CN108495252B - Indoor positioning network element optimization layout method based on genetic algorithm and simulated annealing - Google Patents

Indoor positioning network element optimization layout method based on genetic algorithm and simulated annealing Download PDF

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CN108495252B
CN108495252B CN201810165059.4A CN201810165059A CN108495252B CN 108495252 B CN108495252 B CN 108495252B CN 201810165059 A CN201810165059 A CN 201810165059A CN 108495252 B CN108495252 B CN 108495252B
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冯光升
梁森
吕宏武
王慧强
刘秀兵
马福亮
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Abstract

The invention discloses an indoor positioning network element optimization layout method based on genetic algorithm and simulated annealing, belonging to the field of indoor positioning and comprising the following steps: step (1): carrying out network element layout; step (2): determining control parameters required by the adaptive genetic algorithm; and (3): initializing the network element layout; and (4): calculating the fitness; and (5): judging whether genetic convergence conditions are met; and (6): selecting a network element layout with higher fitness; and (7): performing cross operation on the binary codes to obtain filial generations; and (8): carrying out negation operation on the binary code to obtain variation; and (9): generating a new network element layout space; step (10): carrying out simulated annealing operation on the population; step (11): generating an optimal network element layout result; step (12): and outputting the optimal network element layout result, and ending. The invention has strong global search capability and local search capability, improves the positioning precision and improves the search efficiency.

Description

Indoor positioning network element optimization layout method based on genetic algorithm and simulated annealing
Technical Field
The invention belongs to the field of indoor positioning, and particularly relates to an indoor positioning network element optimization layout method based on a genetic algorithm and simulated annealing.
Background
With the development of network technology and communication technology, location services become increasingly important, and as most of human activities are performed indoors, indoor positioning is receiving more and more attention, wherein UWB-based indoor positioning technology can reach centimeter-level positioning accuracy in a specific scene, and has been widely applied to various indoor positioning products, but UWB positioning base stations are very expensive to lay, and when the number of network elements is limited, how to lay out positioning signals can maximize the coverage and maximize positioning accuracy is a significant research topic.
The problem of network element optimization layout is an NP difficult problem, and the current research is limited in the degree of finding the optimal solution by applying a heuristic algorithm. The genetic algorithm can seek to an optimal solution in a random mode in a probability sense, and the documents of Agapie A, Wright A H, the Theoretical analysis of the step state genetic algorithms [ J ]. Applications of the Mathematics,2014,59(5):509-525.the Theoretical analysis of the step state genetic algorithms "use the genetic algorithm to solve the planning problem of the wireless network, but the genetic algorithm has no feasible feedback mechanism, generates a large number of redundant iterations in some cases, and causes the problems of low efficiency and the like, and simultaneously, the genetic algorithm in practical application has the problems of easy generation of premature phenomenon, poor local optimizing capability and the like. The simulated annealing algorithm has strong local searching capability, can prevent the searching process from being trapped in a local optimal solution, is not suitable for the whole searching space, and is difficult to enter the most promising searching area. In documents "Han R, Feng C, Xia H, et al, collaborative timing for dense deployment of small cell based on public algorithm [ C ],2014ieee 802 th, ieee 2014: 1-5", the cost and coverage of network element deployment are mainly used as layout targets for the network element layout, but in the initial stage of the ant colony algorithm search, there are cases where there is little or no available information, so the ant colony algorithm convergence speed is slow. In the aspect of indoor positioning simulation, a high-precision indoor positioning simulation system is researched and implemented, and a network element layout can be input in a specific indoor scene to obtain the positioning signal coverage rate and the average positioning error of an area to be positioned.
Disclosure of Invention
The invention aims to disclose an indoor positioning network element optimization layout method based on genetic algorithm and simulated annealing, which has strong local search capability and high efficiency.
The purpose of the invention is realized as follows:
an indoor positioning network element optimization layout method based on genetic algorithm and simulated annealing comprises the following steps:
step (1): and (3) carrying out network element layout: numbering the positions of the deployable network elements in sequence, deploying the network elements, and converting the number of each network element into a binary code:
drawing grids in a plan view of a specific indoor scene, considering that intersection points of the grids on the outer wall of the indoor scene are positions where network elements can be arranged, arranging the network elements at the positions where the network elements can be arranged, numbering the network elements in sequence, and converting the number of each network element into binary coding.
Step (2): determining control parameters required by the adaptive genetic algorithm, wherein the control parameters comprise population size, maximum iteration number, cross probability and variation probability:
the control parameters required by the adaptive genetic algorithm include: population size, maximum iteration number, cross probability and mutation probability. In a network element layout, n network elements are required, the binary code of each network element is an m-bit binary number, and each network element layout is regarded as a chromosome with a length len-n-m. If the population number is k, then the population size is k × len matrix.
And (3): initializing the network element layout: randomly selecting n different network elements from all the deployable network element groups as initial network element layouts;
and (4): calculating the fitness:
fitness is as follows:
Figure BDA0001584188830000021
in the above formula, N represents the number of test points of the positioning area, ERRORiIndicating the positioning error of the positioning point i.
And (5): iteratively judging whether a genetic convergence condition is met, if the genetic convergence condition is met, jumping to the step (9), and if not, jumping to the step (6);
and (6): selecting a network element layout with higher adaptability:
generating a random number r between 0 and the total fitness, and then accompanying the population individuals in a roulette mannerMachine sampling, population individual xiProbability of being selected F (x)i)：
Figure BDA0001584188830000022
In the above formula, f (x)i) Is a population of individuals xiThe fitness of (2); and selecting the network element layout with higher fitness.
And (7): the network element layout with higher fitness is arranged according to the cross probability PbPerforming a crossover operation on the binary code to obtain a child:
adopting single-point cross operation: firstly, a random real number 0 is more than or equal to r and less than or equal to 1, and a cross probability 0 is set<Pb<1, if r<PbThen interleaving needs to be done, otherwise it is not. If the crossing is needed, the crossing position is randomly selected, and the binary string codes after the crossing position are exchanged.
And (8): the network element layout with higher fitness is arranged according to the mutation probability PyCarrying out negation operation on the binary code to obtain variation:
generating a random real number 0 ≦ r ≦ 1, if r<PyThen, variation is performed, 0<Py<1 is the mutation probability; if the variation is needed, firstly, the variation position rand chromo size needs to be determined, if the variation position rand chromo size is 0, the variation is not performed, otherwise, the binary code of the variation position is inverted to generate the variation, so that 1 becomes 0, and 0 becomes 1.
And (9): generating a local optimal network element layout result, and generating a new network element layout space according to the coordinate information:
and after the local optimal network element layout result is obtained through the self-adaptive genetic algorithm, the binary code of the local optimal network element layout result is converted into the decimal code and the corresponding coordinate information. And on the premise of keeping the height value z unchanged, respectively making regular hexagons by taking each network element position in the local optimal network element layout result as a center in the xOy plane, calculating coordinate information of the vertex position of each regular hexagon, and taking the calculated coordinate information and each network element position as a center together to serve as a new network element layout space. (ii) a The new number of the net element at the central point of the regular hexagon is decimal code and then is added with 00, and the number of the top point of each regular hexagon is formed by sequentially adding 01, 02, 03, 04, 05 and 06 in the clockwise direction after the decimal code of the net element at the central point of the regular hexagon.
Step (10): the method for obtaining the relatively optimal network element layout comprises the following steps of performing simulated annealing operation on a group by using a simulated annealing network element layout algorithm:
simulated annealing network element layout algorithm: and starting from a certain higher temperature, receiving a new network element layout space with a certain probability according to the positioning precision of the current network element layout and the positioning precision of the new network element layout space along with the reduction of the temperature parameter, and finally obtaining the relatively optimal network element layout by randomly selecting all possible conditions of the network element layout in the whole physical space.
Step (11): generating an optimal network element layout result by using a relatively optimal network element layout method;
step (12): and outputting the optimal network element layout result, and ending.
The invention has the beneficial effects that:
the invention takes the adaptive genetic algorithm as a main flow, and a simulated annealing mechanism is integrated into the main flow to adjust and optimize the population. The method has strong global search capability and local search capability, and improves the positioning accuracy. Because the network element layout group is changed before the simulated annealing algorithm is carried out, the searching efficiency is improved, and the obtained network element layout result is closer to the actual optimal network element layout result.
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FIG. 1 is a flow chart of a method for optimizing the layout of indoor positioning network elements based on genetic algorithm and simulated annealing;
FIG. 2 is a schematic diagram of a cell layout change in a detailed implementation scenario;
fig. 3 is a schematic diagram of a recoding situation of network element No. 3 in a specific implementation scenario.
Detailed Description
The invention is further described below with reference to the accompanying drawings:
an indoor positioning network element optimization layout method based on genetic algorithm and simulated annealing comprises the following steps:
step (1): and (3) carrying out network element layout: numbering the positions of the deployable network elements in sequence, deploying the network elements, and converting the number of each network element into a binary code:
drawing grids in a plan view of a specific indoor scene, considering that intersection points of the grids on the outer wall of the indoor scene are positions where network elements can be arranged, arranging the network elements at the positions where the network elements can be arranged, numbering the network elements in sequence, and converting the number of each network element into binary coding.
Step (2): determining control parameters required by the adaptive genetic algorithm, wherein the control parameters comprise population size, maximum iteration number, cross probability and variation probability:
the control parameters required by the adaptive genetic algorithm include: population size, maximum iteration number, cross probability and mutation probability. In a network element layout, n network elements are required, the binary code of each network element is an m-bit binary number, and each network element layout is regarded as a chromosome with a length len-n-m. If the population number is k, then the population size is k × len matrix.
And (3): initializing the network element layout: randomly selecting n different network elements from all the deployable network element groups as initial network element layouts;
and (4): calculating the fitness:
fitness is as follows:
Figure BDA0001584188830000041
in the above formula, N represents the number of test points of the positioning area, ERRORiIndicating the positioning error of the positioning point i.
And (5): iteratively judging whether a genetic convergence condition is met, if the genetic convergence condition is met, jumping to the step (9), and if not, jumping to the step (6);
and (6): selecting a network element layout with higher adaptability:
generating a random number r between 0 and the total fitness, and then according to the wheelRandomly sampling population individuals in a gambling mode, wherein the population individuals xiProbability of being selected F (x)i)：
Figure BDA0001584188830000042
In the above formula, f (x)i) Is a population of individuals xiThe fitness of (2); and selecting the network element layout with higher fitness.
And (7): the network element layout with higher fitness is arranged according to the cross probability PbPerforming a crossover operation on the binary code to obtain a child:
adopting single-point cross operation: firstly, a random real number 0 is more than or equal to r and less than or equal to 1, and a cross probability 0 is set<Pb<1, if r<PbThen interleaving needs to be done, otherwise it is not. If the crossing is needed, the crossing position is randomly selected, and the binary string codes after the crossing position are exchanged.
And (8): the network element layout with higher fitness is arranged according to the mutation probability PyCarrying out negation operation on the binary code to obtain variation:
generating a random real number 0 ≦ r ≦ 1, if r<PyThen, variation is performed, 0<Py<1 is the mutation probability; if the variation is needed, firstly, the variation position rand chromo size needs to be determined, if the variation position rand chromo size is 0, the variation is not performed, otherwise, the binary code of the variation position is inverted to generate the variation, so that 1 becomes 0, and 0 becomes 1.
And (9): generating a local optimal network element layout result, and generating a new network element layout space according to the coordinate information:
and after the local optimal network element layout result is obtained through the self-adaptive genetic algorithm, the binary code of the local optimal network element layout result is converted into the decimal code and the corresponding coordinate information. And on the premise of keeping the height value z unchanged, respectively making regular hexagons by taking each network element position in the local optimal network element layout result as a center in the xOy plane, calculating coordinate information of the vertex position of each regular hexagon, and taking the calculated coordinate information and each network element position as a center together to serve as a new network element layout space. (ii) a The new number of the net element at the central point of the regular hexagon is decimal code and then is added with 00, and the number of the top point of each regular hexagon is formed by sequentially adding 01, 02, 03, 04, 05 and 06 in the clockwise direction after the decimal code of the net element at the central point of the regular hexagon.
Step (10): the method for obtaining the relatively optimal network element layout comprises the following steps of performing simulated annealing operation on a group by using a simulated annealing network element layout algorithm:
simulated annealing network element layout algorithm: and starting from a certain higher temperature, receiving a new network element layout space with a certain probability according to the positioning precision of the current network element layout and the positioning precision of the new network element layout space along with the reduction of the temperature parameter, and finally obtaining the relatively optimal network element layout by randomly selecting all possible conditions of the network element layout in the whole physical space.
Step (11): generating an optimal network element layout result by using a relatively optimal network element layout method;
step (12): and outputting the optimal network element layout result, and ending.
Example 1 is given below:
referring to fig. 2a, fig. 2b, fig. 2c, i represents a wall of a building, ii represents a network element, and the dark-colored network element is a currently obtained network element layout result. The size of the scene is 8m 4m 3m, and as the engineering construction requirements can only lay the network elements on the wall and in the place within 1m from the wall, 12 grids with the density of 2m are arranged on the plane map, the laying positions of the network elements are arranged on the grids on the wall, the height is randomly distributed between 2.8m and 3m, and the positions of No. 1 network elements are used as the origin of coordinates to sequentially number the network elements into 1 to 12 according to the clockwise direction.
Step (1): binary coding is carried out on the network elements, and because the number of the network elements is 12 at most, four-digit binary numbers are adopted for coding, and the network elements with numbers of 1-12 are sequentially coded into 0001-1100;
step (2): determining the population scale to be 50, the maximum iteration number to be 20, the cross probability to be 0.6 and the variation probability to be 0.01;
and (3): selecting four network elements numbered 1, 5, 7, 11 as an initial layout empirically, as shown in fig. 2 a;
and (4): calculating the fitness according to the disclosed high-precision indoor positioning simulation system;
and (5): iteratively judging whether the fitness meets the condition of being less than 3m, if yes, jumping to the step (9), and not jumping to the step (6) to prepare genetic operation;
step (6) to step (8): performing genetic operation in the network element layout group to obtain a local optimal solution, and assuming that the local optimal solution obtained when a convergence condition is met is transcoded into number 3, 8, 10 and 12 network elements, as shown in fig. 2 b;
and (9): changing the network element layout group and recoding the network element layout group, as shown in fig. 3, taking network element No. 3 as an example, including 7 network element positions, so that the new network element layout group has 7 × 4 — 28 network elements in total;
step (10) to step (12): and performing simulated annealing operation on the population to obtain the network element with the network element layout results of 300, 801, 1004 and 1204, as shown in fig. 2 c.
The invention takes the adaptive genetic algorithm as a main flow, and a simulated annealing mechanism is integrated into the main flow to adjust and optimize the population. The method has strong global search capability and local search capability, and improves the positioning accuracy. Because the network element layout group is changed before the simulated annealing algorithm is carried out, the searching efficiency is improved, and the obtained network element layout result is closer to the actual optimal network element layout result.
The above description is not intended to limit the present invention, and various modifications and changes may be made by 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 (8)

1. An indoor positioning network element optimization layout method based on genetic algorithm and simulated annealing is characterized in that: comprises the following steps:
step (1): and (3) carrying out network element layout: numbering the positions of the deployable network elements in sequence, deploying the network elements, and converting the number of each network element into a binary code;
step (2): determining control parameters required by the adaptive genetic algorithm, including population scale, maximum iteration times, cross probability and variation probability;
and (3): initializing the network element layout: randomly selecting n different network elements from all the deployable network element groups as initial network element layouts;
and (4): calculating the fitness;
and (5): iteratively judging whether a genetic convergence condition is met, if the genetic convergence condition is met, jumping to the step (9), and if not, jumping to the step (6);
and (6): selecting a network element layout with higher fitness;
and (7): the network element layout with higher fitness is arranged according to the cross probability PbPerforming cross operation on the binary codes to obtain filial generations;
and (8): the network element layout with higher fitness is arranged according to the mutation probability PyCarrying out negation operation on the binary code to obtain variation;
and (9): generating a local optimal network element layout result, and generating a new network element layout space according to the coordinate information; the method specifically comprises the following steps:
after obtaining a local optimal network element layout result through a self-adaptive genetic algorithm, converting a binary code of the local optimal network element layout result into a decimal code and corresponding coordinate information; on the premise of keeping the height value z unchanged, respectively making regular hexagons by taking each network element position in the local optimal network element layout result as a center in an xOy plane, calculating coordinate information of the vertex position of each regular hexagon, and taking the calculated coordinate information and each network element position as a center together to serve as a new network element layout space; the new number of the net element at the central point of the regular hexagon is decimal code and then '00', and the number of the vertex of each regular hexagon is formed by sequentially adding '01', '02', '03', '04', '05' and '06' in the clockwise direction after the decimal code of the net element at the central point of the regular hexagon;
step (10): a simulated annealing operation is carried out on the group by using a simulated annealing network element layout algorithm to obtain a relatively optimal network element layout method;
step (11): generating an optimal network element layout result by using a relatively optimal network element layout method;
step (12): and outputting the optimal network element layout result, and ending.
2. The indoor positioning network element optimizing layout method based on genetic algorithm and simulated annealing as claimed in claim 1, wherein: the step (1) is specifically as follows:
drawing grids in a plan view of a specific indoor scene, considering that intersection points of the grids on the outer wall of the indoor scene are positions where network elements can be arranged, arranging the network elements at the positions where the network elements can be arranged, numbering the network elements in sequence, and converting the number of each network element into binary coding.
3. The indoor positioning network element optimizing layout method based on genetic algorithm and simulated annealing as claimed in claim 1, wherein: the step (2) is specifically as follows:
the control parameters required by the adaptive genetic algorithm include: population scale, maximum iteration times, cross probability and variation probability; in the network element layout, n network elements are needed, the binary code of each network element is an m-bit binary number, and each network element layout is regarded as a chromosome with a length len equal to n x m; if the population number is k, then the population size is k × len matrix.
4. The indoor positioning network element optimizing layout method based on genetic algorithm and simulated annealing as claimed in claim 1, wherein: the fitness in the step (4) is as follows:
Figure FDA0002485305300000021
in the above formula, N represents the number of test points of the positioning area, ERRORiIndicating the positioning error of the positioning point i.
5.The indoor positioning network element optimizing layout method based on genetic algorithm and simulated annealing as claimed in claim 1, wherein: the step (6) is specifically as follows:
generating a random number r between 0 and the total fitness, and randomly sampling population individuals x in a roulette modeiProbability of being selected F (x)i)：
Figure FDA0002485305300000022
In the above formula, f (x)i) Is a population of individuals xiThe fitness of (2); and selecting the network element layout with higher fitness.
6. The indoor positioning network element optimizing layout method based on genetic algorithm and simulated annealing as claimed in claim 1, wherein: the step (7) is specifically as follows:
adopting single-point cross operation: firstly, a random real number 0 is more than or equal to r and less than or equal to 1, and a cross probability 0 is set<Pb<1, if r<PbIf not, the crossing is not carried out; if the crossing is needed, the crossing position is randomly selected, and the binary string codes after the crossing position are exchanged.
7. The indoor positioning network element optimizing layout method based on genetic algorithm and simulated annealing as claimed in claim 1, wherein: the step (8) is specifically as follows:
generating a random real number 0 ≦ r ≦ 1, if r<PyThen, variation is performed, 0<Py<1 is the mutation probability; if the variation is needed, firstly, the variation position rand chromo size needs to be determined, if the variation position rand chromo size is 0, the variation is not performed, otherwise, the binary code of the variation position is inverted to generate the variation, so that 1 becomes 0, and 0 becomes 1.
8. The indoor positioning network element optimizing layout method based on genetic algorithm and simulated annealing as claimed in claim 1, wherein: the step (10) is specifically as follows:
simulated annealing network element layout algorithm: and starting from a certain higher temperature, receiving a new network element layout space with a certain probability according to the positioning precision of the current network element layout and the positioning precision of the new network element layout space along with the reduction of the temperature parameter, and finally obtaining the relatively optimal network element layout by randomly selecting all possible conditions of the network element layout in the whole physical space.
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