CN113422695A - Optimization method for improving robustness of topological structure of Internet of things - Google Patents
Optimization method for improving robustness of topological structure of Internet of things Download PDFInfo
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Abstract
The invention relates to an optimization method for improving the robustness of an IOT topological structure, which comprises the steps of selecting a network Motif which accords with 4 nodes from an initialized IOT topological structure as a minimum operation unit, selecting all minimum operation units with reconnectable edges from all the minimum operation units as operation units respectively, changing the edge connection relation of the selected minimum operation units for multiple times respectively, wherein the change operation is not completely the same each time, thus obtaining a plurality of new IOT topological structures, forming a population by the plurality of new IOT topological structures, constructing a robustness measurement index to optimize each new IOT topological structure in the population by utilizing a distributed artificial immune optimization algorithm, outputting the IOT topological structure corresponding to the optimal robustness measurement index as an optimal IOT topological structure, the malicious attack resistance of the topological structure of the Internet of things is effectively improved.
Description
Technical Field
The invention relates to the field of Internet of things, in particular to an optimization method for improving the robustness of a topological structure of the Internet of things.
Background
The internet of things is used as a complex integrating multiple disciplines and an important support part for constructing a smart city, and application objects of the internet of things are continuously integrated into various fields of society and play more and more important roles in realizing interconnection of everything.
The internet of things generally comprises a data service center and a large number of monitoring devices (such as sensors), wherein the monitoring devices can send respective monitored data to the data service center, and then analyze and process the data through the data service center, so that high-quality service is provided for various application scenarios. The monitoring devices are arranged in the internet of things in a topological structure, that is, each monitoring device is distributed at different positions in the internet of things in a topological structure, and the internet of things used in different application scenarios generally have different network topologies.
In an internet of things topology structure arranged in an actual application scenario, an existing internet of things topology structure is often fixed, that is, most nodes (i.e., monitoring devices) in the internet of things are located at relatively fixed positions. When monitoring equipment arranged by the existing topology structure of the internet of things is used for transmitting data to a data service center in the internet of things, the existing network topology structure has low robustness and cannot support the transmission of a large amount of data, and especially when the network topology structure faces threats, the efficiency and the safety of the transmission of a large amount of data are difficult to ensure.
Therefore, how to effectively improve the robustness of the existing internet of things topology structure, resist external attacks, and ensure the efficiency and reliability of data transmission becomes an important technical problem to be solved urgently in the field of the current internet of things.
Disclosure of Invention
The technical problem to be solved by the invention is to provide an optimization method for the robustness of the topology structure of the internet of things in view of the prior art. By the method, the robustness of the topological structure of the Internet of things can be effectively improved, external attack is resisted, and the data transmission efficiency and reliability are ensured.
The technical scheme adopted by the invention for solving the technical problems is as follows: an optimization method for improving robustness of a topology structure of the Internet of things is characterized by comprising the following steps of 1-7:
step 1, generating an initialized topology structure of the Internet of things based on rules of a scale-free network model, and randomly deploying a plurality of network topology nodes in the topology structure of the Internet of things; in the initialized topology structure of the internet of things, each network topology node corresponds to a fixed geographical position, and all the network topology nodes have the same attribute;
step 2, extracting all networks Motif which accord with 4 nodes from the initialized topological structure of the Internet of things according to the networks Motif, and taking each extracted network Motif as a minimum operation unit in the optimization process of the topological structure of the Internet of things;
step 3, selecting all minimum operation units with the reconnectable edges from all the extracted minimum operation units as operation units respectively;
step 4, changing the connection relation of the extracted part of operation units in the initialized topological structure of the Internet of things, and taking the topological structure of the Internet of things with the changed connection relation of the sides as a new topological structure of the Internet of things;
step 5, repeatedly executing the operation of the step 4 for multiple times respectively to obtain a plurality of new Internet of things topological structures, and forming a group by the new Internet of things topological structures; wherein, the change operation of the edge connection relation in each operation is not completely the same, and each new IOT topological structure in the population is taken as an individual;
step 6, constructing a robust performance measurement index for measuring the robust performance of the topological structure of the Internet of things;
and 7, optimizing each formed new Internet of things topological structure in the population by using a distributed artificial immune optimization algorithm, and outputting the Internet of things topological structure with the optimal robustness performance measurement index as the optimal Internet of things topological structure.
In step 1, the probability of a network topology node before a new network topology node added to the internet of things is connected is positively correlated with the degree of the previous network topology node. That is, the greater the degree of the previous network topology node, the greater the probability that the new node connects to the previous network topology node.
Further, in the optimization method for improving the robustness of the topology structure of the internet of things, in step 6, the construction process of the robustness measurement index comprises the following steps 61-65:
step 61, performing accumulative statistics on the number of networks Motif conforming to 3 nodes contained in the initialized topology structure of the Internet of things after each network attack;
step 62, acquiring the total number of edges of the initialized topology structure of the internet of things and the total number of network topology nodes of the initialized topology structure of the internet of things; the total number of edges of the initialized topological structure of the Internet of things is marked as E, and the total number of network topological nodes of the initialized topological structure of the Internet of things is marked as V, wherein V is greater than 3;
step 63, acquiring the total number of edges of a union set formed by all networks Motif conforming to 3 nodes in the topological structure of the Internet of things after the kth network attack; wherein, the total number of edges of the union set formed by all networks Motif conforming to 3 nodes is marked as MC (k), and k is more than or equal to 1;
step 64, judging and processing according to the counted number of the networks Motif conforming to the 3 nodes:
when the number of the network Motif conforming to the 3 nodes is zero, the step 65 is carried out; otherwise, go to step 61;
step 65, normalizing the number of the network Motif which accords with the 3 nodes and is obtained through statistics, and taking the numerical value obtained after normalization as the robust performance measurement index; wherein, the robust performance measurement index is marked as I:
in the optimization method for improving the robustness of the topology structure of the internet of things, in step 7, the output process of the topology structure of the internet of things with the optimal robustness performance measurement index as the optimal topology structure of the internet of things comprises the following steps 71-77:
step 71, setting N local optimization programs and 1 global optimization program; the local optimization programs are mutually independent, each local program runs a population P, each local program respectively carries out population crossing operation, mutation operation and selection operation on the running population P, and the nth local optimization program is marked as LnN is more than or equal to 1 and less than or equal to N, and the global optimization program is marked as GL;
step 72, defining a cross-operation strategy:
motifi,motifj←Gi(,loc),Gj(,loc)；
wherein G isi(, loc) and Gj(. loc) represents two different individuals of the same population with the intercross position at loc, and the short side of the intercross position chromosome is selected for searching, the chromosome is composed of all networks Motif conforming to 4 nodes, and one of the Motif in the chromosome is called as Motif base, and the MotifiOne of the networks Motif, Motif conforming to 4 nodes represented in one type of individualjIs represented by formula (I) and motifiOne of the individuals of the other type, which are in the same type as the individual, conforms to the network Motif of 4 nodes and is identical with the MotifiA cross-linkable network Motif;
for two networks MotifmotifiWith motifjType of (2) making judgment processing: when motifiWith motifjWhen the two networks are of the same type, performing cross operation on the two networks Motif; otherwise, continuously searching two networks Motif which can be operated in a cross mode in the same population;
step 73, defining a mutation operation strategy:
aiming at an individual G in the population P, extracting all operation units conforming to 4 network Motif to form a chromosome;
randomly assigning partially variable chromosomal Motif base positions; wherein, if the chromosome Motif base position is Motif with repeatable connection edge relation, then carrying out reconnection edge; otherwise, the next base position is designated randomly and continuously for judgment;
step 74, defining a selection operation strategy:
PGL＝{Lr,Lt,…,Lz}；
wherein, PGLRepresenting the population of global optimiser GL runs, the population PGLSelecting Elite population individuals L by the ontology optimization program GL by adopting different selection strategies respectivelyr、Lt、…、LzThe components are mixed; after the local optimization program GL finishes the cross variation operation, calculating the robust performance index of each individual, and selecting 2 elite individuals with the maximum robust performance index values to transmit to the global optimization program GL; meanwhile, the global optimization program GL is provided with a communication queue Q for storing the elite population individuals selected by the local optimization program;
in the global optimization program GL, the initial global optimization program directly selects 2 populations in the communication queue Q; then, selecting a group individual from the communication queue Q;
if the robustness performance measurement index of the selected population individual is superior to the average value of the robustness performance measurement indexes of the global population, the population individual is selected; otherwise, continuing to select the next population individual in the communication queue Q;
step 75, define the "federal-state" communication mechanism and global optimization mechanism:
setting a communication queue Q in the global optimization program GL for storing the elite population individuals selected by the local optimization program; in each iteration process, the global optimization program GL selects an elite population individual from the communication queue Q, and the selected elite population individual replaces the population individual with the lowest robust performance measurement index in the original population corresponding to the global optimization program GL;
and step 76, judging the output robust performance measurement index and the iteration frequency:
when the floating range of the output robust performance measurement index is within the preset floating range and the currently executed iteration number does not exceed the preset maximum iteration number, storing the robust performance measurement index, and turning to step 77; otherwise, continuing to execute iteration until the executed iteration number reaches the preset maximum iteration number, and terminating the iteration process;
and 77, taking the internet of things topological structure corresponding to the stored robust performance measurement index as the internet of things topological structure with the optimal robust performance measurement index.
Compared with the prior art, the invention has the advantages that: according to the method, after a network Motif which accords with 4 nodes is selected from an initialized topological structure of the Internet of things as a minimum operation unit, all minimum operation units with reconnectable edges are selected from all the minimum operation units to be respectively used as operation units, the edge connection relation of the selected minimum operation units is changed for multiple times respectively aiming at the selected minimum operation units, the change operation is not completely the same every time, a plurality of new topological structures of the Internet of things are obtained, a group is formed by the plurality of new topological structures of the Internet of things, a robust performance measurement index is constructed to optimize each formed new topological structure of the Internet of things in the group by utilizing a distributed artificial immune optimization algorithm, and the corresponding topological structure of the Internet of things with the optimal robust performance measurement index is output as the optimal topological structure of the Internet of things.
The traditional internet of things topological structure optimization scheme usually adopts a genetic optimization algorithm using a centralized computing mode, and has the defects of high computing overhead, poor population diversity and easiness in falling into an early convergence state. Different from the individual composition of the traditional genetic algorithm, the invention adopts the network Motif conforming to 4 nodes as the gene composition of an individual (namely each new Internet of things topological structure), reduces the searching cost of subsequent crossing and variation, and adopts the distributed artificial immunity algorithm, so that the calculation cost can be reduced, the population diversity can be improved, the global optimal solution (namely the Internet of things topological structure with the optimal robust performance measurement index) can be searched more quickly, the capability of resisting malicious attack of the Internet of things topological structure can be effectively improved on the basis of fully measuring the network topological structure, the risk of paralysis of the Internet of things caused by attack can be reduced, and the data transmission efficiency and reliability can be further ensured.
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Fig. 1 is a schematic flow chart of an optimization method for improving the robustness of the topology structure of the internet of things in the embodiment of the invention.
Detailed Description
The invention is described in further detail below with reference to the accompanying examples.
The embodiment provides an optimization method for improving robustness of an internet of things topological structure, which is suitable for the internet of things topological structure formed by a plurality of network topological nodes (hereinafter referred to as nodes). Referring to fig. 1, the optimization method for improving the robustness of the topology structure of the internet of things in the embodiment includes the following steps 1-7:
step 1, generating an initialized topology structure of the Internet of things based on rules of a scale-free network model, and randomly deploying a plurality of network topology nodes in the topology structure of the Internet of things; in the initialized topology structure of the internet of things, each network topology node corresponds to a fixed geographical position, and all the network topology nodes have the same attribute; in addition, the probability of the network topology node before the new network topology node added into the topology structure of the internet of things is connected is positively correlated with the degree of the previous network topology node;
for example, suppose that M network topology nodes are randomly deployed in the initialized topology of the internet of things, and the mth network topology node is labeled as GmThe geographic position coordinates of the network topology node are
Step 2, extracting all networks Motif which accord with 4 nodes from the initialized topological structure of the Internet of things according to the networks Motif, and taking each extracted network Motif as a minimum operation unit in the optimization process of the topological structure of the Internet of things; wherein, in this technical field, network Motif or Motif is a technical term well known to those skilled in the art, Motif refers to a type of subgraph, and the number of certain interconnected patterns found in the subgraph in a complex network is significantly higher than that in a random network. The network Motif conforming to 4 nodes is an undirected graph formed by 4 nodes (namely four network topology nodes);
suppose that Q networks Motif conforming to 4 nodes are obtained by the extraction operation of the network Motif conforming to 4 nodes in the step 2, and the Q-th network Motif conforming to 4 nodes is marked as MotifqQ is more than or equal to 1 and less than or equal to Q, and each MotifqAll are used as the minimum operation unit in the topology optimization process of the internet of things, and the minimum operation unit is marked as unitqI.e. unitq＝Motifq；
Step 3, selecting all minimum operation units with the reconnectable edges from all the extracted minimum operation units as operation units;
through the extraction operation in step 2, it is assumed that Q minimum operation unit units are obtained1～unitQThen, in step 3, all the minimum operation units with the reconnectable edges are selected from the Q minimum operation units as operation units, and it is assumed that all the selected minimum operation units with the reconnectable edges are units respectively1、unit3、unit4And unit5Then, here again the minimum unit of operation unit will be used1As an operation Unit1The minimum unit of operation unit3As an operation Unit3The minimum unit of operation unit4As an operation Unit4And minimum unit of operation unit5As an operation Unit5；
Step 4, changing the connection relation of the extracted part of operation units in the initialized topological structure of the Internet of things, and taking the topological structure of the Internet of things with the changed connection relation of the sides as a new topological structure of the Internet of things;
assume that the initialized IOT topology in this embodiment is labeled C0Then aiming at the operation Unit in the selected four operation units4And an operation Unit5The two operation units execute the first change operation of changing the connection relation of the edges, thus, the initialized topology structure C of the Internet of things0After the operation of changing the topology structure, the topology structure will change, then the topology structure of the internet of things with the changed edge connection relationship is used as a new topology structure of the internet of things, and the new topology structure of the internet of things obtained after the operation of changing the edge connection relationship for the first time is marked as C1；
Step 5, repeatedly executing the operation of the step 4 for multiple times respectively to obtain a plurality of new Internet of things topological structures, and forming a group by the new Internet of things topological structures; wherein, the change operation of the edge connection relation in each operation is not completely the same, and each new IOT topological structure in the population is taken as an individual;
then, as illustrated in step 4 above, in the topology C of the internet of things for initialization0Executing second change operation for partial operation units, and marking the new IOT topological structure obtained after the first change operation as C2(ii) a If 5 times of changing operation is performed and each operation is not completely the same, 5 new internet of things topological structures are obtained, namely a new internet of things topological structure C1New internet of things topological structure C2New internet of things topological structure C3New internet of things topological structure C4And a new IOT topology C5And again by these 5 new IOT topologies C1～C5Together forming a population S, S ═ { C1,C2,C3,C4,C5}; thus, each new IOT topology C in the population S1～C5As an individual;
step 6, constructing a robust performance measurement index for measuring the robust performance of the topological structure of the Internet of things; the construction process of the robust performance measurement index comprises the following steps 61-65:
step 61, performing accumulative statistics on the number of networks Motif conforming to 3 nodes contained in the initialized topology structure of the Internet of things after each network attack;
step 62, acquiring the total number of edges of the initialized topology structure of the internet of things and the total number of network topology nodes of the initialized topology structure of the internet of things; the total number of edges of the initialized topological structure of the Internet of things is marked as E, and the total number of network topological nodes of the initialized topological structure of the Internet of things is marked as V, wherein V is greater than 3;
step 63, acquiring the total number of edges of a union set formed by all networks Motif conforming to 3 nodes in the topological structure of the Internet of things after the kth network attack; wherein, the total number of edges of the union set formed by all networks Motif conforming to 3 nodes is marked as MC (k), and k is more than or equal to 1;
it should be noted that, in this step 63, the topology of the internet of things is subjected to network attack for the kth time, and we perform union operation on all edge sets of the networks Motif that conform to the 3 nodes, then remove repeated edges to obtain a network topology, and count the number of edges included in the network topology, where the count obtained by the statistics is the total number of edges mc (k) of a union set that is composed of all the networks Motif that conform to the 3 nodes;
step 64, judging and processing according to the counted number of the networks Motif conforming to the 3 nodes:
when the number of the network Motif conforming to the 3 nodes is zero, the step 65 is carried out; otherwise, go to step 61;
step 65, normalizing the number of the network Motif which accords with the 3 nodes and is obtained through statistics, and taking the numerical value obtained after normalization as the robust performance measurement index; wherein, the robust performance measurement index is marked as I:
and 7, optimizing each formed new Internet of things topological structure in the population by using a distributed artificial immune optimization algorithm, and outputting the Internet of things topological structure with the optimal robustness performance measurement index as the optimal Internet of things topological structure. The output process of the IOT topological structure with the optimal robust performance measurement index as the optimal IOT topological structure comprises the following steps 71-77:
step 71, setting N local optimization programs and 1 global optimization program; the local optimization programs are mutually independent, each local program runs a population P, each local program respectively carries out population crossing operation, mutation operation and selection operation on the running population P, and the nth local optimization program is marked as LnN is more than or equal to 1 and less than or equal to N, and the global optimization program is marked as GL;
step 72, defining a cross-operation strategy:
motifi,motifj←Gi(,loc),Gj(,loc)；
wherein G isi(, loc) and Gj(. loc) represents two different individuals of the same population with the intercross position at loc, and the short side of the intercross position chromosome is selected for searching, the chromosome is composed of all networks Motif conforming to 4 nodes, and one of the Motif in the chromosome is called as Motif base, and the MotifiOne of the networks Motif, Motif conforming to 4 nodes represented in one type of individualjIs represented by formula (I) and motifiOne of the individuals of the other type, which are in the same type as the individual, conforms to the network Motif of 4 nodes and is identical with the MotifiA cross-linkable network Motif;
for two networks MotifmotifiWith motifjType of (2) making judgment processing: when motifiWith motifjWhen the two networks are of the same type, performing cross operation on the two networks Motif; otherwise, continuously searching two networks Motif which can be operated in a cross mode in the same population;
step 73, defining a mutation operation strategy:
aiming at an individual G in the population P, extracting all operation units conforming to 4 network Motif to form a chromosome;
randomly assigning partially variable chromosomal Motif base positions; wherein, if the chromosome Motif base position is Motif with repeatable connection edge relation, then carrying out reconnection edge; otherwise, the next base position is designated randomly and continuously for judgment;
step 74, defining a selection operation strategy:
PGL＝{Lr,Lt,…,Lz}；
wherein, PGLRepresenting the population of global optimiser GL runs, the population PGLSelecting Elite population individuals L by the ontology optimization program GL by adopting different selection strategies respectivelyr、Lt、…、LzThe components are mixed; after the local optimization program GL finishes the cross variation operation, the robust performance index of each individual is calculated, 2 elite individuals with the maximum robust performance index values are selected and transmitted to the global optimization program GL, the global optimization program GL receives the elite population individuals, and then the optimization operation is continued; meanwhile, the global optimization program GL is provided with a communication queue Q for storing the elite population individuals selected by the local optimization program; the elite population individuals are individuals with the maximum robust performance index in a population, namely the best topological structure of the Internet of things;
in the global optimization program GL, the initial global optimization program directly selects 2 populations in the communication queue Q; then, selecting a group individual from the communication queue Q;
if the robustness performance measurement index of the selected population individual is superior to the average value of the robustness performance measurement indexes of the global population, the population individual is selected; otherwise, continuing to select the next population individual in the communication queue Q;
step 75, define the "federal-state" communication mechanism and global optimization mechanism:
setting a communication queue Q in the global optimization program GL for storing the elite population individuals selected by the local optimization program; in each iteration process, the global optimization program GL selects an elite population individual from the communication queue Q, and the selected elite population individual replaces the population individual with the lowest robust performance measurement index in the original population corresponding to the global optimization program GL; the "iteration" referred to herein is the operation of repeatedly performing step 7;
and step 76, judging and reading the output robust performance measurement indexes and the iteration times:
when the output robust performance measure index has a floating range within a preset floating range, for example, the preset floating range is not higher than 0.001, and the currently executed iteration number does not exceed the preset maximum iteration number (for example, the preset maximum iteration number is set to 1000), storing the robust performance measure index, and going to step 77; otherwise, continuing to execute iteration until the executed iteration number reaches the preset maximum iteration number, and terminating the iteration process;
and 77, taking the internet of things topological structure corresponding to the stored robust performance measurement index as the internet of things topological structure with the optimal robust performance measurement index.
Although preferred embodiments of the present invention have been described in detail hereinabove, it should be clearly understood that modifications and variations of the present invention are possible to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (4)
1. An optimization method for improving robustness of a topology structure of the Internet of things is characterized by comprising the following steps of 1-7:
step 1, generating an initialized topology structure of the Internet of things based on rules of a scale-free network model, and randomly deploying a plurality of network topology nodes in the topology structure of the Internet of things; in the initialized topology structure of the internet of things, each network topology node corresponds to a fixed geographical position, and all the network topology nodes have the same attribute;
step 2, extracting all networks Motif which accord with 4 nodes from the initialized topological structure of the Internet of things according to the networks Motif, and taking each extracted network Motif as a minimum operation unit in the optimization process of the topological structure of the Internet of things;
step 3, selecting all minimum operation units with the reconnectable edges from all the extracted minimum operation units as operation units;
step 4, changing the connection relation of the extracted part of operation units in the initialized topological structure of the Internet of things, and taking the topological structure of the Internet of things with the changed connection relation of the sides as a new topological structure of the Internet of things;
step 5, repeatedly executing the operation of the step 4 for multiple times respectively to obtain a plurality of new Internet of things topological structures, and forming a group by the new Internet of things topological structures; wherein, the change operation of the edge connection relation in each operation is not completely the same, and each new IOT topological structure in the population is taken as an individual;
step 6, constructing a robust performance measurement index for measuring the robust performance of the topological structure of the Internet of things;
and 7, optimizing each formed new Internet of things topological structure in the population by using a distributed artificial immune optimization algorithm, and outputting the Internet of things topological structure with the optimal robustness performance measurement index as the optimal Internet of things topological structure.
2. The optimization method for improving the robustness of the topology structure of the internet of things according to claim 1, wherein in step 1, the probability of a network topology node before a new network topology node added to the topology structure of the internet of things is connected is positively correlated with the degree of the previous network topology node.
3. The optimization method for improving the robustness of the topology structure of the internet of things according to claim 1, wherein in the step 6, the construction process of the robustness performance measurement index comprises the following steps 61-65:
step 61, performing accumulative statistics on the number of networks Motif conforming to 3 nodes contained in the initialized topology structure of the Internet of things after each network attack;
step 62, acquiring the total number of edges of the initialized topology structure of the internet of things and the total number of network topology nodes of the initialized topology structure of the internet of things; the total number of edges of the initialized topological structure of the Internet of things is marked as E, and the total number of network topological nodes of the initialized topological structure of the Internet of things is marked as V, wherein V is greater than 3;
step 63, acquiring the total number of edges of a union set formed by all networks Motif conforming to 3 nodes in the topological structure of the Internet of things after the kth network attack; wherein, the total number of edges of the union set formed by all networks Motif conforming to 3 nodes is marked as MC (k), and k is more than or equal to 1;
step 64, judging and processing according to the counted number of the networks Motif conforming to the 3 nodes:
when the number of the network Motif conforming to the 3 nodes is zero, the step 65 is carried out; otherwise, go to step 61;
step 65, normalizing the number of the network Motif which accords with the 3 nodes and is obtained through statistics, and taking the numerical value obtained after normalization as the robust performance measurement index; wherein, the robust performance measurement index is marked as I:
4. the optimization method for improving the robustness of the topology structure of the internet of things according to claim 1, wherein in the step 7, the output process of the topology structure of the internet of things with the optimal robustness performance measure index as the optimal topology structure of the internet of things comprises the following steps 71-77:
step 71, setting N local optimization programs and 1 global optimization program; the local optimization programs are independent from each other, each local program runs a population P, and each local program pairThe running population P is respectively subjected to population crossing operation, mutation operation and selection operation, and the nth local optimization program is marked as LnN is more than or equal to 1 and less than or equal to N, and the global optimization program is marked as GL;
step 72, defining a cross-operation strategy:
motifi,motifj←Gi(,loc),Gj(,loc)；
wherein G isi(, loc) and Gj(. loc) represents two different individuals of the same population with the intercross position at loc, and the short side of the intercross position chromosome is selected for searching, the chromosome is composed of all networks Motif conforming to 4 nodes, and one of the Motif in the chromosome is called as Motif base, and the MotifiOne of the networks Motif, Motif conforming to 4 nodes represented in one type of individualjIs represented by formula (I) and motifiOne of the individuals of the other type, which are in the same type as the individual, conforms to the network Motif of 4 nodes and is identical with the MotifiA cross-linkable network Motif;
for two networks MotifmotifiWith motifjType of (2) making judgment processing: when motifiWith motifjWhen the two networks are of the same type, performing cross operation on the two networks Motif; otherwise, continuously searching two networks Motif which can be operated in a cross mode in the same population;
step 73, defining a mutation operation strategy:
aiming at an individual G in the population P, extracting all operation units conforming to 4 network Motif to form a chromosome;
randomly assigning partially variable chromosomal Motif base positions; wherein, if the chromosome Motif base position is Motif with repeatable connection edge relation, then carrying out reconnection edge; otherwise, the next base position is designated randomly and continuously for judgment;
step 74, defining a selection operation strategy:
PGL＝{Lr,Lt,…,Lz}；
wherein, PGLRepresenting global optimizer GL runsOf the population PGLSelecting Elite population individuals L by the ontology optimization program GL by adopting different selection strategies respectivelyr、Lt、…、LzThe components are mixed; after the local optimization program GL finishes the cross variation operation, calculating the robust performance index of each individual, and selecting 2 elite individuals with the maximum robust performance index values to transmit to the global optimization program GL; meanwhile, the global optimization program GL is provided with a communication queue Q for storing the elite population individuals selected by the local optimization program;
in the global optimization program GL, the initial global optimization program directly selects 2 populations in the communication queue Q; then, selecting a group individual from the communication queue Q;
if the robustness performance measurement index of the selected population individual is superior to the average value of the robustness performance measurement indexes of the global population, the population individual is selected; otherwise, continuing to select the next population individual in the communication queue Q;
step 75, define the "federal-state" communication mechanism and global optimization mechanism:
setting a communication queue Q in the global optimization program GL for storing the elite population individuals selected by the local optimization program; in each iteration process, the global optimization program GL selects an elite population individual from the communication queue Q, and the selected elite population individual replaces the population individual with the lowest robust performance measurement index in the original population corresponding to the global optimization program GL;
and step 76, judging and reading the output robust performance measurement indexes and the iteration times:
when the floating range of the output robust performance measurement index is within the preset floating range and the currently executed iteration number does not exceed the preset maximum iteration number, storing the robust performance measurement index, and turning to step 77; otherwise, continuing to execute iteration until the executed iteration number reaches the preset maximum iteration number, and terminating the iteration process;
and 77, taking the internet of things topological structure corresponding to the stored robust performance measurement index as the internet of things topological structure with the optimal robust performance measurement index.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114640594A (en) * | 2022-03-09 | 2022-06-17 | 天津大学 | Internet of things topology optimization method with hierarchical cooperation mechanism |
| CN115225509A (en) * | 2022-07-07 | 2022-10-21 | 天津大学 | Internet of things topological structure generation method based on neural evolution |
| CN115967631A (en) * | 2022-12-19 | 2023-04-14 | 天津大学 | Internet of things topology optimization method based on breadth learning and application thereof |
| CN116720293A (en) * | 2023-05-30 | 2023-09-08 | 中山大学 | Robust influence optimization method and device, electronic equipment and storage medium |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106789320A (en) * | 2017-01-04 | 2017-05-31 | 大连理工大学 | A kind of multi-species cooperative method for optimizing wireless sensor network topology |
| CN110807230A (en) * | 2019-10-29 | 2020-02-18 | 天津大学 | Method for optimizing robustness of topology structure of Internet of things through autonomous learning |
-
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106789320A (en) * | 2017-01-04 | 2017-05-31 | 大连理工大学 | A kind of multi-species cooperative method for optimizing wireless sensor network topology |
| CN110807230A (en) * | 2019-10-29 | 2020-02-18 | 天津大学 | Method for optimizing robustness of topology structure of Internet of things through autonomous learning |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114640594A (en) * | 2022-03-09 | 2022-06-17 | 天津大学 | Internet of things topology optimization method with hierarchical cooperation mechanism |
| CN115225509A (en) * | 2022-07-07 | 2022-10-21 | 天津大学 | Internet of things topological structure generation method based on neural evolution |
| CN115225509B (en) * | 2022-07-07 | 2023-09-22 | 天津大学 | Internet of things topological structure generation method based on neural evolution |
| CN115967631A (en) * | 2022-12-19 | 2023-04-14 | 天津大学 | Internet of things topology optimization method based on breadth learning and application thereof |
| CN116720293A (en) * | 2023-05-30 | 2023-09-08 | 中山大学 | Robust influence optimization method and device, electronic equipment and storage medium |
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