based on the multi-strategy artificial bee colony algorithm until the iteration termination condition is met to obtain an approximately optimal solution, wherein each iteration is to sequentially execute the feasible solution updating at the stage of hiring bees, observing bees and reconnaissance bees;
In the hiring period, each hiring bee randomly selects a search strategy in a multi-strategy pool, and then updates a corresponding feasible solution by using the search strategy, and then accumulates the corresponding improvement amount when each search strategy successfully updates the feasible solution, wherein the multi-strategy pool comprises 2 or more search strategies, and when the adaptability of the new feasible solution is better than that of the current feasible solution, the new feasible solution is regarded as the corresponding search strategy to successfully update the feasible solution and replace the current feasible solution;
in the observing bee stage, the current feasible solution is updated by the searching strategy with the greatest improvement in the hiring bee stage.
Further optionally, the search strategies in the multi-strategy pool are expressed as follows:
wherein X is i The parent individuals correspond to feasible solutions before updating; v (V) i As offspring individuals, corresponding to the updated feasible solution; x is X k And X t Are all feasible solutions corresponding to a random individual in the population, and X i ≠X k ≠X t The method comprises the steps of carrying out a first treatment on the surface of the The parameter K is a variable coefficient that varies with iteration, and
is [ -1.5,1.5]Random numbers distributed uniformly, FEs are the current evaluation times, i.e. every update of the feasible solution, the current evaluation times are added by one, maxFEs are the maximum evaluation times, gaussian (v 1 ,|δ 2 I) is a gaussian distribution function, δ 1 Is the central area of Gaussian distribution, delta 2 For disturbance range X best Is the current best feasible solution in the population.
The technical scheme creatively proposes to construct a multi-strategy pool, and further selects 4 search strategies with different characteristics, wherein the 4 search strategies comprise three search strategies with strong local search capability and one search strategy with strong global search capability, so that the local search capability and the global search capability are enhanced, and the optimization performance of an algorithm is enhanced through the complementation of the advantages and the disadvantages of the strategies. The set K value is a dynamic searching step length, which is favorable for helping the algorithm to jump out of local optimum and obtain a better deployment scheme.
Further preferably, in the process of each iteration update, when the adaptability of the new feasible solution is better than that of the current feasible solution, the new feasible solution is substituted for the current feasible solution. The fitness function is coverage rate of the monitoring area, and the corresponding formula is as follows:
wherein CR is A Representing the coverage of the monitored area a, P i is the set of perceived probabilities of the points covered by the sensor node i,
representing a set of points that D sensor nodes can monitor within the monitored area.
Further preferably, the coverage optimization problem adopts a probability perception model, and the formula of the corresponding perception probability is as follows:
Wherein P is S,Q For the perception probability between the sensor node and the monitoring point, S represents the central position of the sensor node, Q represents the monitoring point in the monitoring area, lambda 1 ＝r e -r+d(S,Q)、λ 2 ＝r e +r-d(S,Q)，λ 1 、λ 2 Are defined intermediate parameters, r e For the radius fluctuation value of the uncertain detection capacity of the sensor node, r is the sensing radius of the sensor node, d (S, Q) is the Euclidean distance between S and Q, and alpha is calculated 1 、α 2 、β 1 、β 2 Is of perceived probabilityThe attenuation coefficient is typically an empirical value, and the set of parameters is set to 1, 0, 1 and 1.5 in the following embodiments of the present invention, but the present invention is not limited thereto; e is a natural base.
When constructing the mathematical model of the coverage optimization problem, environmental factors and the attribute of the sensor node should be considered, otherwise, the accuracy of the simulation experiment result is seriously affected. In the coverage optimization problem, a commonly used mathematical model is a binary perception model, which is too concise and idealized to divide a monitoring area into whether the monitoring area can be perceived by adopting the perception radius of a sensor node. The technical scheme of the invention adopts a probability perception model which is more in line with the actual situation, and the model considers that the perception range of the sensor node can fluctuate along with the environmental factors by introducing the radius fluctuation value, and the perception probability of the model can be attenuated in a negative exponential trend along with the increase of the Euclidean distance between the monitoring point and the sensor node, so that the data model constructed for the coverage optimization problem is more matched with the environmental factors and the attribute of the sensor node, and the integral precision of a feasible solution is finally improved.
In the probability perception model, 1 represents 100%, and the monitoring point can be perceived by the sensor, namely, the range smaller than or equal to r-re is a range which can be monitored by the sensor; and the range larger than r+re corresponds to the sensing probability of 0, which indicates that the sensor node cannot sense the range larger than r+re; it is therefore determined whether a detection point less than or equal to r-re, and greater than the r+re range, can be monitored by the sensor node; however, other ranges besides this indicate that a certain point has
Is monitored. For example, a probability of 60% can be detected, typically within (0-100%) a range, if any<=60%, this point is considered to be monitored; if random>60%, is not detected by the sensor. Since the above are the contents of the probabilistic perceptual model, and will not be explained in more detail, it should be understood that the sensing probability between the sensor node and the monitoring point can be based onAnd (5) determining the coverage rate of the monitoring area.
Further preferably, if a feasible solution is not successfully updated for limit times, in the scout bee stage, updating the feasible solution by adopting a global neighborhood search mechanism, wherein limit is a dynamic threshold;
The dynamic threshold limit is formulated as follows:
limit＝200·(FEs/MaxFEs)
wherein, FEs is the current evaluation times, namely, every update of the feasible solution, the current evaluation times is increased by one, maxFEs is the maximum evaluation times, and the maximum evaluation times are set by the initialization process of the multi-strategy artificial bee colony algorithm.
The fixed threshold limit setting is somewhat difficult to meet the requirements in different optimization states. In the early stage of iteration, the algorithm has strong global searching capability, and the trigger frequency of the mechanism can be increased due to the small limit setting; late in the iteration, the local search capability of the problem should be biased, and the threshold limit needs to be set large to reduce the trigger frequency of the mechanism. Therefore, the technical scheme of the invention dynamically adjusts the parameter limit to be increased along with iteration. It should be noted that it is also preferable that the minimum value of limit be not less than 20, avoiding disruption of the balance of exploration and exploitation capabilities of the algorithm, ensuring good search performance of the algorithm.
Further preferably, the global neighborhood search mechanism is represented as follows:
TX i ＝r 1 ·X i +r 2 ·X best +r 3 ·(X j -X k )
wherein r is 1 、r 2 And r 3 Is [0,1 ]]Random numbers in the interval and satisfy r 1 +r 2 +r 3 ＝1。X j And X k Is two random individuals in the population, X i ≠X j ≠X k ，X best Is the current best feasible solution in the population.
Further preferably, the probability p is used in the employment stage and the observation stage m Accepting a new feasible solution worse than the current feasible solution, wherein the probability p m The method comprises the following steps:
p m ＝0.1*(FEs/MaxFEs)
wherein, FEs is the current evaluation times, namely, every update of the feasible solution, the current evaluation times is increased by one, maxFEs is the maximum evaluation times, and the maximum evaluation times are set by the initialization process of the multi-strategy artificial bee colony algorithm.
The technical scheme of the invention combines the thought of simulated annealing, receives slightly worse feasible solutions under a certain probability, and is beneficial to the algorithm to jump out of local optimum.
In a second aspect, the present invention provides a system based on the deployment method, which includes: a near optimal solution solving module and a deployment module;
the approximate optimal solution solving module is used for solving the coverage optimization problem of the wireless sensor network corresponding to the monitoring area by adopting a multi-strategy artificial bee colony algorithm to obtain an approximate optimal solution;
the deployment module is used for deploying sensor nodes in the monitoring area with the approximate optimal solution;
the coverage optimization problem is as follows: the method comprises the steps that the problem of deploying sensor nodes in a monitoring area with maximum network coverage rate is solved, wherein the monitoring area is discretized into M multiplied by N monitoring points, the number of the sensor nodes to be deployed is D, D is a positive integer, and each solution corresponds to a sensor node deployment result;
In the process of solving the coverage optimization problem by adopting the multi-strategy artificial bee colony algorithm, the feasible solution corresponding to each population individual is a sensor node deployment result of the coverage optimization problem; firstly, randomly generating an initial population to obtain an initial feasible solution, and then iteratively updating the feasible solution corresponding to the population individuals to take the optimal feasible solution after the iteration termination condition is met as the approximate optimal solution;
each round of iterative updating is to sequentially execute feasible solution updating of hiring bees, observing bees and reconnaissance bees; and in the hiring bee stage, each hiring bee randomly selects a search strategy in a multi-strategy pool, and then updates a corresponding feasible solution by using the search strategy, and then accumulates the corresponding improvement amount when each search strategy successfully updates the feasible solution, so that in the observing bee stage, the feasible solution of the observing bee stage is updated by the search strategy with the largest improvement amount in the hiring bee stage; the multi-policy pool includes 2 or more search policies.
In a third aspect, the present invention provides an electronic terminal, which at least includes:
one or more processors;
a memory storing one or more computer programs;
wherein the processor invokes the computer program to implement:
A method for deploying sensor nodes in a wireless sensor network.
In a fourth aspect, the present invention provides a computer readable storage medium storing a computer program, the computer program being invoked by a processor to implement:
a method for deploying sensor nodes in a wireless sensor network.
Advantageous effects
1. According to the deployment method provided by the technical scheme of the invention, the sensor node deployment of the wireless sensor network is optimized by introducing the artificial bee colony algorithm, the artificial bee colony algorithm is optimized, the multi-strategy pool is creatively provided and constructed, a plurality of search strategies are provided, the good and bad complementary effects of different strategies are fully exerted, the optimization performance of the algorithm is enhanced through the good and bad complementary among the strategies, the accuracy of feasible solutions is further improved, and the coverage rate under the wireless sensor network is maximized. In addition, the corresponding improvement amounts when each search strategy is successfully updated to the feasible solution are accumulated in the hiring bee stage, and the maximum delta of the improvement amounts is selected in the observing bee stage in a greedy manner i The search strategy of the stage is used as the search strategy of the stage to update the feasible solution, so that the stage of observing bees can find a better feasible solution, thereby improving the algorithm precision.
2. In order to solve the problem that the existing algorithm is in local optimum, the technical scheme of the invention further improves from multiple angles, and comprises the steps of constructing a multi-strategy pool, preferably 4 search strategies with different characteristics, including three search strategies with strong local search capabilityAnd a search strategy with strong global search capability, thereby enhancing the local search and global search capability; the parameter limit is dynamically adjusted to be increased along with iteration, and the probability p is used in the employment bee stage and the observation bee stage m And receiving new feasible solutions worse than the current feasible solutions, and helping the algorithm to jump out of the local optimum, so that the algorithm precision is improved.
3. According to the technical scheme, a probability perception model which is more in line with actual situations is adopted for the data model of the coverage optimization problem, the perception range of the sensor node is considered to be fluctuated along with environmental factors by introducing a radius fluctuation value, and the perception probability of the sensor node is attenuated in a negative exponential trend along with the increase of Euclidean distance between a monitoring point and the sensor node, so that the data model constructed for the coverage optimization problem is more matched with the environmental factors and the attributes of the sensor node, and the overall accuracy of a feasible solution is finally improved.
Drawings
FIG. 1 is a schematic flow chart of a multi-strategy artificial bee colony algorithm provided by the invention;
FIG. 2 is a sensor profile of a test scenario provided by an example of the present invention;
FIG. 3 is a convergence curve of different algorithm optimizations under WSN coverage optimization in a 40m by 40m scenario, the algorithm categories including SaMABC, PSO, ABC, GABC, GBABC, ABCVSS, ECABC, NABC of the present invention;
FIG. 4 is a convergence curve of different algorithm optimizations under WSN coverage optimization in a 50m by 50m scenario, the algorithm categories including SaMABC, PSO, ABC, GABC, GBABC, ABCVSS, ECABC, NABC of the present invention;
FIG. 5 is a convergence curve of different algorithm optimizations under WSN coverage optimization in a 100m scenario, the algorithm categories including SaMABC, PSO, ABC, GABC, GBABC, ABCVSS, ECABC, NABC of the present invention;
FIG. 6 is a sensor node deployment result of different algorithms, wherein graphs (a) through (i) correspond to an initialization, a PSO optimized deployment result, an ABC optimized deployment result, a GABC optimized deployment result, a GBABC optimized deployment result, an ABCVSS optimized deployment result, an ECABC optimized deployment result, a NABC optimized deployment result, and a SaMABC optimized deployment result of the present invention, respectively.
Detailed Description
The invention provides a deployment method of sensor nodes in a wireless sensor network, which is a technical means of sensor node coverage optimization and is used for determining the deployment of sensor nodes in a monitoring area. The invention aims at the maximum coverage rate, introduces an artificial bee colony algorithm, optimizes the algorithm according to the characteristics of a wireless sensor network, constructs a multi-strategy artificial bee colony algorithm, provides a plurality of search strategies for selection, screens and observes the search strategy of a bee stage by the maximum improvement amount by accumulating the corresponding improvement amount when each search strategy is successfully updated to a feasible solution, further updates the feasible solution, effectively improves the network coverage rate, is favorable for helping the algorithm to jump out of local optimization, and obtains a better deployment scheme. The invention will be further illustrated with reference to examples.
Example 1:
the deployment method of the sensor nodes in the wireless sensor network provided by the embodiment comprises the following steps:
step 1: and solving the coverage optimization problem of the wireless sensor network corresponding to the monitoring area by adopting a multi-strategy artificial bee colony algorithm to obtain an approximate optimal solution.
In this embodiment, the length of the monitoring area a is M meters, the width is N meters, and m×n monitoring points are disposed in the monitoring area a. The monitoring area A is digitally discretized into M multiplied by N monitoring points aiming at the two-dimensional plane monitoring area A, and each monitoring point has corresponding position coordinates.
Wherein, the coverage optimization problem is: aiming at D sensor nodes to be deployed, the sensor node deployment problem that the network coverage rate in the monitoring area reaches the maximum is solved. I.e., essentially an NP-hard problem, is first converted into a solvable objective function, and then the coverage optimization problem is solved using an optimization algorithm.
The implementation process of the step 1 comprises the following steps:
step 1-1: initializing the multi-strategy artificial bee colony algorithm, which at least comprises setting initial values of population size, maximum evaluation times and improvement quantity and randomly generating initial feasible solutions in a solution search space by population individuals.
Specifically, the population size SN in this embodiment is 50, and each individual corresponds to one feasible solution of the coverage optimization problem (in this embodiment, the number of employment bees and observation bees is SN, and when one feasible solution is not successfully improved by limit times, the employment bees or the observation bees related to the feasible solution are converted into the spy bees and responsible for resetting the feasible solution, so that the number of the spy bees is basically 1 each time, and the spy bees or the spy bees are immediately converted back, and one feasible solution is represented as a deployment scheme of wireless sensor nodes in the monitoring area a. And D is set as the dimension of the coverage optimization problem, namely D corresponds to the number of the sensor nodes to be deployed. The maximum evaluation number MaxFEs was 300000 times; p is p m Set to 0.1 x (FEs/MaxFEs), FEs being the current number of evaluations; improvement magnitude delta for all search strategies i Is set to 0. The solution search space is preset or determined by other existing algorithms, which the present invention does not specifically limit.
It should be noted that, the coverage rate is used for representing the fitness, the larger the fitness value (coverage rate) is, the better the quality of the feasible solution corresponding to the individual is, namely, the fitness value is used for evaluating the quality of the deployment scheme.
To set the fitness function, the coverage is first understood. The invention characterizes a data model of the coverage optimization problem by using a probability perception model, and the model considers that the perception range of a sensor node can fluctuate along with environmental factors by introducing a radius fluctuation value, and the perception probability of the model can be attenuated in a negative exponential trend along with the increase of Euclidean distance between a monitoring point and the sensor node. Perception probability P of probability perception model S,Q The calculation formula is as follows:
wherein P is S,Q For the perception probability between the sensor node and the monitoring point, S represents the central position of the sensor node, and Q represents the monitoring pointMonitoring points, lambda in the area 1 ＝r e -r+d(S,Q)、λ 2 ＝r e +r-d(S,Q)，λ 1 、λ 2 Are defined intermediate parameters, r e Radius fluctuation value (r) for uncertain detection capability of sensor node e The value is a predetermined fixed value, r is dependent on the scene e The values are also different. The invention carries out simulation experiments under three wireless sensor network scenes, the configuration parameters are shown in the following table 1), r is the sensing radius of a sensor node, d (S, Q) is the Euclidean distance between S and Q, and alpha is calculated 1 、α 2 、β 1 、β 2 Is the decay coefficient of the perceptual probability, e is the natural base.
Wherein S is x 、S y And Q x 、Q y Representing the abscissa and ordinate of the sensor node S and the monitoring point Q in the two-dimensional plane, respectively.
Table 1 parameter configuration in a wireless sensor network scenario
The coverage rate of the monitoring area A is then determined by using the perception probability:
wherein CR is A Representing the coverage of the monitored area a, P i is the set of perceived probabilities of the points covered by the sensor node i, U represents the meaning of the union in the set,
representing the incorporation of the value of the ith in total D into the set, +.>
Representing the set of monitorable points of D sensor nodes in the monitoring area, namely, confirming M multiplied by N detection points one by one, whether the points can be sensed by D sensors or not, and according to a probability sensing formula P S,Q To determine if a certain detection point is monitored by D sensors. The ratio of the monitored points to the area of the monitored area is the coverage rate.
Step 1-2: and carrying out iterative updating on the feasible solutions of the population individuals based on the multi-strategy artificial bee colony algorithm until the iteration termination condition is met to obtain an approximately optimal solution, wherein each iteration is to sequentially execute the feasible solution updating at the stage of hiring bees, observing bees and reconnaissance bees. The specific process is as follows:
And (3) a step of: firstly judging whether the employment bee stage is completely finished or not, namely, each employment bee searches and updates once based on the step, if not, the corresponding employment bee randomly selects a search strategy in a multi-strategy pool, and the feasible solution X of the current individual is updated by using the selected search strategy i If the updated feasible solution V i Is superior to the current feasible solution X i With new feasible solution V i Substitution of old solution X i Accumulating the value delta of the improvement amount successfully updated by the search strategy i The method comprises the steps of carrying out a first treatment on the surface of the If inferior to the current feasible solution X i With a certain probability p m Accept to go than the current feasible solution X i Slightly worse solution V i . If the employment bee phase is completed, an observe bee phase is performed.
The improvement amount is indicative of the optimization degree, and therefore, the embodiment directly uses the fitness as a standard, and the corresponding calculation formula is as follows. In other possible embodiments, other references may be set to characterize fitness improvements, algorithm improvements, with reference to fitness.
The multi-strategy pool related in the step is provided with two or more search strategies, and in the embodiment, 4 search strategies with different characteristics are provided, namely three search strategies with strong local search capability and one search strategy with strong global search capability, and optimization performance of an algorithm is expected to be enhanced through a good-bad complementation principle among strategies. The search strategies in the multi-strategy pool are expressed as follows:
Wherein X is i The parent individuals correspond to feasible solutions before updating; v (V) i As offspring individuals, corresponding to the updated feasible solution; x is X k And X t Is a feasible solution corresponding to a random individual in the population, and X i ≠X k ≠X t The method comprises the steps of carrying out a first treatment on the surface of the The parameter K is a variable coefficient that varies with iteration, and
is [ -1.5,1.5]Random numbers distributed uniformly, FEs are the current evaluation times, i.e. every update of the feasible solution, the current evaluation times are increased by one, maxFEs are the maximum evaluation times, gaussian (delta) 1 ,|δ 2 I) is a gaussian distribution function, δ 1 Is the central area of Gaussian distribution, delta 2 For disturbance range X best Is the current optimal solution in the population.
And II: in the observing bee stage, judging whether the observing bee stage is completed or not, namely, each observing bee with the number of SN performs searching and updating once based on the step, if not completed, the observing bee greedily selects the maximum improvement amount delta according to the improvement amount delta of the searching strategy recorded in the hiring bee stage i Is used as the searching strategy of the stage to update X i If the updated feasible solution V i Is superior to the current feasible solution X i With new feasible solution V i Substitution of old solution X i If inferior to the current feasible solution X i With a certain probability p m Accept to go than the current feasible solution X i Slightly worse solution V i . If the employment stage has been completed, a scout stage is performed.
Thirdly,: in the stage of the reconnaissance bee, judging whether the stage of the reconnaissance bee is completely finished, namely scanning all (SN) individuals in the whole population, checking whether bees are not successfully updated for limit times, if not, solving the problem X when the honeybees are feasible i The dynamic threshold limit is updated unsuccessfully for times, and a global neighborhood searching mechanism is adopted to update the dynamic threshold limit; otherwise, executing the next step to judge whether the iteration termination condition is met currently.
Wherein, a dynamic threshold limit is set to prevent the algorithm from falling into a search stagnation state, and in order to avoid damaging the balance of the exploration and exploitation capabilities of the algorithm, the minimum value of limit must not be lower than 20 to ensure that the algorithm has good search performance. The specific calculation is as follows:
limit＝200·(FEs/MaxFEs)
the global neighborhood search mechanism learns historical search experience to find a better feasible solution. The global neighborhood search mechanism is as follows:
TX i ＝r 1 ·X i +r 2 ·X best +r 3 ·(X j -X k )
wherein r is 1 、r 2 And r 3 Is [0,1 ]]Random numbers in the interval and satisfy r 1 +r 2 +r 3 ＝1。X j And X k Is two random individuals in the population, X i ≠X j ≠X k 。
Fourth, the method comprises the following steps: judging whether an iteration termination condition is met, and if so, outputting an approximate optimal solution; if not, continuing iteration, and entering a next hiring bee stage. I.e. the near optimal solution is the feasible solution with the largest current fitness value.
In this embodiment, when FEs < = MaxFEs, the iteration termination condition is regarded as being temporarily unsatisfied, and the iteration condition is satisfied. FEs are the current number of evaluations, and each time any individual (feasible solution) in the population is initialized or updated, the fitness value is recalculated, and then the current number of evaluations FEs is increased by one.
Step 4: and deploying sensor nodes in the monitoring area with the approximately optimal solution.
To sum upThe deployment method optimizes the employment of bees and the feasible solution updating of the bee observation stage by constructing a multi-strategy pool and accumulating the improvement quantity, and in addition, sets a dynamic threshold limit and accepts a solution X which is higher than the current feasible solution X with a certain dynamic probability pm i Slightly worse solution V i The algorithm is assisted to jump out of local optimum, and the reliability of an approximate optimum solution obtained by the algorithm is improved.
Example 2:
the present embodiment is based on the deployment method provided in embodiment 1, and further provides a system based on the deployment method, which includes: and the approximate optimal solution solving module and the deployment module.
The approximate optimal solution solving module is used for solving the coverage optimization problem of the wireless sensor network corresponding to the monitoring area by adopting a multi-strategy artificial bee colony algorithm to obtain an approximate optimal solution; the deployment module is configured to deploy sensor nodes within the monitoring area with the approximately optimal solution.
Wherein, the coverage optimization problem is: aiming at D sensor nodes to be deployed, the sensor node deployment problem that the network coverage rate in the monitoring area reaches the maximum is solved. In the process of solving the coverage optimization problem by adopting the multi-strategy artificial bee colony algorithm, the feasible solution corresponding to each population individual is the sensor node deployment result of the coverage optimization problem.
Wherein, the approximate optimal solution solving module comprises: an initialization module and an update module. The initialization module is used for initializing the multi-strategy artificial bee colony algorithm and at least comprises the steps of setting initial values of population size, maximum evaluation times and improvement quantity and randomly generating initial feasible solutions of population individuals in a solution search space; the updating module is used for carrying out iterative updating on the feasible solutions of the population individuals based on the multi-strategy artificial bee colony algorithm until the iteration termination condition is met to obtain an approximately optimal solution, wherein each iteration is to sequentially execute the feasible solution updating of the employment bee, the observation bee and the reconnaissance bee stages.
In the hiring period, each hiring bee randomly selects a search strategy in a multi-strategy pool, and then updates a corresponding feasible solution by using the search strategy, and then accumulates the corresponding improvement amount when each search strategy successfully updates the feasible solution, wherein the multi-strategy pool comprises 2 or more search strategies, and when the adaptability of the new feasible solution is better than that of the current feasible solution, the new feasible solution is regarded as the corresponding search strategy to successfully update the feasible solution and replace the current feasible solution; in the observing bee stage, the current feasible solution is updated by the searching strategy with the greatest improvement in the hiring bee stage.
The implementation process of each module refers to the content of the above method, and will not be described herein. It should be understood that the above-described division of functional modules is merely a division of logic functions, and other divisions may be implemented in actual manners, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Meanwhile, the integrated units can be realized in a hardware form or a software functional unit form.
Example 3:
the present embodiment provides an electronic terminal, which at least includes: one or more processors; and a memory storing one or more computer programs.
Wherein the processor invokes the computer program to implement: a method for deploying sensor nodes in a wireless sensor network.
The method specifically comprises the following steps:
step 1: solving a coverage optimization problem of the wireless sensor network corresponding to the monitoring area by adopting a multi-strategy artificial bee colony algorithm to obtain an approximate optimal solution;
step 2: deploying sensor nodes in the monitoring area by using the approximate optimal solution;
the coverage optimization problem is as follows: the problem of deployment of sensor nodes with the largest network coverage rate in a monitoring area is solved, the number of the sensor nodes to be deployed is D, and D is a positive integer; in the process of solving the coverage optimization problem by adopting the multi-strategy artificial bee colony algorithm, the feasible solution corresponding to each population individual is the sensor node deployment result of the coverage optimization problem.
The process of step 1 is as follows:
step 1-1: initializing a multi-strategy artificial bee colony algorithm, wherein the method at least comprises the steps of setting initial values of population size, maximum evaluation times and improvement quantity and randomly generating initial feasible solutions of population individuals in a solution search space;
step 1-2: and carrying out iterative updating on the feasible solutions of the population individuals based on the multi-strategy artificial bee colony algorithm until the iteration termination condition is met to obtain an approximately optimal solution, wherein each iteration is to sequentially execute the feasible solution updating at the stage of hiring bees, observing bees and reconnaissance bees.
Wherein, each round of iterative process is as follows:
and (3) a step of: firstly judging whether the employment bee stage is completely finished or not, namely, each employment bee searches and updates once based on the step, if not, the corresponding employment bee randomly selects a search strategy in a multi-strategy pool, and the feasible solution X of the current individual is updated by using the selected search strategy i If the updated feasible solution V i Is superior to the current feasible solution X i With new feasible solution V i Substitution of old solution X i Accumulating the value delta of the improvement amount successfully updated by the search strategy i The method comprises the steps of carrying out a first treatment on the surface of the If inferior to the current feasible solution X i With a certain probability p m Accept to go than the current feasible solution X i Slightly worse solution V i . If the employment bee phase is completed, an observe bee phase is performed.
And II: in the observing bee stage, judging whether the observing bee stage is completed or not, namely, each observing bee with the number of SN performs searching and updating once based on the step, if not completed, the observing bee greedily selects the maximum improvement amount delta according to the improvement amount delta of the searching strategy recorded in the hiring bee stage i Is used as the searching strategy of the stage to update X i If the updated feasible solution V i Is superior to the current feasible solution X i With new feasible solution V i Substitution of old solution X i If inferior to the current feasible solution X i With a certain probability p m Accept to go than the current feasible solution X i Slightly worse solution V i . If the employment stage is completed, executeAnd (5) a bee-reconnaissance stage.
Thirdly,: in the stage of the reconnaissance bee, judging whether the stage of the reconnaissance bee is completely finished, namely scanning all (SN) individuals in the whole population, checking whether bees are not successfully updated by limit times, if not, solving the problem X when the updating is feasible i The dynamic threshold limit is updated unsuccessfully for times, and a global neighborhood searching mechanism is adopted to update the dynamic threshold limit; otherwise, executing the next step to judge whether the iteration termination condition is met currently.
Fourth, the method comprises the following steps: judging whether an iteration termination condition is met, and if so, outputting an approximate optimal solution; if not, continuing iteration, and entering a next hiring bee stage. I.e. the near optimal solution is the feasible solution with the largest current fitness value.
It should be understood that the implementation process of part of the steps and whether or not the part of the steps are executed, and the execution sequence may refer to the implementation process of the foregoing embodiment.
The memory may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk memory.
If the memory and the processor are implemented independently, the memory, the processor, and the communication interface may be interconnected by a bus and communicate with each other. The bus may be an industry standard architecture bus, an external device interconnect bus, or an extended industry standard architecture bus, among others. The buses may be classified as address buses, data buses, control buses, etc.
Alternatively, in a specific implementation, if the memory and the processor are integrated on a chip, the memory and the processor may communicate with each other through an internal interface.
It should be appreciated that in embodiments of the present invention, the processor may be a central processing unit (Central Processing Unit, CPU), which may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSPs), application specific integrated circuits (Application Specific Integrated Circuit, ASICs), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The memory may include read only memory and random access memory and provide instructions and data to the processor. A portion of the memory may also include non-volatile random access memory. For example, the memory may also store information of the device type.
Example 4:
the present embodiment provides a computer-readable storage medium storing a computer program that is called by a processor to implement: a method for deploying sensor nodes in a wireless sensor network.
The method specifically comprises the following steps:
step 1: solving a coverage optimization problem of the wireless sensor network corresponding to the monitoring area by adopting a multi-strategy artificial bee colony algorithm to obtain an approximate optimal solution;
step 2: deploying sensor nodes in the monitoring area by using the approximate optimal solution;
the coverage optimization problem is as follows: and enabling the coverage rate of the network in the monitoring area to reach the maximum sensor node deployment problem, wherein the number of the sensor nodes to be deployed is D, D is a positive integer, and in the process of solving the coverage optimization problem by adopting the multi-strategy artificial bee colony algorithm, the feasible solution corresponding to each population individual is the sensor node deployment result of the coverage optimization problem.
The process of step 1 is as follows:
step 1-1: initializing a multi-strategy artificial bee colony algorithm, wherein the method at least comprises the steps of setting initial values of population size, maximum evaluation times and improvement quantity and randomly generating initial feasible solutions of population individuals in a solution search space;
Step 1-2: and carrying out iterative updating on the feasible solutions of the population individuals based on the multi-strategy artificial bee colony algorithm until the iteration termination condition is met to obtain an approximately optimal solution, wherein each iteration is to sequentially execute the feasible solution updating at the stage of hiring bees, observing bees and reconnaissance bees.
Wherein, each round of iterative process is as follows:
and (3) a step of: firstly judging whether the employment bee stage is completely finished or not, namely, each employment bee searches and updates once based on the step, if not, the corresponding employment bee randomly selects a search strategy in a multi-strategy pool, and the feasible solution X of the current individual is updated by using the selected search strategy i If the updated feasible solution V i Is superior to the current feasible solution X i With new feasible solution V i Substitution of old solution X i Accumulating the value delta of the improvement amount successfully updated by the search strategy i The method comprises the steps of carrying out a first treatment on the surface of the If inferior to the current feasible solution X i With a certain probability p m Accept to go than the current feasible solution X i Slightly worse solution V i . If the employment bee phase is completed, an observe bee phase is performed.
And II: in the observing bee stage, judging whether the observing bee stage is completed or not, namely, each observing bee with the number of SN performs searching and updating once based on the step, if not completed, the observing bee greedily selects the maximum improvement amount delta according to the improvement amount delta of the searching strategy recorded in the hiring bee stage i Is used as the searching strategy of the stage to update X i If the updated feasible solution V i Is superior to the current feasible solution X i With new feasible solution V i Substitution of old solution X i If inferior to the current feasible solution X i With a certain probability p m Accept to go than the current feasible solution X i Slightly worse solution V i . If the employment stage has been completed, a scout stage is performed.
Thirdly,: in the stage of the reconnaissance bee, judging whether the stage of the reconnaissance bee is completely finished, namely scanning all (SN) individuals in the whole population, checking whether bees are not successfully updated by limit times, if not, solving the problem X when the updating is feasible i The dynamic threshold limit is updated unsuccessfully for times, and a global neighborhood searching mechanism is adopted to update the dynamic threshold limit; otherwise, executing the next step to judge whether the iteration termination condition is met currently.
Fourth, the method comprises the following steps: judging whether an iteration termination condition is met, and if so, outputting an approximate optimal solution; if not, continuing iteration, and entering a next hiring bee stage. I.e. the near optimal solution is the feasible solution with the largest current fitness value.
It should be understood that the implementation process of part of the steps and whether or not the part of the steps are executed, and the execution sequence may refer to the implementation process of the foregoing embodiment.
The readable storage medium is a computer readable storage medium, which may be an internal storage unit of the controller according to any one of the foregoing embodiments, for example, a hard disk or a memory of the controller. For example, the terrain feature model constructed in the invention exists in a hard disk, and then the computer program for executing the fusion step is stored in a memory, so that the fusion process is realized by depending on the memory. The readable storage medium may also be an external storage device of the controller, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the controller. Further, the readable storage medium may also include both an internal storage unit and an external storage device of the controller. The readable storage medium is used to store the computer program and other programs and data required by the controller. The readable storage medium may also be used to temporarily store data that has been output or is to be output.
Based on such understanding, the technical solution of the present invention is essentially or a part contributing to the prior art, or all or part of the technical solution may be embodied in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned readable storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Examples:
the invention is realized by using Java programs in an Eclipse platform, and performance tests are carried out.
The experimental setup was as follows: three different WSN coverage scenes are selected, simulation analysis is carried out on the SaMABC algorithm provided by the invention, and the WSN coverage optimization problem under the scenes of 40m multiplied by 40m, 50m multiplied by 50m and 100m multiplied by 100m is solved. Fig. 2 is a sensor profile of a test scenario. In three WSN coverage optimization scenes, the monitoring areas are square areas of 40m×40m, 50m×50m and 100m×100m, the number of sensors is 30, 40 and 50, and the sensing radius r of the sensors is set to be 4m, 5m and 10m. The invention respectively performs comparison test with the existing 7 optimization algorithms (PSO, ABC, GABC, GBABC, ABCVSS, ECABC, NABC), and provides a sensor node deployment diagram after optimization of each algorithm.
Fig. 3 is a convergence curve of different algorithm optimizations under WSN coverage optimization in a 40m×40m scenario to further examine the performance differences of the various algorithms. The invention is named as SaMABC. From the results in the graph, sambc already achieved very high coverage at the early stage, and the convergence rate was the fastest in all algorithms, and the final coverage result reached 85.38%. The significant increase in sambc coverage over time, compared to GBABC, shows the strong ability of the algorithm to deviate from local optima. Meanwhile, ABCVSS is also an adaptive multi-strategy improvement algorithm, and SaMABC effectively proves the effectiveness of the improvement point provided by the invention, and the optimization effect is far better than that of the improvement point.
Fig. 4 is a convergence curve of different algorithm optimizations under WSN coverage optimization in a 50m×50m scenario. From the results in the graph, the sambc algorithm has achieved a very high coverage rate in the early stage, and the convergence rate is the fastest in all algorithms, and the final coverage result reaches 95.48%. Similar coverage improvement was also shown in the middle and later stages of sambc, further demonstrating the strong ability of the algorithm to break through local optimization.
Fig. 5 is a convergence curve of different algorithm optimizations under WSN coverage optimization in a 100m×100m scenario. From the results in the graph, the coverage rate optimization of the SaMABC algorithm reaches 99% in 50000 times of evaluation, the solving precision is highest, the convergence speed is fastest, and the final coverage result reaches 99.05%. In addition, fig. 6 also shows the deployment location of the original sensor nodes, and the final deployment map after various algorithm optimizations. As can be seen from the deployment diagram in fig. 6, sambc of diagram (i) in fig. 6 has a more uniform node deployment and a larger detection coverage area than other comparison algorithms. Although sambc has a small uncovered area, in reality, nearby sensor nodes can perceive the area, showing the best coverage deployment scenario.
In conclusion, the results of three scenes show that the SaMABC has good competitiveness in the aspect of WSN coverage and good performance.
It should be emphasized that the examples described herein are illustrative rather than limiting, and that this invention is not limited to the examples described in the specific embodiments, but is capable of other embodiments in accordance with the teachings of the present invention, as long as they do not depart from the spirit and scope of the invention, whether modified or substituted, and still fall within the scope of the invention.
Claims (10)
1. A deployment method of sensor nodes in a wireless sensor network is characterized in that: the method comprises the following steps:
step 1: solving a coverage optimization problem of the wireless sensor network corresponding to the monitoring area by adopting a multi-strategy artificial bee colony algorithm to obtain an approximate optimal solution;
step 2: deploying sensor nodes in the monitoring area by using the approximate optimal solution;
the coverage optimization problem is as follows: the sensor node deployment problem that the network coverage rate in the monitoring area reaches the maximum is solved; discretizing the monitoring area into M multiplied by N monitoring points, wherein the number of sensor nodes to be deployed is D, and D is a positive integer;
in the process of solving the coverage optimization problem by adopting the multi-strategy artificial bee colony algorithm, the feasible solution corresponding to each population individual is a sensor node deployment result of the coverage optimization problem; firstly, randomly generating an initial population to obtain an initial feasible solution, and then iteratively updating the feasible solution corresponding to the population individuals to take the optimal feasible solution after the iteration termination condition is met as the approximate optimal solution;
each round of iterative updating is to sequentially execute feasible solution updating of hiring bees, observing bees and reconnaissance bees; and in the hiring bee stage, each hiring bee randomly selects a search strategy in a multi-strategy pool, and then updates a corresponding feasible solution by using the search strategy, and then accumulates the corresponding improvement amount when each search strategy successfully updates the feasible solution, so that in the observing bee stage, the feasible solution of the observing bee stage is updated by the search strategy with the largest improvement amount in the hiring bee stage; the multi-policy pool includes 2 or more search policies.
2. The deployment method of claim 1, wherein: the search strategies in the multi-strategy pool are expressed as follows:
wherein X is i The parent individuals correspond to feasible solutions before updating; v (V) i As offspring individuals, corresponding to the updated feasible solution; x is X k And X t Are all feasible solutions corresponding to a random individual in the population, and X i ≠X k ≠X t The method comprises the steps of carrying out a first treatment on the surface of the The parameter K is a variable coefficient that varies with iteration, and
is [ -1.5,1.5]Random numbers distributed uniformly, FEs are the current evaluation times, i.e. every update of the feasible solution, the current evaluation times are increased by one, maxFEs are the maximum evaluation times, gaussian (delta) 1 ,|δ 2 I) is a gaussian distribution function, δ 1 Is the central area of Gaussian distribution, delta 2 For disturbance range X best Is the current best feasible solution in the population.
3. The deployment method of claim 1, wherein: in the process of each iteration update, when the adaptability of the new feasible solution is better than that of the current feasible solution, replacing the current feasible solution with the new feasible solution. The fitness function is coverage rate of the monitoring area, and the corresponding formula is as follows:
wherein CR is A Representing the coverage of the monitored area a, P i is the set of perceived probabilities of the points covered by the sensor node i,
representing a set of points that D sensor nodes can monitor within the monitored area.
4. A deployment method according to claim 3, characterized in that: the coverage optimization problem adopts a probability perception model, and the formula of the corresponding perception probability is as follows:
wherein P is S,Q For the perception probability between the sensor node and the monitoring point, S represents the central position of the sensor node, Q represents the monitoring point in the monitoring area, lambda 1 ＝r e -r+d(S,Q)、λ 2 ＝r e +r-d(S,Q)，λ 1 、λ 2 Are defined intermediate parameters, r e For the radius fluctuation value of the uncertain detection capacity of the sensor node, r is the sensing radius of the sensor node, d (S, Q) is the Euclidean distance between S and Q, and alpha is calculated 1 、α 2 、β 1 、β 2 Is the decay coefficient of the perceptual probability, e is the natural base.
5. The deployment method of claim 1, wherein: if a feasible solution is updated unsuccessfully for a limit time, in the reconnaissance bee stage, updating the feasible solution by adopting a global neighborhood searching mechanism, wherein limit is a dynamic threshold;
the dynamic threshold limit is formulated as follows:
limit＝200·(FEs/MaxFEs)
wherein, FEs are the current evaluation times, namely, each update of the feasible solution is performed, and the current evaluation times are increased by one; maxFEs is the maximum evaluation times and is set by the initialization process of the multi-strategy artificial bee colony algorithm.
6. The deployment method of claim 5, wherein: the global neighborhood search mechanism is represented as follows:
TX i ＝r 1 ·X i +r 2 ·X best +r 3 ·(X j -X k )
Wherein TX is i R is a feasible solution updated based on a global domain search mechanism 1 、r 2 And r 3 Is [0,1 ]]Random numbers in the interval and satisfy r 1 +r 2 +r 3 ＝1，X j And X k Is a feasible solution of two random individuals in the population, X i ≠X j ≠X k ，X best Is the current best feasible solution in the population.
7. The deployment method of claim 1, wherein: in the employment stage and the observation stage, the probability p is given m Accepting a new feasible solution worse than the current feasible solution, wherein the probability p m The method comprises the following steps:
p m ＝0.1*(FEs/MaxFEs)
wherein, FEs is the current evaluation times, namely, every update of the feasible solution, the current evaluation times is increased by one, maxFEs is the maximum evaluation times, and the maximum evaluation times are set by the initialization process of the multi-strategy artificial bee colony algorithm.
8. A system based on the deployment method of any one of claims 1-7, characterized in that: comprising the following steps: a near optimal solution solving module and a deployment module;
the approximate optimal solution solving module is used for solving the coverage optimization problem of the wireless sensor network corresponding to the monitoring area by adopting a multi-strategy artificial bee colony algorithm to obtain an approximate optimal solution;
the deployment module is used for deploying sensor nodes in the monitoring area with the approximate optimal solution;
the coverage optimization problem is as follows: the method comprises the steps that the problem of deploying sensor nodes in a monitoring area with maximum network coverage rate is solved, wherein the monitoring area is discretized into M multiplied by N monitoring points, the number of the sensor nodes to be deployed is D, D is a positive integer, and each solution corresponds to a sensor node deployment result;
In the process of solving the coverage optimization problem by adopting the multi-strategy artificial bee colony algorithm, the feasible solution corresponding to each population individual is a sensor node deployment result of the coverage optimization problem; firstly, randomly generating an initial population to obtain an initial feasible solution, and then iteratively updating the feasible solution corresponding to the population individuals to take the optimal feasible solution after the iteration termination condition is met as the approximate optimal solution;
each round of iterative updating is to sequentially execute feasible solution updating of hiring bees, observing bees and reconnaissance bees; and in the hiring bee stage, each hiring bee randomly selects a search strategy in a multi-strategy pool, and then updates a corresponding feasible solution by using the search strategy, and then accumulates the corresponding improvement amount when each search strategy successfully updates the feasible solution, so that in the observing bee stage, the feasible solution of the observing bee stage is updated by the search strategy with the largest improvement amount in the hiring bee stage; the multi-policy pool includes 2 or more search policies.
9. An electronic terminal, characterized in that: at least comprises:
one or more processors;
a memory storing one or more computer programs;
wherein the processor invokes the computer program to implement:
The steps of the deployment method of any one of claims 1-7.
10. A computer-readable storage medium, characterized by: a computer program is stored, which is called by a processor to implement:
the steps of the deployment method of any one of claims 1-7.
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| CN116916475A (en) * | 2023-08-10 | 2023-10-20 | 华东交通大学 | Wireless sensor network deployment method based on multi-strategy improved badger algorithm |
| CN118211735A (en) * | 2024-05-20 | 2024-06-18 | 山东工商学院 | Desert golden mole rat optimization algorithm and application thereof |
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| CN116916475A (en) * | 2023-08-10 | 2023-10-20 | 华东交通大学 | Wireless sensor network deployment method based on multi-strategy improved badger algorithm |
| CN116916475B (en) * | 2023-08-10 | 2024-05-07 | 华东交通大学 | Wireless sensor network deployment method based on multi-strategy improved badger algorithm |
| CN118211735A (en) * | 2024-05-20 | 2024-06-18 | 山东工商学院 | Desert golden mole rat optimization algorithm and application thereof |
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