CN116828484B - Sensor network coverage optimization method based on improved species life-to-life optimization algorithm - Google Patents

Sensor network coverage optimization method based on improved species life-to-life optimization algorithm Download PDF

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CN116828484B
CN116828484B CN202311059852.3A CN202311059852A CN116828484B CN 116828484 B CN116828484 B CN 116828484B CN 202311059852 A CN202311059852 A CN 202311059852A CN 116828484 B CN116828484 B CN 116828484B
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杨永建
甘轶
程嗣怡
申林
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Air Force Engineering University of PLA
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Abstract

A method for optimizing sensor network coverage using an improved specie life-to-death algorithm is provided, the method comprising the steps of: establishing a sensor probability coverage model; clustering; optimizing the position deployment of the rest dynamic sensor nodes by adopting an SED-PSO algorithm; and outputting an optimal position deployment result of the sensor node. The method is used for optimizing the coverage rate of the sensor network, can effectively realize the coverage optimization of the sensor network based on the probability coverage model, and has the advantages of high convergence rate, high stability and high solution quality of the algorithm.

Description

Sensor network coverage optimization method based on improved species life-to-life optimization algorithm
Technical Field
The invention relates to a sensor network coverage optimization technology, in particular to a method for optimizing the coverage rate of a sensor network by utilizing an improved species life-to-death algorithm.
Background
Coverage rate is a measure of detection of a monitoring area of a sensor network and reflects the quality of perceived service that the sensor network can provide. At present, the optimization method for the sensor network coverage is mainly based on a binary coverage model (a disc model or a 0-1 model), and two types of sensor network coverage methods are formed: firstly, a sensor network coverage method based on geometry is provided, and the method solves the optimal position deployment of nodes through the geometrical relationship among the nodes; secondly, a sensor network coverage method based on a group intelligent optimization algorithm is adopted, so that complicated geometric deduction is avoided by adopting the group intelligent optimization algorithm, but the problems of easy local optimum, premature convergence, large iteration times and the like exist.
Because the coverage model of the sensor is not a theoretical disc coverage model, but a probability coverage model, but the coverage optimization of the sensor network based on the probability coverage model is often complex and difficult to apply a geometric method, the position deployment of the sensor nodes is simpler, more convenient and easier by adopting a group intelligent optimization algorithm for the sensor network coverage optimization aiming at the probability coverage model conforming to the actual detection of the sensor.
The species life-to-death algorithm (system engineering and electronic technology, 2018, 40 (4): 941-947) is a new evolutionary computing (evolutionary computing, EC) algorithm which is proposed by the inventor according to the large explosion and large extinction phenomena of the species which have been found in the biological evolution history and by referring to the thought of the species catastrophe evolution theory, realizes optimization by executing the large explosion and large extinction on the species, achieves the purpose of balancing the global optimizing capability and the local optimizing capability of the algorithm by introducing strategies such as main branch transfer, species derivative capability contraction and the like, and has higher quality optimal solution and higher stability.
Disclosure of Invention
In order to solve the problem that the geometrical optimization method is difficult to adopt in the sensor network coverage optimization based on the probability coverage model, and simultaneously in order to further improve the optimization performance of the species life-to-death (species explode AND DERACINATE, SED) algorithm, the invention provides a sensor network coverage optimization method based on an improved species life-to-death optimization algorithm, which specifically comprises the following steps:
the first step: establishing a sensor probability coverage model shown in a formula (1);
（1）
Wherein, Representing the perceived probability of the jth sensor for an object at a distance R, the sensor perceived radius R s, the uncertainty perceived range delta x R s, delta being a coefficient less than 1,/>Representing the distance between the target T and the sensor S j, x t、yt representing the coordinates at the target point,/>, respectively、/>Respectively representing coordinates of the sensor S j; lambda 1、λ2、β1、β2 is a parameter related to the sensor characteristics, alpha 1、α2 is a first and second input parameter, respectively, the values of which are related to r s and d (S j, T), the relation being
（2）
And a second step of: clustering; clustering the static sensors by adopting a K-means algorithm, and dividing all sensor nodes into 4 clusters; a dynamic sensor is respectively arranged at the central position of each cluster and the central position of the optimization area to serve as a cluster head and a network central point;
And a third step of: optimizing the position deployment of the rest dynamic sensor nodes by adopting an SED-PSO algorithm; the SED-PSO algorithm comprises the following steps:
(1) Setting parameters of a species life-to-death algorithm, comprising: the number S of surviving species, the derivatization capability A of the species, the generation number G of the species, the explosion multiple M of the species, the contraction coefficient alpha and the iteration number FE max of the algorithm;
(2) Setting PSO algorithm parameters, including: population size P, first learning factor c 1 and second learning factor c 2, inertial weight factor omega, maximum positive movement speed v max and maximum negative movement speed v -max of particles, and iterative times iter of algorithm;
(3) Establishing an adaptability function; the fitness function of the sensor network coverage based on the improved species life-to-life optimization algorithm is the reciprocal of coverage;
(4) Initializing a species life-to-death algorithm in a randomizing mode according to the range of the optimized region, and calculating the fitness value of each species; each dimension of each species initializes the formula:
(4)
(5)
Wherein X s,d represents the value of the d dimension of the S-th species, a s represents the derivatization capacity of the S-th species, s=1, 2, …, S represents the S-th surviving species; d=1, 2, …, D representing the D-th dimension of the optimization problem, D representing the dimension of the optimization problem, rand representing the random number between (0, 1); [ L min, Lmax ] represents a search interval, and L min, Lmax is the lower limit and the upper limit of the search interval respectively; a epsilon [ A min, Amax],Amax ] represents the maximum species derivatization capacity, and A min is more than or equal to 0 represents the minimum species derivatization capacity;
(5) Performing an outbreak on each surviving species; the generation of new species at each major burst is shown in the following formula;
(6)
(7)
Wherein, Representing the new species derived from the s-th species in the kth optimization at the j-th outbreak,/>The s-th surviving species expressed in the kth optimization, rand is AND/>Random vector between (0, 1) of the same dimension,/>Representing the derivatization ability of the s-th species in the kth optimization,/>Representing the derivatization capacity of a new species derivatized by a jth outbreak of an s-th species in a kth optimization, j=1, 2, …, M representing a jth outbreak;
(6) Judging and processing out-of-range of new species generated by each burst; each dimension of the species generated by the outbreak should have a value within the interval [ L min, Lmax ], and if not, the dimension is beyond the boundary; replacing the dimension value of the out-of-range species with a random number in a [ L min, Lmax ] interval generated by a random function; if the boundary is not crossed, no treatment is needed;
(7) Performing deep iteration on new species generated by each burst by adopting a PSO algorithm;
① Based on new species generated by explosion, generating N particles, wherein the generation formula of each dimension of each particle is as follows:
(8)
(9)
Wherein, Value representing the d-th dimension of the i-th particle,/>A speed value representing the d-th dimension of the i-th particle, i=1, 2, …, N representing the i-th particle; /(I)Representation/>D-th dimensional value of (2);
② Carrying out boundary crossing judgment and treatment on each particle; each dimension of each particle has a value within the interval [ L min, Lmax ], and if not, the dimension is beyond the boundary; replacing the dimension value of the out-of-range particles with a random number in a [ L min, Lmax ] interval generated by a random function; if the boundary is not crossed, no treatment is needed;
③ According to the established fitness function, calculating fitness values of N particles, selecting a global optimal solution g, and enabling the particle self optimal solution to be the current value of the particle;
④ Updating each dimension of each particle velocity according to the following formula;
(10)
wherein ω is inertial weight; d-th dimension of speed at t-th iteration of the ith particle,/> Is the d dimension of the self history optimal position of the ith particle at the t time of iteration,/>Is the d dimension of the position of the ith particle at the t iteration,The d dimension of the optimal position of the group at the t-th iteration; and c 1 and c 2 are acceleration factors, which are constants greater than 0, and are used for controlling the influence of the particle history optimal position and the overall optimal particle position of the population on the current particle speed. r 1 and r 2 are random numbers between (0, 1);
Judging whether the speed is in the interval of [ - (L max-Lmin),( Lmax - Lmin) ] or not, if not, replacing the speed by using a random function to generate random numbers in the interval of [ - (L max - Lmin), (Lmax - Lmin) ] and if so, not processing;
updating the position of the particles according to the following formula;
(11)
Judging whether each dimension of each updated particle is in the [ L min, Lmax ] interval, if not, replacing the updated dimension by a random number in the [ L min, Lmax ] interval generated by a random function; if the interval is within the interval, no processing is needed;
⑤ After the particle speed and the position are updated each time, calculating an fitness function value of the updated particle, if the fitness function value is smaller than the fitness function value of the historical optimal solution of the particle, replacing the historical optimal solution of the particle with the position of the updated particle, otherwise, keeping the historical optimal solution of the particle unchanged;
After all particles complete the position updating of the iteration, selecting the particle with the smallest fitness function value of all updated particles, comparing the fitness value of the particle with the fitness value of the global optimal solution, and if the fitness value of the particle is smaller, replacing the global optimal solution with the position of the particle; otherwise, the global optimal solution remains unchanged;
⑥ Let t=t+1 and repeat ③－⑤ steps until iter iterations are completed;
⑦ Replacing the value of the new species generated by the current burst operation with the iterated global optimal solution g iter, namely: ；
(8) Calculating and comparing fitness function values of the species after and before the explosion, and marking the new species as the main species if the fitness value of the new species is smaller, otherwise, the old species as the main species; only the main species can perform the large burst operation; if a major transfer occurs, the generation number G of the species is increased by 1, and the derivatization capacity of the major species is updated to the derivatization capacity of the new species as shown in the following formula;
(12)
(13)
(9) Repeating the steps (5) to (8) until M bursts are completed;
(10) Sequencing the fitness function values of all new species generated by M bursts and existing species before the bursts, selecting S species with the optimal fitness value as metaplasia species for the next large burst and large extinction, and contracting the derivative capacity of surviving species according to the following formula, wherein all other species are eliminated;
(14)
Wherein, Is the coefficient of contraction;
(11) Repeating the steps (5) to (10) until a termination condition is reached (the termination condition is generally that the maximum iteration number is reached or the required optimizing precision is reached);
fourth step: and outputting an optimal position deployment result of the sensor node.
In one embodiment of the invention, in the third step (3), the coverage is calculated as follows: dividing a monitoring area into [100×100] grids according to granularity g=40m, and calculating the proportion of the grids with the sensor network sensing probability of more than p th =0.6 in the center of the grids to all the grids; the sensing probability of the sensor network is as follows
（3）。
In one embodiment of the invention, the parameters are shown in Table 1;
TABLE 1 scene and algorithm parameters
Scene and algorithm Parameters (parameters)
Monitoring area: 4km×4km Lmin=0,Lmax=4km；
Number of nodes: 38 sensor nodes, wherein 20 sensor nodes are static sensors, and the rest are dynamic sensors; D=（38-20-4-1）×2=13×2=26；
Probability coverage model parameters of (1) rs=1.2km,δ＝0.6,λ1=1,λ2=0.6,β1=3,β2=2；
SED-PSO algorithm M=20，S=30，α=0.05，FEmax=2000,N=2,ω=0.724,c1=c2=2，iter=5；
According to the invention, an SED algorithm and a Particle Swarm Optimization (PSO) algorithm are combined, an improved SED algorithm fused with the PSO algorithm is provided, the SED algorithm is recorded as an SED-PSO algorithm, and a sensor network coverage optimization method based on an improved species life-to-life optimization algorithm is provided based on a sensor probability coverage model.
Drawings
FIG. 1 shows a schematic flow diagram of a sensor network coverage optimization method based on an improved species life-to-life optimization algorithm;
FIG. 2 shows a sensor node location profile after optimization using the SED-PSO algorithm;
FIG. 3 shows sensor network coverage and coverage after optimization using the SED-PSO algorithm;
FIG. 4 shows the average coverage over the number of iterations for 30 Monte Carlo (Monte-Carlo) trials;
FIG. 5 shows the results of optimization of 30 Monte-Carlo experiments.
Detailed Description
The present invention is described in detail below with reference to the accompanying drawings.
The detailed steps of the sensor network coverage optimization method based on the improved species life-to-death optimization algorithm are as follows.
The first step: a probabilistic coverage model of the sensor is established as shown in formula (1) (the coverage model can also be established according to the actual sensing condition of the sensor).
（1）
Wherein,Representing the perceived probability of the jth sensor for an object at a distance R, the sensor perceived radius R s, the uncertainty perceived range delta x R s, delta being a coefficient less than 1, beta 1、β2 being a parameter related to the sensor characteristics, alpha 1、α2 being the first and second input parameters, respectively, the values/>Representing the distance between the target T and the sensor S j, x t、yt representing the coordinates at the target point,/>, respectively、/>The coordinates of the sensor S j are indicated respectively. Lambda 1、λ2, related to r s and d (S j, T), the relation of which is as follows
（2）
And a second step of: clustering. And clustering the static sensors by adopting a K-means (K-means) algorithm, and dividing all sensor nodes into 4 clusters. And arranging a dynamic sensor at the central position of each cluster and the central position of the optimized area as a cluster head and a network central point.
And a third step of: and optimizing the position deployment of the rest dynamic sensor nodes by adopting an SED-PSO algorithm. The SED-PSO algorithm comprises the following steps:
(1) Setting parameters of a species life-stop algorithm, comprising: number of surviving species S, derivatization ability of species a, number of generation of species G, number of outbreaks of species M, coefficient of contraction α, number of iterations of algorithm FE max.
(2) Setting parameters of a PSO algorithm, including: the population size P, the first and second learning factors c 1 and c 2, the inertial weight factor ω, the maximum positive velocity v max and the maximum negative velocity v -max of the particles, the number of iterations iter of the algorithm.
(3) And establishing an adaptability function. The fitness function of the sensor network coverage based on the improved species life-stop optimization algorithm is the reciprocal of the coverage rate. The coverage is calculated as follows: dividing a monitoring area into [100×100] grids according to granularity g=40m, and calculating the proportion of the grids with the sensor network sensing probability of more than p th =0.6 at the center of the grids to all the grids. The sensing probability of the sensor network is as follows
（3）
(4) Initializing a species life-to-death algorithm in a randomization mode according to the range of the optimized region, and calculating the fitness value of each species; each dimension of each species initializes the formula:
(4)
(5)
wherein X s,d represents the value of the d dimension of the S-th species, a s represents the derivatization capacity of the S-th species, s=1, 2, …, S represents the S-th surviving species; d=1, 2, …, D representing the D-th dimension of the optimization problem, D representing the dimension of the optimization problem, rand representing the random number between (0, 1); [ L min, Lmax ] represents a search interval, and L min, Lmax is the lower limit and the upper limit of the search interval, respectively. A.epsilon.A min, Amax],Amax represents the maximum species derivatization capacity and A min.gtoreq.0 represents the minimum species derivatization capacity.
(5) Performing an outbreak on each surviving species; the generation of new species at each major burst is shown in the following formula.
(6)
(7)
Wherein,Representing the new species derived from the s-th species in the kth optimization at the j-th outbreak,/>The s-th surviving species expressed in the kth optimization, rand is AND/>Random vector between (0, 1) of the same dimension,/>Representing the derivatization ability of the s-th species in the kth optimization,/>Represents the derivatization ability of the new species derivatized by the jth outbreak of the jth species in the kth optimization, j=1, 2, …, M, representing the jth outbreak.
(6) Judging and processing out-of-range of new species generated by each burst; each dimension of the species generated by the outbreak should have a value within the interval [ L min, Lmax ], and if not, the dimension is beyond the boundary; replacing the dimension value of the out-of-range species with a random number in a [ L min, Lmax ] interval generated by a random function; if the boundary is not crossed, no treatment is needed;
(7) Performing deep iteration on new species generated by each burst by adopting a PSO algorithm;
① Based on new species generated by explosion, generating N particles, wherein the generation formula of each dimension of each particle is as follows:
(8)
(9)
Wherein, Value representing the d-th dimension of the i-th particle,/>A speed value representing the d-th dimension of the i-th particle, i=1, 2, …, N representing the i-th particle; /(I)Representation/>D-th dimensional value of (c).
② Carrying out boundary crossing judgment and treatment on each particle; each dimension of each particle has a value within the interval [ L min, Lmax ], and if not, the dimension is beyond the boundary; replacing the dimension value of the out-of-range particles with a random number in a [ L min, Lmax ] interval generated by a random function; if the boundary is not crossed, no treatment is needed;
③ According to the established fitness function, calculating fitness values of N particles, selecting a global optimal solution g, and enabling the particle self optimal solution to be the current value of the particle.
④ Updating each dimension of each particle velocity according to the following formula;
(10)
wherein ω is inertial weight; d-th dimension of speed at t-th iteration of the ith particle,/> Is the d dimension of the self history optimal position of the ith particle at the t time of iteration,/>Is the d dimension of the position of the ith particle at the t iteration,The d dimension of the optimal position of the group at the t-th iteration; and c 1 and c 2 are acceleration factors, which are constants greater than 0, and are used for controlling the influence of the particle history optimal position and the overall optimal particle position of the population on the current particle speed. r 1 and r 2 are random numbers between (0, 1).
Judging whether the speed is in the interval of [ - (L max-Lmin),( Lmax - Lmin) ] or not, if not, replacing the speed by using a random function to generate random numbers in the interval of [ - (L max - Lmin), (Lmax - Lmin) ] and if so, not processing;
updating the position of the particles according to the following formula;
(11)
Judging whether each dimension of each updated particle is in the [ L min, Lmax ] interval, if not, replacing the updated dimension by a random number in the [ L min, Lmax ] interval generated by a random function; if the interval is within the interval, no processing is needed;
⑤ After the particle speed and the position are updated each time, calculating an fitness function value of the updated particle, if the fitness function value is smaller than the fitness function value of the historical optimal solution of the particle, replacing the historical optimal solution of the particle with the position of the updated particle, otherwise, keeping the historical optimal solution of the particle unchanged;
After all particles complete the position updating of the iteration, selecting the particle with the smallest fitness function value of all updated particles, comparing the fitness value of the particle with the fitness value of the global optimal solution, and if the fitness value of the particle is smaller, replacing the global optimal solution with the position of the particle; otherwise, the global optimal solution remains unchanged;
⑥ Let t=t+1 and repeat ③－⑤ steps until iter iterations are completed;
⑦ Replacing the value of the new species generated by the current burst operation with the iterated global optimal solution g iter, namely: ；
(8) Calculating and comparing fitness function values of the species after and before the explosion, and marking the new species as the main species if the fitness value of the new species is smaller, otherwise, the old species as the main species; only the main species can perform the large burst operation; if a major transfer occurs, the generation number G of the species is increased by 1, and the derivatization capacity of the major species is updated to the derivatization capacity of the new species as shown in the following formula;
(12)
(13)
(9) Repeating the steps (5) to (8) until M bursts are completed;
(10) Sequencing the fitness function values of all new species generated by M bursts and existing species before the bursts, selecting S species with the optimal fitness value as metaplasia species for the next large burst and large extinction, and contracting the derivative capacity of surviving species according to the following formula, wherein all other species are eliminated;
(14)
Wherein, Is the coefficient of contraction.
(11) Repeating steps (5) to 10 until a termination condition is reached (the termination condition is typically the maximum number of iterations or the required optimization accuracy is reached).
Fourth step: and outputting an optimal position deployment result of the sensor node.
In the present invention, the parameters in the specific embodiments are shown in table 1.
TABLE 1 scene and algorithm parameters
Scene and algorithm Parameters (parameters)
Monitoring area: 4km×4km Lmin=0,Lmax=4km。
Number of nodes: 38 sensor nodes, wherein 20 sensor nodes are static sensors, and the rest are dynamic sensors. D=（38-20-4-1）×2=13×2=26。
Probability coverage model parameters of (1) rs=1.2km,δ＝0.6,λ1=1,λ2=0.6,β1=3,β2=2。
SED-PSO algorithm M=20，S=30，α=0.05，FEmax=2000,N=2,ω=0.724,c1=c2=2，iter=5。

Claims (3)

1. A sensor network coverage optimization method based on an improved species life-to-death optimization algorithm is characterized by comprising the following steps:
the first step: establishing a sensor probability coverage model shown in a formula (1);
Wherein, Representing the perceived probability of the jth sensor for an object at a distance R, the sensor perceived radius R s, the uncertainty perceived range delta x R s, delta being a coefficient less than 1,/>Representing the distance between the target T and the sensor S j, x t、yt representing the coordinates at the target point, respectively, and x s,j、ys,j representing the coordinates of the sensor S j, respectively; lambda 1、λ2、β1、β2 is a parameter related to the sensor characteristics, alpha 1、α2 is a first and second input parameter, respectively, the values of which are related to r s and d (S j, T), the relation being
And a second step of: clustering; clustering the static sensors by adopting a K-means algorithm, and dividing all sensor nodes into 4 clusters; a dynamic sensor is respectively arranged at the central position of each cluster and the central position of the optimization area to serve as a cluster head and a network central point;
And a third step of: optimizing the position deployment of the rest dynamic sensor nodes by adopting an SED-PSO algorithm; the SED-PSO algorithm comprises the following steps:
(1) Setting parameters of a species life-to-death algorithm, comprising: the number S of surviving species, the derivatization capability A of the species, the generation number G of the species, the explosion multiple M of the species, the contraction coefficient alpha and the iteration number FE max of the algorithm;
(2) Setting PSO algorithm parameters, including: population size P, first learning factor c 1 and second learning factor c 2, inertial weight factor omega, maximum positive movement speed v max and maximum negative movement speed v -max of particles, and iterative times iter of algorithm;
(3) Establishing an adaptability function; the fitness function of the sensor network coverage based on the improved species life-to-life optimization algorithm is the reciprocal of coverage;
(4) Initializing a species life-to-death algorithm in a randomizing mode according to the range of the optimized region, and calculating the fitness value of each species; each dimension of each species initializes the formula:
Xs,d＝Lmax+(Lmin-Lmax)×rand (4)
As＝Amax-Amin (5)
Wherein X s,d represents the value of the d dimension of the S-th species, a s represents the derivatization capacity of the S-th species, s=1, 2, …, S represents the S-th surviving species; d=1, 2, …, D representing the D-th dimension of the optimization problem, D representing the dimension of the optimization problem, rand representing the random number between (0, 1); [ L min,Lmax ] represents a search interval, and L min,Lmax is the lower limit and the upper limit of the search interval respectively; a epsilon [ A min,Amax],Amax ] represents the maximum species derivatization capacity, and A min is more than or equal to 0 represents the minimum species derivatization capacity;
(5) Performing an outbreak on each surviving species; the generation of new species at each major burst is shown in the following formula;
Wherein, Representing the new species derived from the s-th species in the kth optimization at the j-th outbreak,/>The s-th surviving species expressed in the kth optimization, rand is AND/>Random vector between (0, 1) of the same dimension,/>Representing the derivatization ability of the s-th species in the kth optimization,/>Representing the derivatization capacity of a new species derivatized by a jth outbreak of an s-th species in a kth optimization, j=1, 2, …, M, representing a jth outbreak;
(6) Judging and processing out-of-range of new species generated by each burst; each dimension of the species generated by the outbreak should have a value within the interval [ L min,Lmax ], and if not, the dimension is beyond the boundary; replacing the dimension value of the out-of-range species with a random number in a [ L min,Lmax ] interval generated by a random function; if the boundary is not crossed, no treatment is needed;
(7) The PSO algorithm is adopted to carry out deep iteration on new species generated by each burst:
① Based on new species generated by explosion, generating N particles, wherein the generation formula of each dimension of each particle is as follows:
vi,d＝(Lmax-Lmin)×(rand-0.5) (9)
where x i,d represents the value of the d-th dimension of the i-th particle, v i,d represents the speed value of the d-th dimension of the i-th particle, i=1, 2, …, N represents the i-th particle; representation/> D-th dimensional value of (2);
② Carrying out boundary crossing judgment and treatment on each particle; each dimension of each particle has a value within the interval [ L min,Lmax ], and if not, the dimension is beyond the boundary; replacing the dimension value of the out-of-range particles with a random number in a [ L min,Lmax ] interval generated by a random function; if the boundary is not crossed, no treatment is needed;
③ According to the established fitness function, calculating fitness values of N particles, selecting a global optimal solution g, and enabling the particle self optimal solution to be the current value of the particle;
④ Updating each dimension of each particle velocity according to the following formula;
wherein ω is inertial weight; d-th dimension of speed at t-th iteration of the ith particle,/> Is the d dimension of the self history optimal position of the ith particle at the t time of iteration,/>Is the d dimension of the position of the ith particle at the t-th iteration,/>The d dimension of the optimal position of the group at the t-th iteration; c 1 and c 2 are acceleration factors, which are constants greater than 0, and are used for controlling the influence of the particle history optimal position and the population overall optimal particle position on the current particle speed, and r 1 and r 2 are random numbers between (0, 1);
Judging whether the speed is in the interval of [ - (L max-Lmin),(Lmax-Lmin) ] or not, if not, replacing the speed by using a random function to generate random numbers in the interval of [ - (L max-Lmin),(Lmax-Lmin) ] and if so, not processing;
updating the position of the particles according to the following formula;
Judging whether each dimension of each updated particle is in the [ L min,Lmax ] interval, if not, replacing the updated dimension by a random number in the [ L min,Lmax ] interval generated by a random function; if the interval is within the interval, no processing is needed;
⑤ After the particle speed and the position are updated each time, calculating an fitness function value of the updated particle, if the fitness function value is smaller than the fitness function value of the historical optimal solution of the particle, replacing the historical optimal solution of the particle with the position of the updated particle, otherwise, keeping the historical optimal solution of the particle unchanged;
After all particles complete the position updating of the iteration, selecting the particle with the smallest fitness function value of all updated particles, comparing the fitness value of the particle with the fitness value of the global optimal solution, and if the fitness value of the particle is smaller, replacing the global optimal solution with the position of the particle; otherwise, the global optimal solution remains unchanged;
⑥ Let t=t+1 and repeat ③-⑤ steps until iter iterations are completed;
⑦ Replacing the value of the new species generated by the current burst operation with the iterated global optimal solution g iter, namely:
(8) Calculating and comparing fitness function values of the species after and before the explosion, and marking the new species as the main species if the fitness value of the new species is smaller, otherwise, the old species as the main species; only the main species can perform the large burst operation; if a major transfer occurs, the generation number G of the species is increased by 1, and the derivatization capacity of the major species is updated to the derivatization capacity of the new species as shown in the following formula;
(9) Repeating the steps (5) to (8) until M bursts are completed;
(10) Sequencing the fitness function values of all new species generated by M bursts and existing species before the bursts, selecting S species with the optimal fitness value as metaplasia species for the next large burst and large extinction, and contracting the derivative capacity of surviving species according to the following formula, wherein all other species are eliminated;
wherein α is the coefficient of contraction;
(11) Repeating the steps 5 to 10 until the termination condition is reached;
fourth step: and outputting an optimal position deployment result of the sensor node.
2. The sensor network coverage optimization method based on the improved species life-to-death optimization algorithm according to claim 1, wherein in the third step (3), the coverage rate is calculated as follows, the monitoring area is divided into [100×100] grids according to the granularity g=40m, and the proportion of the grids with the sensor network sensing probability larger than p th =0.6 at the center of the grids to all grids is calculated; the sensing probability of the sensor network is as follows
3. The method of claim 1, wherein in step (11), the termination condition is that the maximum number of iterations is reached or that the required optimizing accuracy is reached.
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