CN112738709B - Particle swarm search indoor positioning method and system based on UWB signals - Google Patents
Particle swarm search indoor positioning method and system based on UWB signals Download PDFInfo
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Abstract
The invention discloses a particle swarm searching indoor positioning method based on UWB signals, which comprises the following steps: s11, initializing a particle swarm; wherein the population size is N, the initial speed is 0, and the iteration frequency is L; s12, calculating the distance measurement values of all base stations and the positioning terminal, calculating the adaptive value of each particle according to the distance measurement values, and updating the individual optimal value and the global optimal value according to the adaptive value of each particle obtained through calculation; s13, judging whether the iteration stop condition is met, if not, executing the step S14; if yes, go to step S16; s14, updating the position and the speed of each particle; s15, screening particles outside the distance measurement value, eliminating the screened particles, splitting and copying the particles which are not eliminated to obtain optimized particle swarm, and re-executing the step S12; and S16, outputting the position information when the global optimal value is output, and ending the process. The particle search mode based on the minimum mean square error criterion has higher robustness in the TOA UWB positioning system.
Description
Technical Field
The invention relates to the technical field of wireless communication networks, in particular to a particle swarm search indoor positioning method and system based on UWB signals.
Background
With the rapid development of intelligent technology, people have higher and higher requirements for positioning. The traditional positioning technologies such as GPS and the like can not meet the requirement of positioning accuracy, so that an indoor positioning technology based on Wi-Fi, Bluetooth, ZigBee, radio frequency identification and the like appears, and the ultra-wideband positioning technology becomes a wireless communication technology which is most widely applied to positioning at present due to the advantages of high multipath resolution, strong penetrating power, low power consumption, easiness in integration, high positioning performance and the like.
The ultra-wideband (UWB) technology is a wireless technology based on narrow pulses, and because UWB can detect reflected pulses respectively, it can perform a positioning task by positioning only using a base station and a positioning terminal in a known environment, and has the advantages of high transmission rate, low power consumption, strong penetration capability, and the like. However, for a real implementation environment of the indoor positioning system, the real coordinate position cannot be determined due to the obstruction and the introduction of wireless noise and some observation errors.
The particle swarm optimization algorithm simulates the foraging movement of a bird swarm, and an optimal solution is found through cooperation and information sharing among individuals in the swarm. It is widely used for function optimization, neural network training, etc. because it is simple and easy to implement and has no adjustment of many parameters.
For example, the publication number CN110045324A discloses an indoor positioning fusion method based on UWB and bluetooth technologies, which combines the advantages of two indoor positioning schemes, and utilizes the characteristic of small UWB ranging error to define a possible area of an estimated position and reduce a solution range; compared with the traditional method for estimating the position according to the distance measurement, the method has the advantages that the RSSI value of the Bluetooth signal is directly utilized, the probability distribution in the solving area is obtained through Bayesian inference to solve the position, the error introduction of the RSSI signal during the calculation of the distance is avoided, and the positioning precision is improved; the positioning accuracy of the UWB technology can be obtained under most of conditions by utilizing one UWB base station and 2 Bluetooth base stations, and compared with a UWB positioning scheme using 4 UWB base stations, the cost can be reduced to one fourth of the original cost; the extreme value problem under the inequality constraint is solved by utilizing a particle swarm optimization algorithm, a local optimal solution can be obtained under the condition that the minimum value deviates from a real solution due to a large RSSI measurement value error, and a better actual use effect is achieved. Although the above patent can obtain a local optimal solution, it is based on ranging of a uwb base station and signal strengths of a plurality of bluetooth, and the fitness is calculated by bayesian inference in cooperation with a bluetooth signal strength model, which still cannot solve the problem that the optimal positioning terminal coordinate cannot be estimated due to the introduction of blocking objects, wireless noise and some observation errors.
Disclosure of Invention
The invention aims to provide a particle swarm searching indoor positioning method and system based on UWB signals aiming at the defects of the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme:
a particle swarm search indoor positioning method based on UWB signals comprises the following steps:
s1, initializing a particle swarm; wherein the population size is N, the initial speed is 0, and the iteration frequency is L;
s2, calculating the distance measurement values of all base stations and the positioning terminal, calculating the adaptive value of each particle according to the distance measurement values, and updating the individual optimal value and the global optimal value according to the adaptive value of each particle obtained through calculation;
s3, judging whether the iteration stop condition is met, if not, executing a step S4; if yes, go to step S6;
s4, updating the position and the speed of each particle;
s5, screening particles outside the distance measurement value, eliminating the screened particles, splitting and copying the particles which are not eliminated to obtain optimized particle swarm, and re-executing the step S2;
and S6, outputting the position information when the global optimal value is output, and ending the process.
Further, in step S2, the adaptive value of each particle is calculated according to the distance measurement value, and is expressed as:
wherein Q represents an adaptation value; x and y represent the position coordinates of the positioning terminal; x is the number of i ，y i Representing the location coordinates of the base station; d i Indicating a ranging value between the base station and the positioning terminal; i is 1,2,3, …, N.
Further, the optimal criterion of the adaptive value of each particle calculated in step S2 should satisfy the following condition:
where min (x, y) represents the minimum value of the position coordinates of the positioning terminal, i.e., the optimum value of the search.
Further, the updating of the position and the velocity of each particle in step S4 is updated based on the individual optimal value and the global optimal value model, and is represented as:
v i ＝v i +c i ×rand()×(pbest i -x i )+c 2 ×rand()×(gbest i -x i )
x i ＝x i +v i
wherein v is i Represents the velocity of the particles; rand () represents a random vector between (0,1) containing two elements; x is the number of i Representing the current position of the particle; c. C 1 And c 2 Represents a learning factor; pbest i Representing individual optima; gbest i Representing a global optimum.
Further, the step S4 includes introducing an adaptive weighting factor ω, which is expressed as:
v i ＝ω×v i +c i ×rand()×(pbest i -x i )+c 2 ×rand()×(gbest i -x i )
where ω has a dynamic range of [0,1 ].
Further, the step S4 further includes introducing a factor SP to dynamically adjust the adaptive weight factor ω, which is expressed as:
ω＝(ω max -ω min )×SP
and the value of the SP is the ratio of the number of the external particles at any one ranging value to the number N of all the particles after each iteration.
Further, in step S3, it is determined whether the condition in the iteration stop condition is met, that is, the fitness value of the global optimum value is less than 1 or the iteration number reaches L times.
Correspondingly, still provide a particle swarm indoor positioning system based on UWB signal, include:
the initialization module is used for initializing particle swarms; wherein the population size is N, the initial speed is 0, and the iteration frequency is L;
the calculation module is used for calculating the ranging values of all the base stations and the positioning terminal, calculating the adaptive value of each particle according to the ranging values, and updating the individual optimal value and the global optimal value according to the adaptive value of each particle obtained through calculation;
the judging module is used for judging whether the iteration stopping condition is met or not;
the updating module is used for updating the position and the speed of each particle;
the screening module is used for screening particles outside the distance measurement value, eliminating the screened particles, and splitting and copying the particles which are not eliminated to obtain optimized particle swarm;
and the output module is used for outputting the position information when the global optimal value is output.
Further, said updating the position and velocity of each particle further comprises introducing an adaptive weight factor ω, expressed as:
v i ＝ω×v i +c i ×rand()×(pbest i -x i )+c 2 ×rand()×(gbest i -x i )
wherein the dynamic range of ω is [0, 1]]；v i Represents the velocity of the particles; rand () represents a random vector between (0,1) containing two elements; x is the number of i Representing the current position of the particle; c. C 1 And c 2 Represents a learning factor; pbest i Representing individual optima; gbest i Representing a global optimum.
Further, the updating of the position and the velocity of each particle further includes introducing a factor SP to dynamically adjust an adaptive weight factor ω, which is expressed as:
ω＝(ω max -ω min )×SP
and the value of the SP is the ratio of the number of the external particles at any one ranging value to the number N of all the particles after each iteration.
Compared with the prior art, the method utilizes the principle of a positioning mechanism of the TOA in plane geometry, converts the positioning mechanism into a least square solution mode for solving an overdetermined nonlinear equation set, and introduces a particle swarm search algorithm compared with the problem that least square positioning is not stable enough. And in order to improve the positioning precision, on the premise that the uwb positioning error has a positive error, the searching accuracy and efficiency are improved by adopting a mode that particles outside the TOA ranging circle are difficult to survive. Meanwhile, in the iterative process, the screening extinction and splitting evolution ideas of the particles are introduced to improve the searching efficiency and accuracy, and a relatively ideal improvement effect is obtained in an actual uwb test system.
Drawings
Fig. 1 is a flowchart of an indoor positioning method for a particle swarm search based on UWB signals according to an embodiment;
FIG. 2 is a diagram of an embodiment of an UWB indoor positioning model;
fig. 3 is a block diagram of a UWB signal-based particle swarm search indoor positioning system according to the second embodiment.
Detailed Description
The following embodiments of the present invention are provided by way of specific examples, and other advantages and effects of the present invention will be readily apparent to those skilled in the art from the disclosure herein. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
The invention aims to provide a particle swarm indoor positioning method and system based on UWB signals, aiming at the defects of the prior art.
Example one
The embodiment provides a particle swarm search indoor positioning method based on a UWB signal, as shown in fig. 1, comprising the steps of:
s11, initializing a particle swarm; wherein the population size is N, the initial speed is 0, and the iteration frequency is L;
s12, calculating the distance measurement values of all base stations and the positioning terminal, calculating the adaptive value of each particle according to the distance measurement values, and updating the individual optimal value and the global optimal value according to the adaptive value of each particle obtained through calculation;
s13, judging whether the iteration stop condition is met, if not, executing the step S14; if yes, go to step S16;
s14, updating the position and the speed of each particle;
s15, screening particles outside the distance measurement value, eliminating the screened particles, splitting and copying the particles which are not eliminated to obtain optimized particle swarm, and re-executing the step S12;
and S16, outputting the position information when the global optimal value is output, and ending the process.
As shown in fig. 2, which is a diagram of an UWB indoor positioning model, the centers of circles a1, a2, A3 and a4 represent four positioning base stations with known plane coordinates, the five-pointed star position represents the unknown tag coordinates to be positioned, and the length of the dotted line represents the real plane distance obtained by each base station in the UWB positioning system for positioning the tag.
Assume the coordinates of the base station as (x) i ，y i ) I is 1,2 …, N, and the euclidean distance value of the base station from the tag of unknown coordinates (x, y) is d i According to a plane Euclidean distance formula, a nonlinear equation set consisting of N plane distance equations related to a base station and unknown coordinates can be obtained, the formula is shown in the following 1.1, and for a real implementation environment of an indoor positioning system, due to the introduction of shielding objects, wireless noise and some observation errors, positioning circles cannot intersect at a real coordinate position. And usually N (N ≧ 3) base stations are required to participate together to estimate the optimal positioning terminal coordinates.
For solving such an overdetermined (the number of unknown variables is less than the number of equations) nonlinear system of equations, a unique analytical solution cannot be obtained, but an optimal solution is solved. The equation to the left of equation 1.1 can be seen as the square difference of the plane distance of the estimated coordinates of the set base station and the positioning terminal and the actually observed UWB ranging value.
In step S11, a particle group is initialized; wherein the population size is N, the initial speed is 0, and the iteration number is L.
For a planar positioning system, to abstract the position of the positioning tag once, the particle has information in two dimensions (x, y), each dimension contains two attributes: speed, which represents how fast the movement is, and position, which represents the direction of the movement. In a positioning system the particles can be abstracted as (x, y) and the flight velocities expressed as vectors (v) x ，v y )。
The present example is illustrated with a particle number of 20:
the initialization method comprises selecting a minimum distance measurement circle from all distance measurement circles, and randomly generating 20 particle coordinates x i (x i ，y i ) The initial velocity vector is v i (0, 0), the initial iteration number is recorded, and the iteration number L is 6.
In step S12, the ranging values of all the base stations and the positioning terminal are calculated, the adaptive value of each particle is calculated according to the ranging values, and the individual optimal value and the global optimal value are updated according to the calculated adaptive value of each particle.
The (x, y) value used when minimizing Q is obtained to identify the position where the positioning terminal is actually located. In order to solve the extreme value of Q, a particle swarm algorithm is introduced to search the coordinates in the whole possible plane space so that the value of Q is the minimum, i.e. the optimal solution satisfying equation 1.2.
Wherein Q represents an adaptation value; x and y represent the position coordinates of the positioning terminal; x is a radical of a fluorine atom i ，y i Representing the location coordinates of the base station; d i Indicating a ranging value between the base station and the positioning terminal; i is 1,2,3, …, N.
The optimal criterion of the calculated adaptive value of each particle is specifically as follows: each particle has an information-sharing fitness value (fitness value) determined by an objective function, and for a positioning system, the fitness function to be solved is to determine a plane coordinate (x, y) such that, in the presence of errors, the minimum value is determined by satisfying equation 1.3.
Wherein min (x, y) represents the minimum value of the position coordinates of the positioning terminal, namely the optimal value of the search.
The particle swarm approach is primarily initialized to a population of random particles (the random solution). The criterion of the planar positioning system, which is then used to determine the optimal solution by iteratively adapting the criterion of the problem to be solved, is to find (x, y) that satisfies the Q value as much as possible. In each iteration, the particle updates itself by tracking two "extrema". The first is the optimal solution found by the particle itself, which is called the individual extremum pBest. The other extreme is the best solution currently found for the entire population, this extreme is the global extreme gBest. It is of course also possible that not the entire population but only a part thereof is used as a neighborhood of the particle, and that the extremum in all neighborhoods is a local extremum.
In step S13, it is determined whether the iteration stop condition is satisfied, and if not, step S14 is executed; if yes, go to step S16.
During the search, the particle may calculate the fitness value to know the best position (pbest, lowest fitness value) found so far and the corresponding position (x ', y '), which may be regarded as the particle's own search experience. In addition to this, each particle also knows the best position found so far for all particles in the whole population (gbest, which is the best value in pbest), which can be seen as an experience in the population of particles.
Judging whether the condition in the iteration stopping condition is met, wherein the fitness value of the global optimal value is smaller than 1 or the iteration times reach L times; when not, execute step S14; when the coincidence is found, step S16 is executed.
In step S14, the position and velocity of each particle are updated.
The position and velocity information of the particles is updated. In each iteration, the particle updates its velocity and position by tracking two "extrema" (pbest).
v i ＝v i +c i ×rand()×(pbest i -x i )+c 2 ×rand()×(gbest i -x i ) (1.4)
x i ＝x i +v i (1.5)
Wherein i ═ 1,2, …, N, represents the total number of particles in the population; v. of i Represents the velocity of the particles; rand () represents a random vector between (0,1) containing two elements; x is the number of i Representing the current position of the particle; c. C 1 And c 2 Representing a learning factor.
At present, get c 1 ＝c 2 ＝2，v i Maximum value of (A) is V max (greater than 0), V set according to the current system max Is a minimum distance measurement circle d min /(2×T range )，T range Representing a ranging period. If v is a certain time i Greater than V max Then v is i ＝V max In order to avoid falling into the local optimum problem, or in the process of obtaining the global optimum, the searching process is longer.
In this embodiment, an adaptive weight factor ω is introduced in updating the position and velocity of each particle, which is expressed as:
v i ＝ω×v i +c i ×rand()×(pbest i -x i )+c 2 ×rand()×(gbest i -x i ) (1.6)
wherein, the dynamic range of omega is [0, 1], and the dynamic self-adaptive omega can obtain better optimization result than a fixed value. According to the particularity of the TOA positioning system, since the UWB wireless positioning system will cause larger ranging value due to interference caused by reflection or multipath propagation, this characteristic can be used as a constraint condition in the positioning process to improve the efficiency and accuracy of the search.
In this embodiment, another factor SP is introduced to dynamically adjust ω, which is expressed as:
ω＝(ω max -ω min )×SP (1.7)
wherein the value of SP equals to after each iteration, whether the particle exists outside any TOA range value; if M particles exist outside the TOA ranging circle among the total N particles, SP is M/N.
In step S15, particles outside the range measurement value are screened, the screened particles are rejected, the particles that are not rejected are split and copied to obtain an optimized particle group, and step S12 is executed again.
It should be noted that the particles outside the ranging value indicate that the positions of the particles from the base station are larger than the actual measured distance between the tag and the base station.
In the searching process, the particles always approach to the optimal solution, if the optimal solution is a local optimal solution, the particles are difficult to approach to the global optimal solution as the particles are gradually gathered, and in order to avoid the phenomenon, the embodiment introduces the characteristics of particle extinction and fragmentation.
If a part of M particles are positioned outside the TOA ranging value in the previous search process or the fitness is low, the part of particles are eliminated, the surviving particles are split and copied, and the search is carried out in the next iteration. The mode with the population elimination evolution mechanism improves the diversity of the population and optimizes the searching efficiency.
The screening mechanism is as follows, if M is larger than N/3, M particles are eliminated, if M is smaller than 3/N, M particles are eliminated firstly, and then 3/N-M particles at the tail part are selected to eliminate in the rest particles according to the sequence from small to large of the fitness.
The splitting and copying mode is that the original N particles are uniformly sampled and complemented among the surviving particles to obtain an optimized particle swarm, the iteration times are +1, the step S12 is executed again, and the next iteration is carried out.
In step S16, the position information at the time of outputting the global optimum value ends the flow.
The particles determine the next step of movement through their own experience and the best experience among the partners. Namely, the selection of the search path, through continuous search iteration, the iteration is terminated when a certain convergence condition is reached, and a gbest value in the search process is given.
And when the iteration stopping condition is met, namely when the fitness value of the global optimal value is less than 1 or the iteration times reach L times, jumping out of iteration, outputting the position information of the optimal gBest value in the iteration process, namely the optimal positioning coordinate to be solved in the UWB system, and ending the process.
Tests show that the particle search mode according to the minimum mean square error criterion has higher robustness in a TOA UWB positioning system, the positioning deviation can be controlled to be maintained at about 80cm in an NLOS (non line of sight) system, and the error in the LOS system is below 30 cm. In addition, the effective particle constraint mechanism in the TOA ranging error circle added in the embodiment can solve the problem that the particle swarm is easy to fall into local optimum, the extinction screening and the population evolution splitting of the particles also improve the search efficiency, and the optimal solution meeting the least square criterion can be obtained by convergence from the original approximately average 8 iterations to the convergence of 3-4 iterations.
Example two
The present embodiment provides a UWB signal-based particle swarm search indoor positioning system, as shown in fig. 3, including:
an initialization module 11, configured to initialize a particle swarm; wherein the population size is N, the initial speed is 0, and the iteration frequency is L;
a calculating module 12, configured to calculate ranging values of all base stations and the positioning terminal, calculate an adaptive value of each particle according to the ranging values, and update an individual optimal value and a global optimal value according to the calculated adaptive value of each particle;
a judging module 13, configured to judge whether an iteration stop condition is met;
an update module 14 for updating the position and velocity of each particle;
the screening module 15 is used for screening particles outside the distance measurement value, eliminating the screened particles, and splitting and copying the particles which are not eliminated to obtain optimized particle swarm;
and an output module 16, configured to output the location information when the global optimal value is obtained.
Further, said updating the position and velocity of each particle further comprises introducing an adaptive weight factor ω, expressed as:
v i ＝ω×v i +c i ×rand()×(pbest i -x i )+c 2 ×rand()×(gbest i -x i )
wherein the dynamic range of ω is [0, 1]]；v i Represents the velocity of the particles; rand () represents a random vector between (0,1) containing two elements; x is the number of i Representing the current position of the particle; c. C 1 And c 2 Represents a learning factor; pbest i Representing individual optima; gbest i Representing a global optimum.
Further, the updating of the position and the velocity of each particle further includes introducing a factor SP to dynamically adjust an adaptive weight factor ω, which is expressed as:
ω＝(ω max -ω min )×SP
and the value of the SP is the ratio of the number of the external particles at any one ranging value to the number N of all the particles after each iteration.
It should be noted that, the particle swarm searching indoor positioning system based on the UWB signal provided in this embodiment is similar to the embodiment, and is not repeated herein.
Compared with the prior art, the method and the device have the advantages that the principle of a positioning mechanism of the TOA in plane geometry is utilized, the least square solution mode of an overdetermined nonlinear equation set is converted, and compared with the problem that least square positioning is not stable enough, a particle swarm search algorithm is introduced. And in order to improve the positioning precision, on the premise that the uwb positioning error has a positive error, the searching accuracy and efficiency are improved by adopting a mode that particles outside the TOA ranging circle are difficult to survive. Meanwhile, in the iterative process, the screening extinction and splitting evolution ideas of the particles are introduced to improve the searching efficiency and accuracy, and a relatively ideal improvement effect is obtained in an actual uwb test system.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (5)
1. A particle swarm indoor positioning method based on UWB signals is characterized by comprising the following steps:
s1, initializing a particle swarm; wherein the population size is N, the initial speed is 0, and the iteration frequency is L;
s2, calculating the distance measurement values of all base stations and the positioning terminal, calculating the adaptive value of each particle according to the distance measurement values, and updating the individual optimal value and the global optimal value according to the adaptive value of each particle obtained through calculation;
s3, judging whether the iteration stop condition is met, if not, executing the step S4; if yes, go to step S6;
s4, updating the position and the speed of each particle;
s5, screening particles outside the distance measurement value, eliminating the screened particles, splitting and copying the particles which are not eliminated to obtain optimized particle swarm, and re-executing the step S2;
s6, outputting position information when the global optimal value is output, and ending the process;
the updating of the position and velocity of each particle in step S4 is based on the individual optimal value and the global optimal value, and is represented as:
v i ＝v i +c 1 ×rand()×(pbest i -x i )+c 2 ×rand()×(gbest i -x i )
x i ＝x i +v i
wherein v is i Represents the velocity of the particles; rand () represents a random vector between (0,1) containing two elements; x is the number of i Representing the current position of the particle; c. C 1 And c 2 Represents a learning factor; pbest i Representing individual optima; gbest i Representing a global optimum;
the step S4 further includes introducing an adaptive weighting factor ω, which is expressed as:
v i ＝ω×v i +c 1 ×rand()×(pbest i -x i )+c 2 ×rand()(gbest i -x i )
wherein the dynamic range of ω is [0,1 ];
the step S4 further includes introducing a factor SP to dynamically adjust the adaptive weight factor ω, which is expressed as:
ω＝(ω max -ω min )×SP
and the value of the SP is the ratio of the number of the external particles at any one ranging value to the number N of all the particles after each iteration.
2. The method as claimed in claim 1, wherein the step S2 is implemented by calculating an adaptive value of each particle according to the ranging value, and the adaptive value is expressed as:
wherein Q represents an adaptation value; x and y represent the position coordinates of the positioning terminal; x is a radical of a fluorine atom i ，y i Representing the location coordinates of the base station; d i Indicating a ranging value between the base station and the positioning terminal; i is 1,2,3, …, N.
3. The UWB signal based particle swarm indoor positioning method of claim 2, wherein the optimal criterion of the adaptive value of each particle calculated in step S2 is satisfied with the following conditions:
wherein min, the (x, y) represents the minimum value of the position coordinates of the positioning terminal, i.e. the optimum value of the search.
4. The UWB signal based particle swarm search indoor positioning method of claim 1, wherein the condition of whether the iteration stop condition is met in the step S3 is that the fitness value of the global optimum is less than 1 or the iteration number reaches L times.
5. A particle swarm indoor positioning system based on UWB signals is characterized by comprising:
the initialization module is used for initializing particle swarms; wherein the population size is N, the initial speed is 0, and the iteration frequency is L;
the calculation module is used for calculating the ranging values of all the base stations and the positioning terminal, calculating the adaptive value of each particle according to the ranging values, and updating the individual optimal value and the global optimal value according to the adaptive value of each particle obtained through calculation;
the judging module is used for judging whether the iteration stopping condition is met or not;
the updating module is used for updating the position and the speed of each particle;
the screening module is used for screening particles outside the distance measurement value, eliminating the screened particles, and splitting and copying the particles which are not eliminated to obtain optimized particle swarm;
the output module is used for outputting the position information when the global optimal value is output;
said updating the position and velocity profile of each particle further comprises introducing an adaptive weight factor ω, expressed as:
v i ＝ω×v i +c 1 ×rand()×(pbest i -x i )+c 2 ×rand()×(gbest i -x i )
wherein the dynamic range of ω is [0, 1]]；v i Represents the velocity of the particles; rand () represents a random vector between (0,1) containing two elements; x is a radical of a fluorine atom i Representing the current position of the particle; c. C 1 And c 2 Represents a learning factor; pbest i Representing individual optima; gbest i Representing a global optimum;
further comprising introducing a factor SP to dynamically adjust the adaptive weight factor ω, expressed as:
ω＝(ω max -ω min )×SP
and the value of the SP is the ratio of the number of the external particles at any one ranging value to the number N of all the particles after each iteration.
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