CN113472573B - High-dimensional multi-objective collaborative optimization method for wireless sensor network resource scheduling - Google Patents

High-dimensional multi-objective collaborative optimization method for wireless sensor network resource scheduling Download PDF

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CN113472573B
CN113472573B CN202110733366.XA CN202110733366A CN113472573B CN 113472573 B CN113472573 B CN 113472573B CN 202110733366 A CN202110733366 A CN 202110733366A CN 113472573 B CN113472573 B CN 113472573B
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张磊
唐庭龙
汪方毅
崔文超
龚国强
冉昌艳
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China Three Gorges University CTGU
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Abstract

The invention discloses a high-dimensional multi-target collaborative optimization method for wireless sensor network resource scheduling, which comprises the steps of firstly designing a hybrid wireless sensor network perception model comprising static nodes, mobile nodes and relay nodes; then establishing a wireless sensor network high-dimensional multi-target service quality evaluation model comprising the targets of coverage rate, connectivity, energy consumption, life cycle, time delay, economy and the like; secondly, providing a high-dimensional multi-target decomposition algorithm based on the co-evolution of the double-ion population; and finally, optimizing the evaluation model by using the proposed algorithm.

Description

High-dimensional multi-objective collaborative optimization method for wireless sensor network resource scheduling
Technical Field
The invention belongs to the field of wireless sensor networks, and particularly relates to a high-dimensional multi-target collaborative optimization method for resource scheduling of a wireless sensor network.
Background
The wireless sensor network is a multi-hop autonomous network and is widely applied to the fields of environmental monitoring, rescue and relief work, military communication and the like. The wireless sensor network has the characteristics of multi-hop transmission, dynamic topology, limited node resources and the like, so that the resource management technology of the wireless sensor network is particularly important. Aiming at the complex high-dimensional multi-objective optimization problem, the excellent algorithm in the field of computational intelligence at present is used for resource scheduling research of the wireless sensor network, and the method has very important practical significance.
Disclosure of Invention
In order to overcome the defects of the prior art,
the invention provides a high-dimensional multi-target collaborative optimization method for wireless sensor network resource scheduling, which comprises the following steps: establishing a hybrid network perception model comprising static nodes, mobile nodes and relay nodes, designing a novel WSN high-dimensional multi-target service quality evaluation model comprising the targets of coverage rate, connectivity, energy consumption, life cycle, time delay, economy and the like, and finally providing a dual-ion population co-evolution high-dimensional multi-target optimization algorithm for optimization of the evaluation model. The method comprises the following steps:
step 1, establishing a WSN (wireless sensor network) perception model of a hybrid wireless sensor network;
step 2, establishing a hybrid wireless sensor network WSN high-dimensional multi-target service quality evaluation model;
and 3, optimizing the WSN high-dimensional multi-target service quality evaluation model of the hybrid wireless sensor network.
In step 1, the WSN perception model of the hybrid wireless sensor network comprises a static node, a mobile node and a relay node, and the communication radius of the static node is set to be rsThe communication radius of the mobile node is rmThe communication radius of the relay node is R.
The step 2 comprises the following steps:
step 2-1, establishing a coverage rate evaluation function;
Step 2-2, establishing a connectivity evaluation function;
step 2-3, establishing an energy consumption evaluation function;
step 2-4, establishing a life cycle evaluation function;
step 2-5, establishing a time delay evaluation function;
step 2-6, establishing an economic evaluation function;
and 2-7, establishing a WSN high-dimensional multi-target service quality evaluation model of the hybrid wireless sensor network.
Step 2-1 comprises: discretizing a two-dimensional plane area to be monitored into a grid shape, and calculating the total number N of the gridstotal(ii) a Calculating the total number of grids covered by all wireless sensor nodes in the working state of the WSN (wireless sensor network)
Figure BDA0003140527850000021
i is 1,2, …, N, N is the number of working nodes,
Figure BDA0003140527850000022
the number of grids covered by the ith node;
establishing a coverage rate evaluation function CrComprises the following steps:
Figure BDA0003140527850000023
step 2-2 comprises: calculating the distances between all working wireless sensor nodes and recording the distances as a distance matrix D; then, the magnitude relation between each element in the distance matrix D and the node perception radius r is judged, so that the communication state between nodes in the working state, namely a communication matrix L is established, wherein DijIs the distance between node i and node j, lijThe relationship between the node i and the node j is shown in formula (2):
Figure BDA0003140527850000024
Figure BDA0003140527850000025
the established connection matrix L is a symmetric matrix, and elements in the symmetric matrix are not 0, namely 1; the WSN formed by all the working nodes has connectivity which must satisfy the following conditions:
Condition 1: rank (l) is equal to N,
condition 2: sum (L) is more than or equal to 2(N-1),
wherein rank (L) is the rank of the matrix L, and sum (L) is the sum of all elements of the matrix L;
the condition 1 indicates that the rank of the connectivity matrix L is N, and any node is ensured to have a node capable of communicating with the node; the condition 2 represents that the sum of all elements of the connectivity matrix is more than or equal to 2(N-1), and at least one other node capable of communicating with any node is ensured to exist in any node;
establishing a connectivity evaluation function C as follows:
max C＝max Rank(L) (3)
Figure BDA0003140527850000031
the step 2-3 comprises the following steps: establishing an energy consumption evaluation function E as follows:
Figure BDA0003140527850000032
Figure BDA0003140527850000033
Figure BDA0003140527850000034
in the formula, EtEnergy consumption for transmitting data per unit time, ErEnergy consumption for receiving data per unit time, EiIs the energy consumption of the ith node per unit time, tiAmount of data sent for the ith node, EecFor electrical energy consumption, AecTo amplify the energy consumption, diIs the communication distance between the ith node and the next hop node, lambda is the data loss coefficient, riThe amount of data received for the ith node.
The steps 2-4 comprise: lifetime L of the ith nodeiComprises the following steps:
Figure BDA0003140527850000035
Es＝Eini-t×Ei (9)
in the formula, EsResidual energy for wireless sensor nodes, EiniIs the initial energy of the wireless sensor node, t is the working time of the node, EiEnergy consumption of the ith node in unit time;
establishing life period evaluation function L netComprises the following steps:
Lnet＝min Li (10)。
the steps 2-5 comprise: establishingTime delay evaluation function RtComprises the following steps:
Figure BDA0003140527850000041
where d is the communication path distance,
Figure BDA0003140527850000042
is the duty cycle of the ith node, CdFor communication time, DPdIs the packet duration.
The steps 2-6 comprise: establishing a WSN economic evaluation function EcComprises the following steps:
Figure BDA0003140527850000043
Figure BDA0003140527850000044
Figure BDA0003140527850000045
in the formula, wfAnd wdRepresents a weight coefficient, LnetFor hybrid wireless sensor network WSN lifetime, Costf(si) For the ith sensor node siFinancial cost of P(s)i) For the ith sensor node siThe probability of normal work in a designated area, phi is a specific constant of the wireless sensor;
Figure BDA0003140527850000046
in order to be at a financial cost,
Figure BDA0003140527850000047
to the deployment cost;
the steps 2-7 comprise: the method comprises the following steps of establishing a WSN high-dimensional multi-target service quality evaluation model of the hybrid wireless sensor network:
F(X)＝(f1(X),f2(X),f3(X),f4(X),f5(X),f6(X))＝(Cr,C,E,Lnet,Rt,Ec) (16)
wherein F (X) is an objective function, and X is (X)1,…,xi,…,xN) Is a variable, and is a function of,
Figure BDA0003140527850000048
parameters of the ith wireless sensor node comprise position coordinates of the node
Figure BDA0003140527850000049
Node operating time ratio
Figure BDA00031405278500000410
Whether or not it is in working state deltaiNode operating rule deltai1, otherwise δi＝0；f1(X),f2(X),f3(X),f4(X),f5(X),f6(X) corresponds to 6 functions C, respectivelyr,C,E,Lnet,Rt,Ec。
The step 3 comprises the following steps:
step 3-1, randomly generating an initial population with the scale of P, dividing the population into two, namely an anion population A and a cation population C, and setting the maximum evolution generation Gmax：
Figure BDA0003140527850000051
Wherein the content of the first and second substances,
Figure BDA0003140527850000052
The p/2 th line of A is the p/2 th anion individual;
Figure BDA0003140527850000053
the p/2 th row of C is the p/2 th cation individual;
Figure BDA0003140527850000054
is the element of the p/2 th row and the N column of the anion population A;
Figure BDA0003140527850000055
is the element of the p/2 th line and the N column of the cation population C;
and 3-2, updating the liquid state of the ion population based on cooperative guidance of the isotropic optimal ions and the anisotropic optimal ions:
Ai(t+1)＝Ai(t)+rand()×(Cbest(t)-Ai(t))+rand()×(Abest(t)-Ai(t))， (17)
Ci(t+1)＝Ci(t)+rand()×(Abest(t)-Ci(t))+rand()×(Cbest(t)-Ci(t))， (18)
where t is the number of evolutionary iterations, Ai(t) represents the ith anion, Ci(t) represents the ith cation, cbest (t) represents the optimum cation in the t generation, abest (t) represents the optimum anion in the t generation, and rand () is a random number between 0 and 1;
and 3-3, updating the solid state of the ion population based on heterosexual optimal ion guidance:
Ai(t+1)＝Ai(t)+rand()×(Cbest(t)-Ai(t)) (17)
Ci(t+1)＝Ci(t)+rand()×(Abest(t)-Ci(t)) (18)；
step 3-4: carrying out high-dimensional multi-target individual selection operation, and establishing a fitness evaluation function which is adaptively adjusted, wherein the fitness evaluation function is shown as a formula (19):
Figure BDA0003140527850000056
Figure BDA0003140527850000057
where t is the number of evolutionary iterations, GmaxFor maximum number of evolutionary iterations, X is an individual in the anion population a or the cation population C; fit (X) is a fitness function of the individual X; θ (X) is the angle between individual Y and the smallest angle X;
Figure BDA0003140527850000061
is fiNormalized function of (X), let fiValue of (X) to [0,1](ii) a d (X) is a vector
Figure BDA0003140527850000062
The Euclidean distance of (c);
Figure BDA0003140527850000063
is a normalized target vector for individual X, as shown in equation (21):
Figure BDA0003140527850000064
Figure BDA0003140527850000065
wherein cos (θ (X)) represents an angle between the individual X and the individual Y whose angle is the smallest, and d (X) represents objective function information of the individual X;
step 3-5, judging whether the maximum evolutionary algebra G is reachedmaxAnd if so, outputting the optimal solution in the population, otherwise, turning to the step 3-2.
Has the beneficial effects that: the invention provides a novel high-dimensional multi-target service quality evaluation model of a wireless sensor network, which comprises the targets of coverage rate, connectivity, energy consumption, life cycle, time delay, economy and the like, so that the performance requirement of the wireless sensor network can be reflected more comprehensively. The method comprises an ion population liquid state updating mode based on isotropic optimal ion and anisotropic optimal ion cooperative guidance, an ion population solid state updating method based on anisotropic optimal ion guidance and a self-adaptive evaluation function method, and effectively improves the algorithm convergence speed, so that the resource allocation scheme of a wireless sensor network can be quickly adjusted, and the overall performance of the network is enhanced.
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The above and other advantages of the present invention will become more apparent from the following detailed description of the invention when taken in conjunction with the accompanying drawings.
Fig. 1 is a schematic diagram of a hybrid network aware model.
Fig. 2 is a flow chart of the method of the present invention.
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments.
As shown in fig. 2, the present invention provides a high-dimensional multi-objective collaborative optimization method for resource scheduling of a wireless sensor network, including:
step 1: the method comprises the steps of establishing a WSN perception model of the hybrid wireless sensor network, wherein the WSN perception model comprises a large number of static nodes, a proper amount of mobile nodes and relay nodes. Setting the communication radius of a static node as rsThe communication radius of the mobile node is rmAnd if the communication radius of the relay node is R, the hybrid WSN sensing model is as shown in fig. 1.
(step 2, according to the step 1, establishing a WSN high-dimensional multi-target service quality evaluation model comprising the targets of coverage rate, connectivity, energy consumption, life cycle, time delay, economy and the like.)
Step 2: in order to comprehensively evaluate the performance of the WSN, a WSN high-dimensional multi-target service quality evaluation model comprising the targets of coverage rate, connectivity, energy consumption, life cycle, time delay, economy and the like is established.
Step 2.1: and establishing a coverage rate evaluation function. Firstly, discretizing a two-dimensional plane area to be monitored into a grid shape, wherein the precision can be determined according to actual needs, such as 1m multiplied by 1 m; then, the total number N of grids in the whole monitoring area is calculated total(ii) a Secondly, the total number of grids covered by all the wireless sensor nodes in the working state of the WSN is calculated
Figure BDA0003140527850000071
i is 1,2, …, N, N is the number of working nodes,
Figure BDA0003140527850000072
the number of grids covered for each node; establishing a WSN coverage evaluation function CrComprises the following steps:
Figure BDA0003140527850000073
the position of the static node is fixed, and the coverage rate of the WSN is only related to the positions of the static node and the mobile node and the number of nodes in the working state.
Step 2.2: and establishing a connectivity evaluation function. Firstly, calculating the distances between all working wireless sensor nodes, and recording the distances as a distance matrix D; and then judging the size relationship between each element in the distance matrix D and the node sensing radius r, so as to establish a communication state between nodes in a working state, namely a communication matrix L. Where N is the number of nodes in operation, dijIs the distance between node i and node j, lijAs shown in formula (2).
Figure BDA0003140527850000074
Figure BDA0003140527850000081
The established connectivity matrix L is a symmetric matrix and the elements therein are not 0, i.e. 1. Further analysis can show that the WSN formed by all the working nodes has connectivity which must satisfy the following conditions: 1) rank (l) ═ N; 2) sum (L) ≧ 2 (N-1). The condition 1 indicates that the rank of the connectivity matrix L is N, and it is ensured that any node has a node capable of communicating with the node. Condition 2 indicates that the sum of all elements of the connectivity matrix is greater than or equal to 2(N-1), ensuring that there is at least one other node with which any node can communicate. Establishing a WSN connectivity evaluation function C as follows:
max C＝max Rank(L) (3)
Figure BDA0003140527850000082
The connectivity of a WSN is largely determined by the location of the stationary and mobile nodes in an operational state.
Step 2.3: and establishing an energy consumption evaluation function. The energy consumption of the wireless sensor node is mainly spent in the data transmission and data receiving processes, and therefore, the WSN energy consumption evaluation function E is established as follows:
Figure BDA0003140527850000083
Figure BDA0003140527850000084
Figure BDA0003140527850000085
in the formula, EtEnergy consumption for transmitting data per unit time, ErEnergy consumption for receiving data per unit time, EiIs the energy consumption of the ith node in unit time, N is the number of working nodes, tiAmount of data sent for the ith node, EecFor electrical energy consumption, AecTo amplify the energy consumption, diIs the communication distance between the ith node and the next hop node, lambda is the data loss coefficient, riThe amount of data received for the ith node. The energy consumption of the WSN is mainly related to the node communication distance and the amount of data carried per unit time.
Step 2.4: and establishing a life cycle evaluation function. The WSN life cycle is considered to be terminated when certain nodes of the WSN die prematurely, so that the network cannot be connected and isolated areas appear, and normal communication among the nodes is finally influenced. The lifetime of an individual node is generally determined by the remaining energy E of the nodesAnd within unit timeData volume t of beareri+riOn the other hand, the lifetime of the node is:
Figure BDA0003140527850000091
In the formula, i is 1,2, …, N, and N is the number of the wireless sensor nodes in operation.
Es＝Eini-t×Ei (9)
In the formula, EiniIs the initial energy of the wireless sensor node, t is the working time of the node, EiIs the energy consumption of the ith node per unit time.
The WSN life period is defined as the node life period which is dead firstly due to electricity exhaustion, and a life period evaluation function L is establishednetComprises the following steps:
Lnet＝min Li,i＝1,2,…,N (10)
the life cycle of the WSN is mainly related to the node communication distance, the working time and the data volume per unit time.
Step 2.5: and establishing a time delay evaluation function. In order to reduce the WSN delay, the node wake-up time and the number of working nodes should be increased appropriately, so that there are more and faster communication paths for information transmission. The WSN delay is mainly focused on multi-hop transmission data, i.e., multi-hop relay delay. For this purpose, a WSN time delay evaluation function R is establishedtComprises the following steps:
Figure BDA0003140527850000092
wherein d is the communication path distance, r is the node communication radius,
Figure BDA0003140527850000093
is the duty cycle of the ith node, CdFor communication time, DPdIs the packet duration. The delay of the WSN is mainly related to the distance between nodes and the working time and duty ratio of the nodes.
Step 2.6: and establishing an economic evaluation function. For a single wireless sensor node, the economic cost of the single wireless sensor node mainly comprises the financial cost E fAnd deployment cost Ed. The financial cost is mainly related to purchase cost, the deployment cost is mainly related to the contribution rate of the wireless sensor nodes to the coverage in the specified area, and the larger the contribution rate is, the lower the average cost is. For this purpose, a WSN economic evaluation function E is establishedcComprises the following steps:
Figure BDA0003140527850000101
Figure BDA0003140527850000102
Figure BDA0003140527850000103
in the formula, wfAnd wdRepresents a weight coefficient, LnetFor hybrid wireless sensor network WSN lifetime, Costf(si) For the ith sensor node siFinancial cost of P(s)i) For the ith sensor node siAnd phi is a specific constant of the wireless sensor in the probability of normal work in the designated area. The economics of a WSN are primarily related to the life-time and operating-time ratio of the nodes.
Step 2.7: the method comprises the following steps of establishing a WSN high-dimensional multi-target service quality evaluation model of the hybrid wireless sensor network:
F(X)＝(f1(X),f2(X),f3(X),f4(X),f5(X),f6(X))＝(Cr,C,E,Lnet,Rt,Ec) (15)
wherein F (X) is an objective function, and X is (X)1,…,xi,…,xN) Is a variable, N is the number of nodes in the WSN,
Figure BDA0003140527850000104
parameters for the ith wireless sensor node, including node location
Figure BDA0003140527850000105
Node operating time ratio
Figure BDA0003140527850000106
Whether or not it is in working state deltai(node operating rule δi1, otherwise δi＝0)。
(step 3 according to step 2, a high-dimensional multiobjective optimization algorithm for evaluation model optimization is proposed)
And step 3: in order to solve the model established by the formula (15), a dual-ion population co-evolution high-dimensional multi-target optimization algorithm for wireless sensor network resource scheduling is provided, and meanwhile, the coverage rate, the connectivity, the energy consumption, the life cycle, the time delay and the economy are optimized.
Step 3.1: and randomly generating an initial population with the scale of P, and dividing the population into two, namely an anion population A and a cation population C. Setting maximum evolution algebra Gmax。
Figure BDA0003140527850000107
Step 3.2: and updating the liquid state of the ion population based on cooperative guidance of the isotropic optimal ions and the anisotropic optimal ions.
Ai(t+1)＝Ai(t)+rand()×(Cbest(t)-Ai(t))+rand()×(Abest(t)-Ai(t)) (16)
Ci(t+1)＝Ci(t)+rand()×(Abest(t)-Ci(t))+rand()×(Cbest(t)-Ci(t)) (17)
Where t is the number of evolutionary iterations, Ai(t) represents the ith anion, Ci(t) represents the ith cation, cbest (t) represents the optimum cation in the t generation, abest (t) represents the optimum anion in the t generation, and rand () is a random number between 0 and 1.
Step 3.3: and updating the solid state of the ion population based on heterosexual optimal ion guidance.
Ai(t+1)＝Ai(t)+rand()×(Cbest(t)-Ai(t)) (18)
Ci(t+1)＝Ci(t)+rand()×(Abest(t)-Ci(t)) (19)
Step 3.4: and (3) carrying out high-dimensional multi-target individual selection operation, and establishing a fitness evaluation function of self-adaptive adjustment, as shown in a formula (20). The function mainly aims at the population distribution in the early stage of population evolution and mainly aims at the population convergence in the later stage of evolution, so that the diversity and the convergence of the algorithm are effectively balanced.
Figure BDA0003140527850000111
Figure BDA0003140527850000112
Where t is the number of evolutionary iterations, GmaxFor maximum number of evolutionary iterations, X is the individual in either the anion population a or the cation population C.
Figure BDA0003140527850000113
Is the normalized target vector for individual X, as shown in equation (22).
Figure BDA0003140527850000114
Figure BDA0003140527850000115
Note that X and Y are taken in both populations a or C. Wherein cos (θ (X)) represents the angle between the individual X and the individual Y with the smallest angle, and is measured as the distribution of the algorithm, d (X) represents the objective function information of the individual X, and is measured as the convergence of the algorithm.
Step 3.5: judging whether the maximum evolution algebra G is reachedmaxIf yes, outputting the optimal solution in the population, otherwise, turning to the step 3.2.
The invention provides a high-dimensional multi-objective collaborative optimization method for wireless sensor network resource scheduling, and a plurality of methods and approaches for implementing the technical scheme, and the above description is only a preferred embodiment of the invention, and it should be noted that, for those skilled in the art, a plurality of improvements and modifications can be made without departing from the principle of the invention, and these improvements and modifications should also be regarded as the protection scope of the invention. All the components not specified in this embodiment can be implemented by the prior art.

Claims (1)

1. The high-dimensional multi-objective collaborative optimization method for wireless sensor network resource scheduling is characterized by comprising the following steps of:
step 1, establishing a WSN (wireless sensor network) perception model of a hybrid wireless sensor network;
Step 2, establishing a WSN high-dimensional multi-target service quality evaluation model of the hybrid wireless sensor network;
step 3, optimizing a WSN high-dimensional multi-target service quality evaluation model of the hybrid wireless sensor network;
in step 1, the WSN perception model of the hybrid wireless sensor network comprises a static node, a mobile node and a relay node, and the communication radius of the static node is set to be rsThe communication radius of the mobile node is rmThe communication radius of the relay node is R;
the step 2 comprises the following steps:
step 2-1, establishing a coverage rate evaluation function;
step 2-2, establishing a connectivity evaluation function;
step 2-3, establishing an energy consumption evaluation function;
step 2-4, establishing a life cycle evaluation function;
step 2-5, establishing a time delay evaluation function;
2-6, establishing an economic evaluation function;
2-7, establishing a WSN high-dimensional multi-target service quality evaluation model of the hybrid wireless sensor network;
step 2-1 comprises: discretizing two-dimensional plane area to be monitored into a grid shape, and calculating the total number N of the gridtotal(ii) a Calculating the total number of grids covered by all wireless sensor nodes in the working state of the WSN (wireless sensor network)
Figure FDA0003631477360000011
i is 1,2, …, N, N is the number of working nodes,
Figure FDA0003631477360000012
The number of grids covered by the ith node;
establishing a coverage rate evaluation function CrComprises the following steps:
Figure FDA0003631477360000013
step 2-2 comprises: calculating the distances between all working wireless sensor nodes and recording the distances as a distance matrix D; then, the magnitude relation between each element in the distance matrix D and the node perception radius r is judged, so that the communication state between nodes in the working state, namely a communication matrix L is established, wherein DijIs the distance between node i and node j, lijThe relationship between the node i and the node j is shown in formula (2):
Figure FDA0003631477360000021
Figure FDA0003631477360000022
the established connection matrix L is a symmetric matrix, and elements in the symmetric matrix are not 0, namely 1; the WSN formed by all the working nodes has connectivity which must satisfy the following conditions:
condition 1: rank (l) is equal to N,
condition 2: sum (L) is more than or equal to 2(N-1),
wherein rank (L) is the rank of the matrix L, and sum (L) is the sum of all elements of the matrix L;
the condition 1 indicates that the rank of the connectivity matrix L is N, and any node is ensured to have a node capable of communicating with the node; the condition 2 represents that the sum of all elements of the connectivity matrix is more than or equal to 2(N-1), and at least one other node capable of communicating with any node is ensured to exist in any node;
establishing a connectivity evaluation function C as follows:
max C＝max Rank(L) (3)
Figure FDA0003631477360000023
the step 2-3 comprises the following steps: establishing an energy consumption evaluation function E as follows:
Figure FDA0003631477360000024
Figure FDA0003631477360000025
Figure FDA0003631477360000026
In the formula, EtEnergy consumption for transmitting data per unit time, ErEnergy consumption for receiving data per unit time, EiEnergy consumption per unit time for the ith node, tiAmount of data sent for the ith node, EecFor electrical energy consumption, AecTo amplify the energy consumption, diIs the communication distance between the ith node and the next hop node, lambda is the data loss coefficient, riThe amount of data received for the ith node;
the steps 2-4 comprise: life of the ith nodePeriod LiComprises the following steps:
Figure FDA0003631477360000031
Es＝Eini-t×Ei (9)
in the formula, EsResidual energy for wireless sensor nodes, EiniIs the initial energy of the wireless sensor node, t is the working time of the node, EiEnergy consumption of the ith node in unit time;
establishing life period evaluation function LnetComprises the following steps:
Lnet＝min Li (10)
the steps 2-5 comprise: establishing a time delay evaluation function RtComprises the following steps:
Figure FDA0003631477360000032
where d is the communication path distance,
Figure FDA0003631477360000033
is the duty cycle of the ith node, CdFor communication time, DPdIs the packet duration;
the steps 2-6 comprise: establishing a WSN economic evaluation function EcComprises the following steps:
Figure FDA0003631477360000034
Figure FDA0003631477360000035
Figure FDA0003631477360000036
in the formula, wfAnd wdRepresents a weight coefficient, LnetFor hybrid wireless sensor network WSN lifetime, Costf(si) For the ith sensor node siFinancial cost of P(s)i) For the ith sensor node siThe probability of normal work in a designated area, phi is a specific constant of the wireless sensor;
Figure FDA0003631477360000037
In order to be a financial cost,
Figure FDA0003631477360000038
to the deployment cost;
the steps 2-7 comprise: the method for establishing the WSN high-dimensional multi-target service quality evaluation model comprises the following steps:
F(X)＝(f1(X),f2(X),f3(X),f4(X),f5(X),f6(X))＝(Cr,C,E,Lnet,Rt,Ec) (16)
wherein F (X) is an objective function, and X is (X)1,…,xi,…,xN) Is a variable of the number of the main chain,
Figure FDA0003631477360000041
parameters of the ith wireless sensor node comprise position coordinates of the node
Figure FDA0003631477360000042
Node operating time ratio
Figure FDA0003631477360000043
Whether or not it is in working state deltaiNode operating rule deltai1, otherwise δi＝0；f1(X),f2(X),f3(X),f4(X),f5(X),f6(X) corresponds to 6 functions C, respectivelyr,C,E,Lnet,Rt,Ec；
The step 3 comprises the following steps:
step 3-1, randomly generating an initial population with the scale of P, dividing the population into two, namely an anion population A and a cation population C, and setting the maximum evolution generation Gmax：
Figure FDA0003631477360000044
Wherein the content of the first and second substances,
Figure FDA0003631477360000045
the p/2 th row of A is the p/2 th anion individual;
Figure FDA0003631477360000046
the p/2 th row of C is the p/2 th cation individual;
Figure FDA0003631477360000047
is an element of row p/2 and column N of the anion population A;
Figure FDA0003631477360000048
is an element of row p/2 and column N of cation population C;
and 3-2, updating the liquid state of the ion population based on cooperative guidance of the isotropic optimal ions and the anisotropic optimal ions:
Ai(t+1)＝Ai(t)+rand()×(Cbest(t)-Ai(t))+rand()×(Abest(t)-Ai(t))， (17)
Ci(t+1)＝Ci(t)+rand()×(Abest(t)-Ci(t))+rand()×(Cbest(t)-Ci(t))， (18)
where t is the number of evolutionary iterations, Ai(t) represents the ith anion, Ci(t) represents the ith cation, Cbest (t) represents the optimum cation in the t generation, abest (t) represents the optimum anion in the t generation And rand () is a random number between 0 and 1;
step 3-3, updating the solid state of the ion population based on heterosexual optimal ion guidance:
Ai(t+1)＝Ai(t)+rand()×(Cbest(t)-Ai(t)) (17)
Ci(t+1)＝Ci(t)+rand()×(Abest(t)-Ci(t)) (18)；
step 3-4: carrying out high-dimensional multi-target individual selection operation, and establishing a fitness evaluation function which is adaptively adjusted, wherein the fitness evaluation function is shown as a formula (19):
Figure FDA0003631477360000051
Figure FDA0003631477360000052
where t is the number of evolutionary iterations, GmaxFor maximum number of evolutionary iterations, X is an individual in the anion population a or the cation population C; fit (X) is a fitness function of the individual X; θ (X) is the angle between individual Y and the smallest angle X;
Figure FDA0003631477360000053
is fiNormalized function of (X), let fiValue of (X) to [0,1](ii) a d (X) is a vector
Figure FDA0003631477360000054
The Euclidean distance of (c);
Figure FDA0003631477360000055
is the normalized target vector for individual X, as shown in equation (21):
Figure FDA0003631477360000056
Figure FDA0003631477360000057
wherein cos (θ (X)) represents an angle between the individual X and the individual Y whose angle is the smallest, and d (X) represents objective function information of the individual X;
step 3-5, judging whether the maximum evolution algebra G is reachedmaxAnd if so, outputting the optimal solution in the population, otherwise, turning to the step 3-2.
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