CN103648139B - Wireless sensor network node deployment method for designing based on cultural ant group algorithm - Google Patents
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Abstract
Method for designing is disposed the present invention relates to a kind of wireless sensor network node based on cultural ant group algorithm, the algorithm is included the framework of Cultural Algorithm by ant colony optimization algorithm, optimizing is carried out using population space and the double-deck genetic structure of belief space based on ant colony optimization algorithm, pheromone update strategy, taboo list are added in group space and greedy strategy etc. is added for the sparse situation of node, the speed of searching optimization of algorithm is accelerated on the whole, improving the quality for solving.It is contemplated that discussing the number for optimizing nodes under conditions of coverage rate and connectedness is ensured.
Description
Technical Field
The invention relates to a method for deploying wireless sensor network nodes, in particular to a design algorithm for deploying wireless sensor network nodes based on a culture ant colony algorithm.
Background
Wireless sensor networks are increasingly showing great utility in environmental monitoring and event tracking, both in harsh conditions and unattended. On the one hand, sensors that are inexpensive to manufacture have become technically and economically feasible; on the other hand, wireless sensor network technologies such as energy consumption, node location, and routing protocols are becoming more and more mature. Therefore, it has become a reality to deploy large-scale, ad-hoc wireless sensor networks consisting of thousands of sensor nodes. The node deployment problem was first traced to the two classical computational geometry problems of the artistic corridor proposed by O' Rourke and the circumferential coverage proposed by Williams. The two geometrical problems have important referential significance to the wireless sensor network node deployment problem.
The deployment of the wireless sensor network nodes is problematic in that the sensor nodes are arranged in a certain area through a proper strategy to meet a certain specific requirement. Coverage and connectivity constraints of wireless sensor networks are typically required to be met when deploying sensor nodes. The culture-ant system algorithm provided by the method considers the two constraint conditions at the same time, and the algorithm shows stronger stability and good performance under different environments and different parameter conditions. In addition, the number of nodes and the distribution form of the nodes are optimized when a deployment strategy is designed, limited sensor network resources are efficiently utilized, and the maximum reduction of network energy consumption is the problem to be noticed when the nodes are deployed.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the node deployment algorithm designed by the basic ant colony algorithm has the following defects that: the convergence rate is slow, the search time is long, the node deployment algorithm is prone to trapping stagnation and premature and the like, and therefore the node deployment algorithm is long in optimization time, prone to trapping local optimization, unstable in deployment result and the like. Therefore, aiming at the defects, a wireless sensor network node deployment design algorithm based on the culture ant colony algorithm is provided. On the basis of the existing research results of the ant colony algorithm, the problem of wireless sensor node deployment is better solved by using a culture frame and the ant colony algorithm.
The invention discloses a wireless sensor network node deployment algorithm based on a culture ant colony algorithm, which is mainly characterized in that the culture algorithm is added aiming at the defects of the basic ant colony algorithm and corresponding improvement measures are provided for the basic ant colony algorithm, so that the optimization rate and efficiency of the ant colony algorithm are improved. Compared with the wireless sensor network node deployment algorithm based on the ant colony algorithm, the wireless sensor network node deployment algorithm mainly comprises the following differences:
(1) and (4) introduction of a culture algorithm. The culture algorithm comprises two independent evolution spaces which influence each other: the system comprises a belief space and a population space, wherein the overall evolution mechanism of the cultural algorithm is that the population space transmits the obtained elite individuals to the belief space by adopting a certain evolution strategy, and the elite individuals evolved in the belief space influence the evolution of the population space. The method solves the problems of slow optimization speed, poor optimization result and the like in the existing algorithm.
(2) A greedy strategy. The improved strategy mainly aims at the situation that target points in the wireless sensor network are sparsely placed. In this case, the existing node deployment algorithm has a poor deployment result and shows irregular variation as the target point increases. The greedy strategy is added, so that the overall algorithm deployment result shows a regular change trend, and a better result is achieved under the condition that the target point is sparse.
(3) And (5) a convergence judgment strategy. The strategy mainly solves the problems of instability and the like of the existing algorithm, on one hand, the algorithm is quickly converged to ensure that the stability of the algorithm for optimizing each time is the same, and on the other hand, the algorithm is enabled to avoid misjudgment and obtain the optimal solution quickly.
In order to achieve the purpose, the invention discloses the following technical scheme:
a wireless sensor network node deployment design method based on a culture ant colony algorithm is characterized by comprising the following steps:
(1) designing a population space;
(2) maintaining a belief space;
(3) and (4) a convergence judgment algorithm.
Wherein the step (1) comprises the following steps:
(2. A), calculating heuristic information, and in a transfer probability formula of the ant individuals, dividing pheromone concentration in an ant systemBesides, heuristic information representing prior effect；
(2. B), maintaining the taboo table of the ants and updating the information of the working space, wherein the ant individual maintains a taboo table in the searching process to avoid repeatedly transferring to the same position; the taboo table is initially empty, after the ants make transfer selection, the taboo table needs to be updated while the sensors are added into the sensor network to the specified position, namely the taboo table is added to the position of the newly added sensor node, and then the coverage point set and the effective candidate point set are updated;
(2. C), the ant individuals in the ant colony algorithm release pheromones at the positions where the ant individuals pass through, but the pheromones are evaporated along with the change of time and environment, and the pheromone on the point j in the working space is updated according to the formula:wherein,ρrepresents the evaporation coefficient of the pheromone, andrepresenting the increment of pheromone left on a certain grid point after the ants pass through the grid point; whileIt plays a role as a guide, and is defined as follows:
whereinQis a constant, which represents the number of traces left by ants, a variablesenserUsedIs used for representing antskThis iteration uses the number of sensors.
The step (2) comprises the following steps:
(3. A), initializing a belief space, and executing an acceptance function or an influence function on a generation population according to a rule by a culture algorithm after the generation population is generated: when the receiving function is executed, excellent individuals in the population can enter a belief space of the culture algorithm; when the influence function is executed, a number of excellent individuals in the belief space replace the poor individuals in the current population.
And (3. B), receiving a function, wherein the receiving function is mainly used for transmitting the elite ant individuals obtained by comparison in the population space to a belief space. The invention adopts a strategy of periodic update, namely, in the evolution process of the population space, when the ant colony optimization algorithm runs multiple generations of Acceptstep, the globally optimal individual in the population space is selected to replace the worst individual in the belief space. Here, the AcceptStep value is 2.
(3. C), an influence function, wherein the influence function refers to the upper-layer belief space, after the iteration update of the elite solution set is completed, the upper-layer belief space is fed back to the lower-layer population space to guide the subsequent evolution direction of the lower-layer population space, a dynamic strategy is adopted for influencing the time of the population, a certain number of individuals with better adaptation values in the belief space are replaced by the same number of individuals with worse adaptation values in the population space when the population runs a multiple generation of the infiuestep in the belief space by setting a dynamically changed variable infiuestep, and the infiuestep is represented by the following formula:
wherein N is1、N2Is a constant number of times, and is,iter maxrepresenting the maximum number of iterations of the algorithm; and Currentstep is the space evolution algebra of the current population. Here N1 and N2 take the values 2 and 5, respectively.
The step (3) comprises the following steps:
(4. A), calculating the variance sigma of the fitness value of each ant individual in the belief space, namely calculating the variance of the number of required sensor nodes in the wireless sensor network corresponding to each individual in the belief space, and if the sigma is less than a standard valueIf yes, counting is carried out, and step 2 is executed;
(4. B), if there is still σ in the later execution of the accepted function<The counter i is accumulated by 1, and the step 3 is executed; otherwise, the counter is reset after the counting process is finished, and the step 1 is returned;
and (4. C), if the counter I is larger than a set constant value I, the algorithm is converged, and a proper value I can effectively avoid misjudgment in the early stage of the algorithm and use a novel wireless sensor network node deployment design algorithm based on the culture ant colony algorithm to better solve the wireless sensor node deployment problem.
The more detailed method of the invention is as follows:
(1) transfer rule of ant individuals
When the ant individuals in the ant system construct solutions step by step, the transfer rules of the next step are selected according to certain probability. The ant's choice of transfer rules is expressed below.
Ant k at node i, selects node j according to the following rule:
wherein r is-U (0,1),∈[0,1]setting specific parameter value by user, J ∈(t) is a node randomly selected according to the following probability:
wherein(t) is a set of optional nodes. In the above formula, the first and second carbon atoms are,representing the posterior effect of moving from node i to node j, representing the current candidate point in the node deployment problemPheromone concentrations at candidate points in the set;representing the prior effect of moving from the node i to the node j, and calculating through heuristic information to obtain the number of monitoring points near a certain candidate point represented in the node deployment problem.
Andrelative coefficients representing both "pheromone" effects and heuristic information. According to the theory of the ant colony algorithm,andthe relative importance between the past experience data and the independent search is shown.
(2) Calculation of heuristic information
In the transfer probability formula of the ant individual, except the pheromone concentration in the ant systemBesides, heuristic information representing prior effectThe heuristic information is calculated as follows:
when the ant individual selects the next transfer node, the ant individual judges the effective point set (W->E) Whether the coverage point set is empty or not, if the coverage point set is empty, searching a transfer target in the coverage point set, and calculatingSetting the value to be 1, if not, searching the transfer target in the effective point set,the number of monitoring points covered by the node is set.
(3) Greedy strategy under sparse monitoring point condition
Working space
The grid-based network model constructs a spatial region (i.e., a grid) for running the algorithm.
Monitoring point
And nodes which are required to be covered by the sensor nodes and are positioned at the intersection positions of the grid lines in the working space. The set of watchpoints is represented by a doublet C (M, H), where M represents the set of all watchpoints, and H represents the set of watchpoints that have been covered in M, i.e., the set of covered watchpoints.
If the monitoring points scattered in the working space are sparse, the situation that the effective point set E to be selected is empty can occur when the ant individual transfers, at the moment, heuristic information cannot be calculated, and a greedy strategy is adopted in the method, so that the algorithm can find new effective points as soon as possible.
Distance of point to network
The minimum of the distances from a point in the workspace to each sensor in the sensor network is called the point-to-network distance.
The main algorithm steps are as follows:
firstly, calculating the distance from each point in a coverage point set A of a working space to a current network;
secondly, finding a coverage point set A with the maximum distance from the current network;
and thirdly, selecting the point with the maximum pheromone concentration as a target point for next transfer.
(4) Maintenance of taboo list of ants and workspace information update
Effective point
The point is a point in the formed network communication range of the working space, and if a sensor is placed on the point, the sensor at least covers a monitoring point which is not covered before, the point is a valid candidate point.
The ant individual maintains a tabu table in the searching process to avoid repeatedly transferring to the same position. The taboo list is initially empty, and after ants make transfer selection, the taboo list needs to be updated while the sensors are added into the sensor network to the specified position. Firstly, ants add the positions of the newly added sensor nodes into a tabu table, and then the ants update the coverage point set and the effective point set in the working space. As new nodes are added, the coverage point set is continuously increased, the change of the effective point set is complex, and the new nodes may cause the old effective points to fail and bring a plurality of new effective points.
(5) Pheromone updating strategy of ant system
In the ant colony algorithm, an ant individual releases pheromone at a place where the ant individual passes through, but the pheromone evaporates along with the change of time and environment, and an pheromone updating formula on a point j in a working space is as follows:
whereinρwhich represents the evaporation coefficient of the pheromone,representing the increment of pheromone left at a certain grid point by an ant after passing through the point. It can thus be seen that the pheromones on good grid points will slowly increase, whereas those on bad grid points will decrease to disappear, thus attracting subsequent ants to continue searching for a better solution along good grid points, and thusAnd plays a guiding role.WhereinQis a constant that represents the number of traces left by the ant. Variables ofsenserUsedIs used for representing antskThis iteration uses the number of sensors.
In order to avoid the unlimited accumulation of pheromone concentration values in the pheromone updating process, the limitation on the pheromone concentration is added in the algorithm so as to avoid premature convergence. By doing so, the attraction of the points with high pheromone concentration to the individual ants can be limited, and the attraction of the points with low pheromone concentration to the ants can be ensured not to be too small. The pheromone update limit is shown as follows:
wherein variables are usedThe concentration of the pheromone at the grid points,pher_MINandpher_MAXrepresenting the minimum and maximum values of the pheromone.
(6) Initialization of belief spaces
The individual in the belief space is obtained by receiving the superior individual in the group space, the belief space compares and optimizes the individual and the original individual according to a certain rule, and updates the empirical knowledge of the belief space, thereby guiding the evolution direction of the ant individual in the group space and improving the evolution efficiency of the group space.
After the generation population is generated, the culture algorithm executes an acceptance function or an influence function according to the rule. When the receiving function is executed, excellent individuals in the population can enter a belief space of the culture algorithm; when the influence function is executed, a number of excellent individuals in the belief space replace the poor individuals in the current population.
After a plurality of generations, usually, good individuals in the population tend to be similar, namely, the algorithm converges, or the algorithm reaches the maximum generation number, the generation of the new generation population is stopped, and the obtained optimal solution is output.
In the algorithm, a List List is used as a belief space of the culture algorithm, and the List is materialized to finish the initialization work of the belief space. The belief space exists relatively independently of the population space, however, the belief space is longer than the population space life cycle because the output of the algorithm herein is the optimal solution from the belief space.
(7) Receiving function
The receiving function is mainly used for transmitting the elite ant individuals obtained by comparison in the population space to the belief space. Here, a strategy of periodic update is adopted, namely in the evolution process of the population space, when the ant colony optimization algorithm runs multiple generations of AcceptStep, the globally optimal individual in the population space is selected to replace the worst individual in the belief space. Here, the AcceptStep value is 2.
(8) Influence function
The influence function refers to that the upper layer belief space feeds back to the lower layer population space to guide the subsequent evolution direction after the iterative update of the elite solution set is completed. The method adopts a strategy of dynamically selecting the influence opportunity, so that the influence of the belief space on the population space is increased along with the increase of the evolution algebra. The strategy for dynamically selecting the influence opportunity is specifically realized as follows: by setting a dynamically changing variable, a certain number of individuals with better adaptation values in the belief space population are replaced by the same number of individuals with worse adaptation values in the population space every time the population runs for a generation in the belief space. The value of infiluestep is calculated by:wherein, N1 and N2 are constants and are the maximum evolution algebra of the preset algorithm; and Currentstep is the space evolution algebra of the current population. This is achieved byAnd the values of the inner N1 and the N2 are 2 and 5 respectively.
(9) Convergence decision algorithm
The periodic updating of the individuals in the belief space enables the list to represent the most recently appearing optimal individuals in the group space, and the algorithm analyzes the belief space to judge the convergence of the algorithm. The algorithm executes the decision algorithm each time after the received function is executed. The algorithm is described as follows:
first, the variance σ of each individual evaluation value in the belief space, that is, the variance of the number of nodes used by the wireless network in the belief space is calculated. If the sigma is smaller than a standard valueThen the counting process is started and the second step is performed.
Second, if there is still σ in the later execution of the accepted function<The counter i accumulates 1 and executes the third step; otherwise, the counter is cleared to zero in the counting process, and the first step is returned.
And thirdly, if the counter I is larger than a set constant value I, setting the flag to false, and indicating that the algorithm converges.
(10) Constructing a tree topology of a sensor network
The problem to be solved is to initialize and deploy a wireless sensor network, the output of an algorithm is a network, and the network is composed of a node set and an edge set, so that when an ant individual organizes a solution, a tree-shaped network is established by adopting a corresponding routing mechanism. The routing algorithm is executed after the ant individuals perform the transfer operation, and the algorithm is described as follows:
firstly, scanning an area which takes a newly added node as a circle center and takes a node communication radius as a radius, if other nodes exist in the area, recording the set of the nodes as V, and executing the step 2, if no other nodes exist in the area, making an algorithm go wrong;
secondly, searching a node closest to the newly added node in the V, if the node has the only closest node, establishing an edge between the newly added node and the closest node, and if the node does not have the only closest node, executing the step 3;
and thirdly, searching a non-leaf node in the tree network in the nearest node set, if the non-leaf node is found, establishing an edge between the newly added node and the nearest node, otherwise, randomly establishing an edge with one node in the nearest node set.
Compared with the prior art, the wireless sensor network node deployment algorithm based on the culture ant colony algorithm has the positive effects that:
(1) the number of sensors deployed by the algorithm is steadily increased along with the increase of the number of monitoring points, and the quality of the solution is superior to the deployment result of the existing algorithm. The deployment effect is more obvious in the case of sparse target points.
(2) The average iteration times required by the existing algorithm for searching to obtain the optimal solution are obviously higher than that of the algorithm, so that the algorithm can search to obtain the optimal solution more quickly under given conditions.
(3) The ant colony algorithm in the algorithm can search the global optimal solution more quickly, and meanwhile, the stability of the algorithm is further improved.
Drawings
FIG. 1 is a basic framework diagram of the cultural ant colony algorithm of the present invention;
FIG. 2 is a workspace diagram of the present invention;
FIG. 3 is a diagram of a valid candidate set E;
fig. 4 is a graph of the effect of R =48 experiments;
fig. 5 is a graph of experimental effect of R = 72;
fig. 6 is a graph of the effect of the R =96 experiment.
Detailed Description
The present invention is further described below with reference to examples. The scope of the invention is not limited by these examples, which are set forth in the following claims.
Example 1
Wireless sensor network node deployment design system based on culture ant colony algorithm includes:
(1) designing a population space;
(2) maintaining a belief space;
(3) and (4) a convergence judgment algorithm.
The step (1) comprises the following steps:
(2. A), calculating heuristic information, and in a transfer probability formula of the ant individuals, dividing pheromone concentration in an ant systemBesides, heuristic information representing prior effect；
(2. B), maintaining the taboo table of the ants and updating the information of the working space, wherein the ant individual maintains a taboo table in the searching process to avoid repeatedly transferring to the same position; the taboo table is initially empty, after the ants make transfer selection, the taboo table needs to be updated while the sensors are added into the sensor network to the specified position, namely the taboo table is added to the position of the newly added sensor node, and then the coverage point set and the effective candidate point set are updated;
（2c), the ant individuals in the ant colony algorithm release pheromones at the places where the ant individuals pass through, but the pheromones are evaporated along with the change of time and environment, and the pheromone updating formula on the point j in the working space is as follows:whereinρrepresents the evaporation coefficient of the pheromone, andrepresenting the increment of pheromone left on a certain grid point after the ants pass through the grid point; whileIt plays a role as a guide, and is defined as follows:
whereinQis a constant, which represents the number of traces left by ants, a variablesenserUsedIs used for representing antskThis iteration uses the number of sensors.
The step (2) comprises the following steps:
(3. A), initializing a belief space, and executing an acceptance function or an influence function on a generation population according to a rule by a culture algorithm after the generation population is generated: when the receiving function is executed, excellent individuals in the population can enter a belief space of the culture algorithm; when the influence function is executed, a number of excellent individuals in the belief space replace the poor individuals in the current population.
And (3. B), receiving a function, wherein the receiving function is mainly used for transmitting the elite ant individuals obtained by comparison in the population space to a belief space. The invention adopts a strategy of periodic update, namely, in the evolution process of the population space, when the ant colony optimization algorithm runs multiple generations of Acceptstep, the globally optimal individual in the population space is selected to replace the worst individual in the belief space. Here, the AcceptStep value is 2.
(3. C), an influence function, wherein the influence function refers to the upper-layer belief space, after the iteration update of the elite solution set is completed, the upper-layer belief space is fed back to the lower-layer population space to guide the subsequent evolution direction of the lower-layer population space, a dynamic strategy is adopted for influencing the time of the population, a certain number of individuals with better adaptation values in the belief space are replaced by the same number of individuals with worse adaptation values in the population space when the population runs a multiple generation of the infiuestep in the belief space by setting a dynamically changed variable infiuestep, and the infiuestep is represented by the following formula:
wherein N is1、N2Is a constant number of times, and is,iter maxrepresenting the maximum number of iterations of the algorithm; and Currentstep is the space evolution algebra of the current population. Here N1 and N2 take the values 2 and 5, respectively.
The step (3) comprises the following steps:
(4. A), calculating the variance sigma of the fitness value of each ant individual in the belief space, namely calculating the variance of the number of required sensor nodes in the wireless sensor network corresponding to each individual in the belief space, and if the sigma is less than a standard valueIf yes, counting is carried out, and step 2 is executed;
(4. B), if there is still σ in the later execution of the accepted function<The counter i is accumulated by 1, and the step 3 is executed; otherwise, the counter is reset after the counting process is finished, and the step 1 is returned;
and (4. C), if the counter I is larger than a set constant value I, the algorithm is converged, and a proper value I can effectively avoid misjudgment in the early stage of the algorithm.
Example 2
(1) Key parameter setting
In order to verify the feasibility and the effectiveness of the CA-ACA algorithm, the existing typical Easidesign algorithm for solving the wireless sensor node deployment problem based on the ant colony algorithm is selected to be compared with the ACO-TCAT. The algorithm adopts Java to simulate, the grid size is set to be 24, the grid scale is set to be 20 x 20, and monitoring points are generated in a pseudo-random mode.
(2) Wireless sensor network node deployment design algorithm based on culture ant colony algorithm
An important objective of designing a node deployment algorithm is to obtain an optimal deployment scheme meeting conditions for reasonable network requirements, and the algorithm has strong optimizing capability. Here, the sensor communication radii are set to R =48,72, and 96, respectively. After the three algorithms (CA-ACA, Easidesign, ACO _ TCAT) are respectively operated for 20 times corresponding to different communication radiuses R, the average data statistical result is obtained.
Fig. 4, 5, and 6 in the experimental results show the relationship between the deployment number of sensors and the number of monitoring points in the workspace under the condition that the sensor sensing radius = the sensor communication radius =48,72, and 96, respectively. The horizontal axis represents the number of monitoring points, and the vertical axis represents the average number of sensor nodes required by the node deployment searched by the corresponding algorithm. It can be seen that in three cases, the number of sensors deployed by the CA-ACA is steadily increased along with the increase of the number of monitoring points, and the quality of the solution is superior to ACO-TCAT and more superior to Easidesign. Under the condition that the monitoring points are sparse (for example, the number of the monitoring points is less than or equal to 80), Easidesign cannot obtain a valuable solution under the sparse environment of the monitoring points because of no adaptive mechanism, and the solution of the ACO-TCAT in the figure 5 has an obvious difference with the CA-ACA. And a greedy strategy is added to the CA-ACA aiming at the sparse situation, so that the solution of the CA-ACA is superior to that of the other two algorithms. For the dense condition of the monitoring points, the difference between the three algorithms is small but the CA-ACA is relatively excellent as the number of the monitoring points increases. In conclusion, the CA-ACA can well optimize the number of the sensor nodes under the condition that the monitoring points are sparse and dense.
Example 3
In an application example of the invention, such as monitoring of some flammable areas in cities, compared with the conventional method, the application effect is as follows:
the conventional method comprises the following steps: wireless sensor network node deployment algorithm based on basic ant colony algorithm
The method comprises the following steps: wireless sensor network node deployment algorithm based on culture ant colony algorithm
TABLE 1
| Target point | Cultural ant colony | Basic ant colony |
| 40 | 54 | 97 |
| 80 | 63 | 76 |
| 120 | 66 | 72 |
| 160 | 69 | 70 |
| 200 | 73 | 73 |
TABLE 2
And (4) conclusion:
(1) as shown in tables 1 and 2, the algorithm shows strong stability and good performance under different environments and different parameters.
(2) The number of nodes and the node distribution form are optimized during the design of the deployment strategy, limited sensor network resources are efficiently utilized, and the deployment cost is reduced to the maximum extent, as shown in table 1, the deployment cost required by the method is much lower than that required by the conventional method, and the data presentation stability is increased.
(3) The invention realizes the optimization of the number of nodes in the network under the condition of ensuring the coverage rate and the connectivity.
Claims (1)
1.A wireless sensor network node deployment design method based on a culture ant colony algorithm is characterized by comprising the following steps:
(1) designing a population space;
(2) maintaining a belief space;
(3) a convergence decision algorithm;
the step (1) comprises the following steps:
(1. A), calculating heuristic information, and in a transfer probability formula of the ant individuals, dividing pheromone concentration in an ant systemHeuristic information representing prior effect
(1, B), maintaining the taboo table of the ants and updating the information of the working space, wherein the ant individual maintains a taboo table in the searching process to avoid repeatedly transferring to the same position; the taboo table is initially empty, after the ants make transfer selection, the taboo table needs to be updated while the sensors are added into the sensor network to the specified position, namely the taboo table is added to the position of the newly added sensor node, and then the coverage point set and the effective candidate point set are updated;
(1. C), in the ant colony algorithm, the ant individuals release pheromone at the place where the ant individuals pass through, but the pheromone evaporates along with the change of time and environment, and the working space is in the middleThe above pheromone update formula is:wherein,which represents the evaporation coefficient of the pheromone,representing antsThe increment of the pheromone left at a certain grid point after passing the grid point; whileIt plays a role as a guide, and is defined as follows:
whereinis a constant, which represents the number of traces left by ants, a variableIs used for representing antsThe number of sensors used in the iteration;
the step (2) comprises the following steps:
(2. A), initializing a belief space, and executing an acceptance function or an influence function on a generation population according to a rule by a culture algorithm after the generation population is generated: when the receiving function is executed, excellent individuals in the population can enter a belief space of the culture algorithm; when the influence function is executed, a plurality of excellent individuals in the belief space replace poor individuals in the current population;
(2. B), an acceptance function, wherein the acceptance function is mainly used for transferring elite ant individuals obtained by comparison in the population space to the belief space and adopting a strategy of periodic update, namely in the evolution process of the population space, when the ant colony optimization algorithm runs multiple generations of Acceptstep, selecting the globally optimal individual in the population space to replace the worst individual in the belief space;
here, the value of AcceptStep is 2;
(2. C), an influence function, wherein the influence function refers to the fact that after iteration updating of an elite solution set is completed in an upper layer belief space, the upper layer belief space is fed back to a lower layer population space to guide the subsequent evolution direction of the lower layer population space, a dynamic strategy is adopted for influencing the timing of the population, a certain number of individuals with better adaptation values in the belief space are replaced by the same number of individuals with worse adaptation values in the population space when the population runs a multiple generation of the infiuestep in the belief space by setting a dynamically changed variable infiuestep, and the infiuestep is represented by the following formula:
wherein
is a constant number of times, and is,representing the maximum number of iterations of the algorithm; the Currentstep is a current population space evolution algebra;
where N1 and N2 take the values of 2 and 5, respectively; the step (3) comprises the following steps:
(3. A), calculating the variance of the individual fitness value of each ant in the belief spaceNamely, calculating the variance of the number of required sensor nodes in the wireless sensor network corresponding to each individual in the belief space, if the variance is equal to the number of required sensor nodes in the wireless sensor network corresponding to each individual in the belief spaceLess than a standard valueCounting is carried out, and step 2 is executed;
(3. B), if there is still a function execution process in the following acceptanceThe counter m increments by 1 and step 3 is executed; otherwise, the counter is reset after the counting process is finished, and the step 1 is returned;
and (3, C), if the counter m is larger than a set constant value I, the algorithm is converged, and a proper value I can effectively avoid misjudgment in the early stage of the algorithm.
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