CN107172627B - Sensor node deployment method based on chaos optimization bacterial foraging algorithm - Google Patents

Sensor node deployment method based on chaos optimization bacterial foraging algorithm Download PDF

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CN107172627B
CN107172627B CN201710401253.3A CN201710401253A CN107172627B CN 107172627 B CN107172627 B CN 107172627B CN 201710401253 A CN201710401253 A CN 201710401253A CN 107172627 B CN107172627 B CN 107172627B
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CN107172627A (en
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王振东
陈峨霖
胡中栋
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Jiangxi University of Science and Technology
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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Abstract

The invention discloses a sensor node deployment method based on a chaos optimization bacterial foraging algorithm, which comprises the following steps of: initializing, setting circulation variables, chemotactic circulation, propagation circulation, migration operation and judging conditions for finishing circulation of the algorithm, finishing the algorithm and outputting an optimal bacteria combination if the conditions are met, and returning to the set circulation variables if the conditions are not met. The invention has the advantages that: in the node coverage scheme obtained by the chaos-optimized bacterial foraging algorithm, WSN nodes are uniformly distributed in a monitoring area, little node redundancy is generated, almost no coverage hole exists, compared with a strategy of randomly deploying the nodes, the coverage rate of a node deployment strategy network is improved, the nodes are more uniformly distributed in the monitoring area, the number of repeatedly-covered areas is less, the redundancy rate of the nodes is extremely low, the purpose of optimizing coverage of the WSN is achieved, the monitoring area can be effectively covered by fewer nodes in the optimized algorithm, the deployment cost is saved, and meanwhile, the monitoring time of the WSN is greatly prolonged.

Description

Sensor node deployment method based on chaos optimization bacterial foraging algorithm
Technical Field
The invention relates to establishment of a group intelligent algorithm optimized sensor node deployment model and realization of simulation, and belongs to the field of wireless sensor network optimized coverage monitoring areas.
Background
A self-organizing multi-hop Wireless Sensor Network (WSN) widely applied to the fields of geological monitoring, environmental protection and the like has the advantages of flexible deployment, low cost, wide coverage range and the like, but the problem of uneven node distribution is caused by large-range random deployment of a monitoring area. Aiming at the problem, numerous scholars use group intelligent bionic algorithm to carry out optimization processing. For example, a network coverage model is constructed by adopting an artificial fish swarm algorithm, and network coverage is optimized through model solution; a probability perception model is utilized to combine a genetic algorithm and a particle swarm algorithm to optimize network coverage; virtual force computation formulas and node reciprocation are improved using effective and overlapping centroids to optimize network node deployment. The group intelligent algorithm obtains great effect on the WSN coverage optimization problem, but also has the problems of high solving complexity, low convergence speed, low convergence precision, high operation cost and the like.
Therefore, those skilled in the art look at a Chaos-optimized bacterial foraging algorithm (COBFO) and simulate the node deployment situation of a monitoring area based on the cob fo, but research shows that the existing bacterial foraging algorithm has poor global space optimizing capability, the search speed and the search accuracy are required to be improved, and the existing bacterial foraging algorithm is easy to fall into local optimization and generate prematurity.
Disclosure of Invention
The invention aims to solve the technical problem of providing a sensor node deployment method based on a chaos optimization bacterial foraging algorithm.
The invention is realized by the following scheme: a sensor node deployment method based on a chaos optimization bacterial foraging algorithm comprises the following steps:
the method comprises the following steps: initializing and setting a cycle variable;
step two: judging whether the operation times of the colony migration operator reach the maximum iteration value, if so, entering a third step, and otherwise, entering a fourth step;
step three: finishing the algorithm and outputting the optimal bacteria combination;
step four: judging whether the operation times of the colony propagation operator reach, if so, entering a fifth step, and otherwise, entering a sixth step;
step five: calculating the network coverage rate and updating the position, improving the migration operator, and then returning to the step two;
step six: calculating chemotaxis step length and rebound step length of the bacteria after the improved turnover probability is turned over, selecting direction to move through a chaos sequence generated by chaos disturbance after the bacteria is turned over, judging whether the operation frequency of a colony chemotaxis operator is reached, if so, entering a seventh step, and otherwise, entering an eighth step;
step seven: improving a propagation operator, performing cross variable operation on bacteria, and returning to the fourth step;
step eight: improving the adaptability of the bacteria, and returning to the step six after the bacteria tend to move.
In the first step, N coordinates are initialized randomly as sensor nodes, and chemotaxis, replication and migration times of bacteria are N respectivelyc,Nre,Ned(ii) a The counting parameters of the operation are set as g, s and l, the chemotaxis step length of a single bacterium is C, the maximum chemotaxis step number in the same direction is Nc (i), the migration probability is Ped(ii) a The cycle variable is increased from 1, the maximum value of chemotactic cycle g is NcThe maximum value of the replication cycle s is NreThe maximum value of migration cycle l is Ned；
Bi＝Lmin+rand(0，1)×
(Lmax-Lmin)(i∈1，2，…，N)
Wherein N is the total number of bacteria, LmaxAnd LminRespectively, the lower and upper limits of the monitored region.
In the second step, the migration probability P of the bacteria is calculated according to the following formulaselfIf the bacteria do not meet the migration probability, the bacteria are deleted, the bacteria are re-selected in the flora, then whether the migration times reach the maximum iteration value or not is judged,
Figure GDA0002871150260000021
wherein JhealthIs a fitness function, PedIs the probability of the original migration and is,
Figure GDA0002871150260000022
is the maximum fitness of the bacterium,
Figure GDA0002871150260000023
is the minimum fitness of the bacterium that is,
Figure GDA0002871150260000024
is the fitness of the current bacterium, fitness function JhealthIs obtained by the original bacterial foraging algorithm; the calculation formula is as follows:
Figure GDA0002871150260000031
wherein, P (g, s, l)) { θ }i(g,s,l)|i＝1,2,…,N}，θi(g, s, l) indicates the location of the bacteria at the g-th chemotaxis, s-th reproduction (replication), l-th migration (dispersion), JhealthThe expression is a fitness function, N is the total number of bacteria, P is the number of variables to be optimized, expressed in each bacteria and θ ═ θ1,θ2,…,θP]TIs a point in the P-dimensional search domain, dattractant，wattractant，hrepellant，wrepellantAre different coefficients that can be appropriately selected.
In the fourth step, selective propagation is carried out after chemotactic circulation of bacteria, the bacteria fitness is sequenced firstly, half of bacteria with poor fitness are eliminated, then the other half of bacteria are divided and copied to keep the total number of the bacteria unchanged, and the ith bacteria fitness is accumulated as follows:
Figure GDA0002871150260000032
wherein J (i, g, s, l) represents the fitness of bacterium i at l dispels, s replicates, g chemotaxis.
In the fifth step, by f ═ w1f1+w2f2+w3f3Calculating the coverage rate of different bacteria combinations, finding out the group with the maximum coverage rate, and updating
Figure GDA0002871150260000037
Information wherein w1、w2、w3As a weight value, w1+w2+w 31, wherein f1、f2、f3Respectively representing effective coverage rate, node idle rate and residual energy balance function,
Figure GDA0002871150260000036
for the current search for bacteriaxiThe best fitness of.
In the sixth step, press
Figure GDA0002871150260000033
Formula improved after flip probability flip
Figure GDA0002871150260000034
Formula (II) and
Figure GDA0002871150260000035
calculating chemotaxis step length and rebound step length of bacteria, selecting direction to swim through chaos sequence generated by chaos disturbance after bacteria turnover operation, wherein the unit step length of swimming is c (i), Pbesti m＝(Pbest1 m，Pbest2 m，…，Pbesti m) A fitness value representing a position after the selection of the chaotic swimming direction to determine whether to update the position information; where Δ (i) represents the random direction vector generated, ΔT(i) The generated vector after random direction rotation, and delta (i) belongs to Rn，xi，xj，yi，yjFor coordinate information of the flipped particles, ai，biIs [0, 1 ]]A random value of ziA chaotic sequence iterated by the Logistic chaotic system;
the chaotic disturbance process is as follows:
by
Figure GDA0002871150260000041
The initial value z is selected00.9978, iterating the chaos mapping parameter rho 4 by the chaos system to obtain the determined chaos sequence Z1，z2，Z3，…；
Figure GDA0002871150260000042
In the formula, rho is a chaotic control parameter,n is the total number of bacteria, LlongIs the diagonal length of the solution area, d is the dimension of the solution area, X (h, k) represents the coordinate information of the bacteria k in the h dimension solution area,
Figure GDA0002871150260000043
mean values of coordinates representing all bacteria in the h-dimension solution area;
a. search for bacteria xiIs/are as follows
Figure GDA0002871150260000044
By passing
Figure GDA0002871150260000045
Formula mapping to
Figure GDA0002871150260000046
Formula Logistic equation definition domain (0, 1);
wherein the content of the first and second substances,
Figure GDA0002871150260000047
is a node coordinate range;
b. by Logistic equation
Figure GDA0002871150260000048
And (3) carrying out multiple iterations to obtain a chaotic sequence:
Zm(m＝1，2，3，…)
c. passing the generated chaotic sequence through
Figure GDA0002871150260000049
Inverse mapping of formula
Pbestxi m＝axi+(bxi-axi)×zm
(m ═ 1, 2, 3, …) to return to the original solution space and produce a solvable chaotic sequence containing chaotic variables:
Pbestci m＝(Pbest1 m，Pbestx2 m，…，Pbestci m)
in the seventh step, calculating and sequencing the adaptability of each bacterium, and selecting a half of bacteria with high flora adaptability as flora elite bacteria; hybridizing half of bacteria with poor adaptability with the selected elite bacteria by using a crossover operator to generate N/2 new bacteria, performing mutation operation on half of bacteria after the crossover operation by using a mutation operator, and copying and splitting to form a new bacteria group Nnew。
In step eight, if the fitness of the bacteria is improved, the bacteria continue to swim in the same direction, and when the fitness is not improved any more or the maximum chemotaxis number N is reachedcThe swimming is stopped.
The invention has the beneficial effects that: in a node coverage scheme obtained by using a chaos-optimized bacterial foraging algorithm, WSN nodes are uniformly distributed in a monitored area, few node redundancies are generated, the coverage rate can reach 98.84%, and almost no coverage holes exist. Compared with a strategy of randomly deploying nodes, the coverage rate of a node deployment strategy network obtained by a chaotic sequence optimization bacterial foraging algorithm is improved by 8.71%, the nodes are more uniformly distributed in a monitoring area, the number of repeatedly covered areas is less, the redundancy of the nodes is extremely low, the purpose of optimizing coverage of the WSN is achieved, the monitoring area can be effectively covered by the optimized algorithm by using fewer nodes, the deployment cost is saved, and meanwhile, the monitoring time of the WSN is greatly prolonged.
Drawings
Fig. 1 is an algorithm flow diagram of a sensor node deployment method based on a chaos optimization bacterial foraging algorithm.
FIG. 2 illustrates a random node coverage method for node deployment
FIG. 3 is a coverage curve for randomly deployed nodes
FIG. 4 is a BFO optimized overlay node deployment
FIG. 5 is a BFO optimized coverage curve
FIG. 6 is a CBFO optimized deployment strategy
FIG. 7 is a CBFO optimized deployment strategy coverage curve
FIG. 8 is a life cycle contrast curve
FIG. 9 is a graph of the residual energy versus node energy
In fig. 10, ρ is 2.5, z00.9978-hour iteration track graph
In fig. 11, ρ is 3.56, z00.9978-hour iteration track graph
FIG. 12 shows ρ 3.8, z00.9978-hour iteration track graph
In fig. 13, ρ is 4, z00.9978-hour iteration track graph
In fig. 14, ρ is 4, z00.5 hour iteration trace diagram
Detailed Description
The invention is further described below with reference to fig. 1-14, without limiting the scope of the invention.
In the following description, for purposes of clarity, not all features of an actual implementation are described, well-known functions or constructions are not described in detail since they would obscure the invention with unnecessary detail, it being understood that in the development of any actual embodiment, numerous implementation details must be set forth in order to achieve the developer's specific goals, such as compliance with system-related and business-related constraints, changing from one implementation to another, and it being recognized that such development effort might be complex and time consuming, but would nevertheless be a routine undertaking for those of ordinary skill in the art.
The invention is realized by the following scheme: a sensor node deployment method based on a chaos optimization bacterial foraging algorithm comprises the following steps:
the method comprises the following steps: initializing and setting a cycle variable;
step two: judging whether the operation times of the colony migration operator reach the maximum iteration value, if so, entering a third step, and otherwise, entering a fourth step;
step three: finishing the algorithm and outputting the optimal bacteria combination;
step four: judging whether the operation times of the colony propagation operator reach, if so, entering a fifth step, and otherwise, entering a sixth step;
step five: calculating the network coverage rate and updating the position, improving the migration operator, and then returning to the step two;
step six: calculating chemotaxis step length and rebound step length of the bacteria after the improved turnover probability is turned over, selecting direction to move through a chaos sequence generated by chaos disturbance after the bacteria is turned over, judging whether the operation frequency of a colony chemotaxis operator is reached, if so, entering a seventh step, and otherwise, entering an eighth step;
step seven: improving a propagation operator, performing cross variable operation on bacteria, and returning to the fourth step;
step eight: improving the adaptability of the bacteria, and returning to the step six after the bacteria tend to move.
In the first step, N coordinates are initialized randomly as sensor nodes, and chemotaxis, replication and migration times of bacteria are N respectivelyc,Nre,Ned(ii) a The counting parameters of the operation are set as g, s and l, the chemotaxis step length of a single bacterium is C, the maximum chemotaxis step number in the same direction is Nc (i), the migration probability is Ped(ii) a The cycle variable is increased from 1, the maximum value of chemotactic cycle g is NcThe maximum value of the replication cycle s is NreThe maximum value of migration cycle l is Ned；
Bi＝Lmin+rand(0，1)×
(Lmax-Lmin)(i∈1，2，…，N)
Wherein N is the total number of bacteria, LmaxAnd LminRespectively, the lower and upper limits of the monitored region.
In the second step, the migration probability P of the bacteria is calculated according to the following formulaselfIf the bacteria do not meet the migration probability, the bacteria are deleted, the bacteria are re-selected in the flora, then whether the migration times reach the maximum iteration value or not is judged,
Figure GDA0002871150260000071
wherein JhealthIs a fitness function, PedIs the probability of the original migration and is,
Figure GDA0002871150260000072
is bacterialThe maximum degree of fitness of (a) is,
Figure GDA0002871150260000073
is the minimum fitness of the bacterium that is,
Figure GDA0002871150260000074
is the fitness of the current bacterium, fitness function JhealthIs obtained by the original bacterial foraging algorithm; the calculation formula is as follows:
Figure GDA0002871150260000075
wherein, P (g, s, l)) { θ }i(g,s,l)|i＝1,2,…,N}，θi(g, s, l) indicates the location of the bacteria at the g-th chemotaxis, s-th reproduction (replication), l-th migration (dispersion), JhealthThe expression is a fitness function, N is the total number of bacteria, P is the number of variables to be optimized, expressed in each bacteria and θ ═ θ1,θ2,…,θP]TIs a point in the P-dimensional search domain, dattractant，wattractant，hrepellant，wrepellantAre different coefficients that can be appropriately selected.
In the fourth step, selective propagation is carried out after chemotactic circulation of bacteria, the bacteria fitness is sequenced firstly, half of bacteria with poor fitness are eliminated, then the other half of bacteria are divided and copied to keep the total number of the bacteria unchanged, and the ith bacteria fitness is accumulated as follows:
Figure GDA0002871150260000081
where J (i, g, sl) represents the fitness of bacterium i at l dispels, S replications, g chemotaxis.
In the fifth step, by f ═ w1f1+w2f2+w3f3Calculating the coverage rate of different bacteria combinations, finding out the group with the maximum coverage rate, and updating
Figure GDA0002871150260000082
Information wherein w1、w2、w3As a weight value, w1+w2+w 31, wherein f1、f2、f3Respectively representing effective coverage rate, node idle rate and residual energy balance function,
Figure GDA0002871150260000083
for the current search for bacteria xiThe best fitness of.
In the sixth step, press
Figure GDA0002871150260000084
Formula improved after flip probability flip
Figure GDA0002871150260000085
Formula (II) and
Figure GDA0002871150260000086
calculating chemotaxis step length and rebound step length of bacteria, wherein the bacteria swim in the direction selected by chaotic sequence generated by chaotic disturbance after overturning operation, and the unit step length of the swimming is c (i) and Pbesti m＝(Pbest1 m，Pbest2 m，…，Pbesti m) A fitness value representing a position after the selection of the chaotic swimming direction to determine whether to update the position information; where Δ (i) represents the random direction vector generated, ΔT(i) The generated vector after random direction rotation, and delta (i) belongs to Rn，xi，xj，yi，yjFor coordinate information of the flipped particles, ai，biIs [0, 1 ]]A random value of ziA chaotic sequence iterated by the Logistic chaotic system;
the chaotic disturbance process is as follows:
by
Figure GDA0002871150260000091
The initial value z is selected00.9978, iterating the chaos mapping parameter rho 4 by the chaos system to obtain the determined chaos sequence z1，z2，z3，…；
Figure GDA0002871150260000092
Where ρ is a chaos control parameter, N is the total number of bacteria, LlongIs the diagonal length of the solution area, d is the dimension of the solution area, X (h, k) represents the coordinate information of the bacteria k in the h dimension solution area,
Figure GDA0002871150260000093
mean values of coordinates representing all bacteria in the h-dimension solution area;
a. search for bacteria xiIs/are as follows
Figure GDA0002871150260000094
By passing
Figure GDA0002871150260000095
Formula mapping to
Figure GDA0002871150260000096
Formula Logistic equation definition domain (0, 1);
wherein the content of the first and second substances,
Figure GDA0002871150260000097
is a node coordinate range.
b. By Logistic equation
Figure GDA0002871150260000098
And (3) carrying out multiple iterations to obtain a chaotic sequence:
zm(m＝1，2，3，…)
c. passing the generated chaotic sequence through
Figure GDA0002871150260000099
Inverse mapping of formula
Pbestci m＝axi+(bxi-axi)×zm
(m ═ 1, 2, 3, …) to return to the original solution space and produce a solvable chaotic sequence containing chaotic variables:
Pbestxi m＝(Pbestc1 m，Pbestx2 m，…，Pbestxi m)
in the seventh step, calculating and sequencing the adaptability of each bacterium, and selecting a half of bacteria with high flora adaptability as flora elite bacteria; hybridizing half of bacteria with poor adaptability with the selected elite bacteria by using a crossover operator to generate N/2 new bacteria, performing mutation operation on half of bacteria after the crossover operation by using a mutation operator, and copying and splitting to form a new bacteria group Nnew。
In step eight, if the fitness of the bacteria is improved, the bacteria continue to swim in the same direction, and when the fitness is not improved any more or the maximum chemotaxis number N is reachedcThe swimming is stopped.
Simulation experiments were performed in the Matlab2010b environment. 200 sensor nodes are randomly distributed in a rectangular plane with a monitoring area of 800m x 800 m. Each sensor node represents a bacterium in a flora, the radius R of a monitoring area of the node is 50, the communication distance R is 100, and various parameters of an algorithm are initialized, wherein N is the number of the nodesre∶Nc∶NedInitializing node energy E at 5: 60: 30The chemotaxis step length C is 0.05J, the rebound collision coefficient alpha is 0.05, the migration probability is 0.25, the maximum number of single-direction overturn times is 10 times, and the experimental result is shown in figures 2-9. Fig. 2 and 3 are graphs of deployment conditions and coverage rates of nodes when a monitoring area is covered by randomly distributed nodes, fig. 4 and 5 are WSN deployment node conditions and optimized coverage curves obtained after BFO optimization, and fig. 6 and 7 are WSN deployment node conditions obtained after COBFO algorithm optimization, wherein black points represent coordinate position information of sensor nodes, and circles represent areas of node coverage areas.
As can be seen from the analysis of fig. 2 to 9, in the random node deployment strategy, the nodes are extremely unevenly distributed in the area, a large amount of node redundancy and large-area repeated coverage also exist while coverage holes occur, the node coverage rate is 90.13%, the node coverage is more uniform after the node deployment is optimized by the bacterial foraging algorithm, the repeated coverage area is greatly reduced, a small amount of redundant nodes and coverage holes still exist, and the coverage rate is 95.27%. In a node coverage scheme obtained by using a chaos-optimized bacterial foraging algorithm, WSN nodes are uniformly distributed in a monitored area, few node redundancies are generated, the coverage rate can reach 98.84%, and almost no coverage holes exist. Compared with a strategy of randomly deploying nodes, the coverage rate of a node deployment strategy network obtained by a chaotic sequence optimization bacterial foraging algorithm is improved by 8.71%, the nodes are more uniformly distributed in a monitoring area, the number of repeatedly covered areas is less, the redundancy of the nodes is extremely low, the purpose of optimizing coverage of the WSN is achieved, the monitoring area can be effectively covered by the optimized algorithm by using fewer nodes, the deployment cost is saved, and meanwhile, the monitoring time of the WSN is greatly prolonged.
Table-deployment policy comparison
Figure GDA0002871150260000101
As can be seen from the table one, the average coverage rate of the network of the chaotically optimized bacterial foraging node deployment strategy is higher than that of other comparison algorithms, the number of used nodes is less than that of other node deployment algorithms, the utilization rate and the energy balance of the nodes are superior to those of other node deployment algorithms, the speed for searching the optimal coverage strategy is fastest, and through mutual comparison, the CBFO optimization algorithm can effectively improve the utilization rate and the network coverage rate of the nodes, reduce the energy consumption of the nodes and prolong the service life of the network.
Study on influence of initial values and parameters on chaotic mapping system
The Logistic mapping system is very sensitive to initial values and parameters, the chaotic sequence can change along with different initial values and parameters, and a better global search sequence is obtained by researching a track graph obtained after different initial values and parameters are iterated for 500 times in a mapping equation. The results are shown in FIGS. 10-14:
from the results, when ρ is 4, z0The iterative chaotic sequence obtained when the number is 0.9978 is ideal, and the local search area can be searched globally.
Although the invention has been described and illustrated in some detail, it should be understood that various modifications may be made to the described embodiments or equivalents may be substituted, as will be apparent to those skilled in the art, without departing from the spirit of the invention.

Claims (8)

1. A sensor node deployment method based on a chaos optimization bacterial foraging algorithm is characterized by comprising the following steps: which comprises the following steps:
the method comprises the following steps: initializing and setting a cycle variable;
step two: judging whether the operation times of the colony migration operator reach the maximum iteration value, if so, entering a third step, and otherwise, entering a fourth step;
step three: finishing the algorithm and outputting the optimal bacteria combination;
step four: judging whether the operation times of the colony propagation operator reach, if so, entering a fifth step, and otherwise, entering a sixth step;
step five: calculating the network coverage rate and updating the position, improving the migration operator, and then returning to the step two;
step six: calculating chemotaxis step length and rebound step length of the bacteria after the improved turnover probability is turned over, selecting direction to move through a chaos sequence generated by chaos disturbance after the bacteria is turned over, judging whether the operation frequency of a colony chemotaxis operator is reached, if so, entering a seventh step, and otherwise, entering an eighth step;
step seven: improving a propagation operator, performing cross variable operation on bacteria, and returning to the fourth step;
step eight: improving the adaptability of the bacteria, and returning to the step six after the bacteria tend to move.
2. The sensor node deployment method based on the chaos optimization bacterial foraging algorithm according to claim 1, characterized in that: in the first step, N coordinates are initialized randomly as sensor nodes, and chemotaxis, replication and migration times of bacteria are N respectivelyc，Nre，Ned(ii) a The counting parameters of the manipulations were set to g, sl, the chemotactic step size of the individual bacteria was C and the maximum number of chemotactic steps in the same direction was Nc(i)Migration probability of Ped(ii) a The cycle variable is increased from 1, the maximum value of chemotactic cycle g is NcThe maximum value of the replication cycle s is NreThe maximum value of migration cycle l is Ned，
Bi＝Lmin+rand(0，1)×(Lmax-Lmin)(i∈1，2，…，N)
Wherein N is the total number of bacteria, LmaxAnd LminRespectively, the lower and upper limits of the monitored region.
3. The sensor node deployment method based on the chaos optimization bacterial foraging algorithm according to claim 1, characterized in that: in the second step, the migration probability P of the bacteria is calculated according to the following formulaselfIf the bacteria do not meet the migration probability, the bacteria are deleted, the bacteria are re-selected in the flora, then whether the migration times reach the maximum iteration value or not is judged,
Figure FDA0002871150250000021
wherein JhealthIs a fitness function, PedIs the probability of the original migration and is,
Figure FDA0002871150250000022
is the maximum fitness of the bacterium,
Figure FDA0002871150250000023
is the minimum fitness of the bacterium that is,
Figure FDA0002871150250000024
is the fitness of the current bacterium, fitness function JhealthIs obtained by the original bacterial foraging algorithm; the calculation formula is as follows:
Figure FDA0002871150250000025
wherein, P (g, s, l)) { θ }i(g，s，l)|i＝1，2，…，N}，θi(g, s, l) indicates the location of the g-th chemotaxis, s-th reproduction, l-th migration of the bacteria, JhealthThe expression is a fitness function, N is the total number of bacteria, P is the number of variables to be optimized, expressed in each bacteria and θ ═ θ1，θ2，…，θP]TIs a point in the P-dimensional search domain, dattractant，wattractant，hrepellant，wrepellantAre different coefficients that can be appropriately selected.
4. The sensor node deployment method based on the chaos optimization bacterial foraging algorithm according to claim 1, characterized in that: in the fourth step, selective propagation is carried out after chemotactic circulation of bacteria, the bacteria fitness is sequenced firstly, half of bacteria with poor fitness are eliminated, then the other half of bacteria are divided and copied to keep the total number of the bacteria unchanged, and the ith bacteria fitness is accumulated as follows:
Figure FDA0002871150250000026
where J (i, g, sl) represents the fitness of bacterium i at l dispels, s replicates, g chemotaxis.
5. The sensor node deployment method based on the chaos optimization bacterial foraging algorithm according to claim 1, characterized in that: in the fifth step, by f ═ w1f1+w2f2+w3f3Calculating the coverage rate of different bacteria combinations, finding out the group with the maximum coverage rate, and updating
Figure FDA0002871150250000027
Information, explain w therein1、w2、w3As a weight value, w1+w2+w31, wherein f1、f2、f3Respectively representing effective coverage rate, node idle rate and residual energy balance function,
Figure FDA0002871150250000036
for the current search for bacteria xiThe best fitness of.
6. The sensor node deployment method based on the chaos optimization bacterial foraging algorithm according to claim 1, characterized in that: in the sixth step, press
Figure FDA0002871150250000031
Formula improved after flip probability flip
Figure FDA0002871150250000032
Formula (II) and
Figure FDA0002871150250000033
calculating chemotaxis step length and rebound step length of bacteria, wherein the bacteria swim in the direction selected by chaotic sequence generated by chaotic disturbance after overturning operation, and the unit step length of the swimming is c (i) and Pbesti m＝(Pbest1 m，Pbest2 m，…，Pbesti m) A fitness value representing a position after the selection of the chaotic swimming direction to determine whether to update the position information; where Δ (i) represents the random direction vector generated, ΔT(i) The generated vector after random direction rotation, and delta (i) belongs to Rn，xi，xj，yi，yjFor coordinate information of the flipped particles, ai，biIs [0, 1 ]]A random value of ziA chaotic sequence iterated by the Logistic chaotic system;
the chaotic disturbance process is as follows:
by
Figure FDA0002871150250000034
The initial value Z is selected00.9978, iterating the chaos mapping parameter rho 4 by the chaos system to obtain the determined chaos sequence z1，z2，Z3，…；
Figure FDA0002871150250000037
Where ρ is a chaos control parameter, N is the total number of bacteria, LlongIs the diagonal length of the solution area, d is the dimension of the solution area, X (h, k) represents the coordinate information of the bacteria k in the h dimension solution area,
Figure FDA0002871150250000035
mean values of coordinates representing all bacteria in the h-dimension solution area;
a. search for bacteria xiIs/are as follows
Figure FDA0002871150250000038
By passing
Figure FDA0002871150250000039
Formula mapping to
Figure FDA0002871150250000042
Formula Logistic equation definition domain (0, 1);
wherein the content of the first and second substances,
Figure FDA0002871150250000043
as a node coordinate systemEnclosing;
b. by Logistic equation
Figure FDA0002871150250000044
And (3) carrying out multiple iterations to obtain a chaotic sequence:
zm(m＝1，2，3，…)
c. passing the generated chaotic sequence through
Figure FDA0002871150250000041
Inverse mapping of formula
Figure FDA0002871150250000045
(m ═ 1, 2, 3, …) to return to the original solution space and produce a solvable chaotic sequence containing chaotic variables:
Figure FDA0002871150250000046
7. the sensor node deployment method based on the chaos optimization bacterial foraging algorithm according to claim 1, characterized in that: in the seventh step, calculating and sequencing the adaptability of each bacterium, and selecting a half of bacteria with high flora adaptability as flora elite bacteria; hybridizing half of bacteria with poor adaptability with the selected elite bacteria by using a crossover operator to generate N/2 new bacteria, performing mutation operation on half of bacteria after the crossover operation by using a mutation operator, and copying and splitting to form a new bacteria group Nnew。
8. The sensor node deployment method based on the chaos optimization bacterial foraging algorithm according to claim 1, characterized in that: in step eight, if the fitness of the bacteria is improved, the bacteria continue to swim in the same direction, and when the fitness is not improved any more or the maximum chemotaxis number N is reachedcThe swimming is stopped.
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* Cited by examiner, † Cited by third party
Title
Sensor node deployment using Bacterial Foraging Optimization;Gurjot Singh Gaba，Kanwaljit Singh，etc.;《2011 International Conference on Recent Trends in Information Systems》;20120209;73-76 *
菌群优化算法的研究及应用;车伟伟;《中国优秀硕士学位论文全文数据库》;20120715;全文 *

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