CN107609649A - A kind of lighting location calculation optimization method based on Genetic Particle Swarm mixing - Google Patents
A kind of lighting location calculation optimization method based on Genetic Particle Swarm mixing Download PDFInfo
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Abstract
The present invention relates to the technical field of lightning monitoring system, more particularly, to a kind of lighting location calculation optimization method based on Genetic Particle Swarm mixing.Used with reference to particle cluster algorithm the advantages of simple, fast convergence rate and the advantages of genetic algorithm ability of searching optimum is strong, utilize the optimization that previous stage is carried out the characteristics of particle cluster algorithm fast convergence rate, obtain the initial population of certain evolution degree, then the optimization of the latter half is carried out by genetic algorithm, improve the local convergence speed and ability of searching optimum for calculating solution in solution space, and the parameters such as population velocity factor, the hereditary variation factor can be optimized by result of calculation, realize efficient, the high accuracy positioning of thunder and lightning；And can avoid by the total lightning parameter deviation that calculates of different original data sets it is larger, the problem of homologous uniformity can not be ensured.
Description
Technical field
The present invention relates to the technical field of lightning monitoring system, is mixed more particularly, to one kind based on Genetic Particle Swarm
Lighting location calculation optimization method.
Background technology
Thunder and lightning is a kind of instantaneous discharge phenomenon frequently occurred in nature, the adjoint highfield of its discharge process, Qiang Ci
The effects such as field, high current, strong light, strong shock wave and strong electromagnetic radiation, the electric power facility being distributed to wide area cause serious
Infringement, the safe and stable operation of power network is threaten for a long time.With developing rapidly for China's modern times interconnected power grid, power system
Complexity and nonlinear degree are growing day by day, even if causing small external disturbance to bring unpredictable consequence, and
Transmission line of electricity flashover is one of an important factor for system disturbs caused by thunderbolt.Therefore strengthen electric network thunder and lightning monitoring and prevent
Shield, so as to reduce lightening hazard, it is to ensure power system security power supply, builds the prerequisite of sturdy power grid.
At present, lightning monitoring system inherits the comprehensive positioning thought of " time difference+direction " in terms of lighting location calculating, and
It is proposed to realize positioning and the self-optimization techniques of parameter computation model.However, there are problems in this self-optimization techniques：It is being
When system monitoring power network distribution reaches certain scale, the acquisition station for detecting lightning wave signal increases, and thunder and lightning propagation path is more
Complexity, cause a variety of detecting errors to be introduced into location Calculation, influence system location Calculation precision；Using iterative calculation side
Method, calculated in the lightening activity outbreak period and produce delay, while be also unable to reach the speed service requirement of historical data analysis arrangement；
The parameters such as lightning current are calculated using fixed thunder and lightning Propagation models of electromagnetic wave propagation, can cause to be calculated by different original data sets are total
The lightning parameter deviation come is larger, can not ensure homologous uniformity.
The content of the invention
It is an object of the invention to overcome the deficiencies of the prior art and provide a kind of thunder and lightning based on Genetic Particle Swarm mixing to determine
Position calculation optimization method, certain evolution journey is obtained using the optimization of previous stage is carried out the characteristics of particle cluster algorithm fast convergence rate
The initial population of degree, the optimization of the latter half is carried out the characteristics of strong using genetic algorithm ability of searching optimum, improve calculating solution and exist
Local convergence speed and ability of searching optimum in solution space, and population velocity factor, heredity can be become by result of calculation
The parameters such as the different factor optimize, and realize efficient, the high accuracy positioning of thunder and lightning.
In order to solve the above technical problems, the technical solution adopted by the present invention is：
A kind of lighting location calculation optimization method based on Genetic Particle Swarm mixing is provided, the calculation optimization method is grain
The hybrid algorithm of swarm optimization and genetic algorithm, comprises the following steps：
S1. result (the x just calculated according to the earth ellipsoid calculation formula0,y0), solution space scope delimited as x ∈ using A radiuses
[x0-A,x0+ A], y ∈ [y0-A,y0+ A], and random generation solution particle in solution space delimited, the wherein scope of A radiuses is 4km
~6km；
S2. after step S1, population is made to search for the optimal of particle in x-y two-dimensional spaces using the first fitness function
Fitness function value and optimum position, or population is made using the second fitness function while searched for using time and azimuth information
The optimal fitness function value of particle and optimum position；
S3. after step S2, foundation step S2 optimization information updating flying speed of partcles and particle position parameter are simultaneously excellent
Change the particle inertia factor so that population has preferable ability of searching optimum in initial search phase, in the search phase in later stage
With preferable local search ability；
S4. after step S3, the mixed strategy design alternative operator of adoption rate selection and optimal save strategy, adoption rate choosing
Select strategy and ensure the selected maximum probability of the individual with minimum fitness, using optimum maintaining strategy after new population generation
The minimum fitness individual in new and old population is compared, and substitutes the maximum adaptation degree individual in new population with the individual；
S5. after step S4, single-point cross method design crossover operator is used to determine that the position in crosspoint and gene are handed over
The method changed；And the fitness of particle is calculated by the first fitness function in step S2 or the second fitness function；Work as adaptation
Degree reaches desired value or when iterations reaches maximum, and lightning strike accident point is calculated；Otherwise, S6 is gone to step；
S6. after step S5, design mutation operator is with the gene replacement side at the position of definitive variation point and variable position
Method；And the fitness of particle is calculated by the first fitness function in step S2 or the second fitness function；When fitness reaches
When desired value or iterations reach maximum, lightning strike accident point is calculated；Otherwise, S4 is gone to step.
Preferably, the first fitness function described in step S2It is calculated as follows：
Wherein, di(Xm) for particle current location to the distance of i-th of acquisition station, t0The thunder and lightning derived for each station occurs
The average value of time, tiThe thunder and lightning time of origin derived for i-th of acquisition station, n are the number of particle, and c is inertial factor.
The fitness function of particleWhen minimum, Q (X) also reaches minimum, particle position be thunder and lightning position most
Maximum-likelihood estimates that particle cluster algorithm can reach precision when least-squares iteration calculates convergence；Symbolization χ2Represent to adapt to letter
Number, it is because the minimum value of fitness function obeys chi square distribution χ2(n-3), wherein (n-3) is the quantity of redundant measurements；Work as nothing
During redundant measurements, fitness function minimum value is zero.
Preferably, the second fitness function described in step S2It is calculated as follows：
Wherein, di(Xm) for particle current location to the distance of i-th of acquisition station, t0The thunder and lightning derived for each station occurs
The average value of time, tiThe thunder and lightning time of origin derived for i-th acquisition station, n are the number of particle, and c is inertial factor, (xm,
ym)、(xi,yi) for the location of m-th of particle, i-th particle.
Preferably, flying speed of partcles described in step S3, the particle position parameter, the particle inertia factor are by such as
Lower formula optimizes：
Note total number of particles is M, XmFor particle current location, m=1,2 ..., M, PmFor particle history optimal location, P is rememberedgFor
The current global optimum position of population, then the renewal equation of each iteration of particle be：
Vm(k+1)=ω × Vm(k)+c1×Rm1×(pm(k)-Xm(k))+c2×Rm2×(pg(k)-Xm(k))
Xm(k+1)=Xm(k)+Vm(k+1)
Wherein, VmFor flying speed of partcles, k is current renewal step number；ω is the inertial factor between 0.1~0.9；
c1And c2For Studying factors, the acceleration that particle moves towards itself optimal location of the particle and global optimum position is represented respectively,
Typically take c1=c2=2；Rm1And Rm2The diagonal matrix of respectively D × D dimensions, each diagonal element is between [0,1]
Random number；
Inertial factor is calculated as follows：
Wherein, kmaxFor total iterations, ω1=0.9 and ω2=0.4 be respectively the initial inertia factor and final inertia because
Son.
ω is the inertial factor between 0.1~0.9, can reduce flying speed of partcles, prevents search from dissipating；c1With
c2Represent particle towards itself optimal location of the particle and the acceleration of global optimum's position motion respectively；ω1=0.9 and ω2=
0.4 makes population have preferable ability of searching optimum in initial search phase, and has preferable Local Search energy in the later stage
Power；Population size is generally 20, maximum iteration 1000.
Preferably, probability P selected individual i in step S4iIt is calculated as follows：
Wherein, K represents Population Size, FiFor particle individual adaptation degree.Propertional model can ensure there is minimum fit
The selected maximum probability of the individual of response, and after new population generation, using optimum maintaining strategy, that is, compare new and old kind
Minimum fitness individual in group, the maximum adaptation degree individual in new population is substituted with the individual；Propertional model can be with general
The mode of rate selection ensures the quality and diversity of population, and optimum maintaining strategy is then to ensure the convergent bar of genetic algorithm
Part.
Preferably, used in step S5 single-point cross method design crossover operator method for：One intersection of stochastic production
Position, the chromosome of crossover location leading portion then is intercoursed using crossover location as boundary using two father's chromosomes, so as to produce two
Whether individual new daughter chromosome, the implementation of crossover operation are determined by crossover probability Pc.
Compared with prior art, the beneficial effects of the invention are as follows：
The lighting location calculation optimization method based on Genetic Particle Swarm mixing of the present invention, letter is used with reference to particle cluster algorithm
The advantages of the advantages of list, fast convergence rate and genetic algorithm ability of searching optimum are strong, utilizes particle cluster algorithm fast convergence rate
Feature carries out the optimization of previous stage, obtains the initial population of certain evolution degree, then carries out the latter half by genetic algorithm
Optimization, improve and calculate local convergence speed and ability of searching optimum of the solution in solution space, and can be by result of calculation to grain
The parameters such as subgroup velocity factor, the hereditary variation factor optimize, and realize efficient, the high accuracy positioning of thunder and lightning.
Brief description of the drawings
Fig. 1 is the flow chart of the lighting location calculation optimization method based on Genetic Particle Swarm mixing of the present invention.
Fig. 2 is the Central Plains lightning location system Query Result of embodiment two.
Fig. 3 is that lightning strike accident positions initial data in embodiment two.
Embodiment
With reference to embodiment, the present invention is further illustrated.Wherein, being given for example only property of accompanying drawing illustrates,
What is represented is only schematic diagram, rather than pictorial diagram, it is impossible to is interpreted as the limitation to this patent；In order to which the reality of the present invention is better described
Example is applied, some parts of accompanying drawing have omission, zoomed in or out, and do not represent the size of actual product；To those skilled in the art
For, some known features and its explanation may be omitted and will be understood by accompanying drawing.
Same or analogous label corresponds to same or analogous part in the accompanying drawing of the embodiment of the present invention；In retouching for the present invention
In stating, it is to be understood that if it is based on accompanying drawing to have the orientation of the instructions such as term " on ", " under ", "left", "right" or position relationship
Shown orientation or position relationship, it is for only for ease of and describes the present invention and simplify description, rather than indicates or imply meaning
Device or element must have specific orientation, with specific azimuth configuration and operation, therefore position relationship described in accompanying drawing
Term being given for example only property explanation, it is impossible to the limitation to this patent is interpreted as, for the ordinary skill in the art, can
To understand the concrete meaning of above-mentioned term as the case may be.
Embodiment one
It is as shown in Figure 1 the flow chart of the lighting location calculation optimization method based on Genetic Particle Swarm mixing of the invention,
Calculation optimization method is particle cluster algorithm and the hybrid algorithm of genetic algorithm, is comprised the following steps：
S1. result (the x just calculated according to the earth ellipsoid calculation formula0,y0), solution space scope delimited as x ∈ using A radiuses
[x0-A,x0+ A], y ∈ [y0-A,y0+ A], and random generation solution particle in solution space delimited, the wherein scope of A radiuses is 4km
~6km；
S2. after step S1, population is made to search for the optimal of particle in x-y two-dimensional spaces using the first fitness function
Fitness function value and optimum position, or population is made using the second fitness function while searched for using time and azimuth information
The optimal fitness function value of particle and optimum position；
S3. after step S2, foundation step S2 optimization information updating flying speed of partcles and particle position parameter are simultaneously excellent
Change the particle inertia factor so that population has preferable ability of searching optimum in initial search phase, in the search phase in later stage
With preferable local search ability；
S4. after step S3, the mixed strategy design alternative operator of adoption rate selection and optimal save strategy, adoption rate choosing
Select strategy and ensure the selected maximum probability of the individual with minimum fitness, using optimum maintaining strategy after new population generation
The minimum fitness individual in new and old population is compared, and substitutes the maximum adaptation degree individual in new population with the individual；
S5. after step S4, single-point cross method design crossover operator is used to determine that the position in crosspoint and gene are handed over
The method changed；And the fitness of particle is calculated by the first fitness function in step S2 or the second fitness function；Work as adaptation
Degree reaches desired value or when iterations reaches maximum, and lightning strike accident position, program determination is calculated；Otherwise, go to step
S6；
S6. after step S5, design mutation operator is with the gene replacement side at the position of definitive variation point and variable position
Method；And the fitness of particle is calculated by the first fitness function in step S2 or the second fitness function；When fitness reaches
When desired value or iterations reach maximum, lightning strike accident position, program determination is calculated；Otherwise, S4 is gone to step.
Wherein, the first fitness function described in step S2It is calculated as follows：
Wherein, di(Xm) for particle current location to the distance of i-th of acquisition station, t0The thunder and lightning derived for each station occurs
The average value of time, tiThe thunder and lightning time of origin derived for i-th of acquisition station, n are the number of particle, and c is inertial factor.
Second fitness function described in step S2It is calculated as follows：
Wherein, di(Xm) for particle current location to the distance of i-th of acquisition station, t0The thunder and lightning derived for each station occurs
The average value of time, tiThe thunder and lightning time of origin derived for i-th acquisition station, n are the number of particle, and c is inertial factor, (xm,
ym)、(xi,yi) for the location of m-th of particle, i-th particle.
Flying speed of partcles described in step S3, the particle position parameter, the particle inertia factor are as follows
Optimize：
Note total number of particles is M, XmFor particle current location, m=1,2 ..., M, PmFor particle history optimal location, P is rememberedgFor
The current global optimum position of population, then the renewal equation of each iteration of particle be：
Vm(k+1)=ω × Vm(k)+c1×Rm1×(pm(k)-Xm(k))+c2×Rm2×(pg(k)-Xm(k))
Xm(k+1)=Xm(k)+Vm(k+1)
Wherein, VmFor flying speed of partcles, k is current renewal step number；ω is the inertial factor between 0.1~0.9；
c1And c2For Studying factors, the acceleration that particle moves towards itself optimal location of the particle and global optimum position is represented respectively,
Typically take c1=c2=2；Rm1And Rm2The diagonal matrix of respectively D × D dimensions, each diagonal element is between [0,1]
Random number；
Inertial factor is calculated as follows：
Wherein, kmaxFor total iterations, ω1=0.9 and ω2=0.4 be respectively the initial inertia factor and final inertia because
Son.
Probability P selected individual i in step S4iIt is calculated as follows：
Wherein, K represents Population Size, FiFor particle individual adaptation degree.
Used in step S5 single-point cross method design crossover operator method for：One crossover location of stochastic production, so
The chromosome of crossover location leading portion is intercoursed using crossover location as boundary using two father's chromosomes afterwards, it is new so as to produce two
Daughter chromosome.
Embodiment two
Certain electric power transmission line trip accident August in 2011 27 days, trip time：15:52:29, B phase faults, switch are jumped
Lock, reclosing failure, differential, distance I section protection acts, former lightning models result of calculation such as Fig. 2 institutes are used for the electric power accident
Show, corresponding acquisition station thunder and lightning initial data is as shown in figure 3, the thunder and lightning generation position calculated by Genetic Particle Swarm Algorithm is
(119.9234,30.1331), and both power line thunders are at a distance of 267.87m.And line walking result is：No. 2 tower B (in) phase strain insulator is small
Number side glass insulator has spark tracking, has and slightly burns, and landform is hillside, and property is shielding, and grounding resistance is 2.2 Ω, thunder
Hit point coordinates：(119.921408333249,30.131419444614).The thunder and lightning calculated by Genetic Particle Swarm Algorithm occurs
Position is (119.9234,30.1331), and both power line thunders are at a distance of 267.87m, it is seen that the height of the achievable thunder and lightning of the present invention
Effect, high accuracy positioning；And can avoid by the total lightning parameter deviation that calculates of different original data sets it is larger, can not ensure
The problem of homologous uniformity.
The coordinate system that use is calculated in the present embodiment is WGS-84 ellipsoidal coordinates, and whether the implementation of crossover operation is by intersecting
Probability PcDetermine, general span be 0.3~0.9, in this implementation in take 0.3；Mutation operation is by mutation probability PmDecide whether
Carry out, general span is 0.001~0.100, and 0.1 is taken in the present embodiment.
Obviously, the above embodiment of the present invention is only intended to clearly illustrate example of the present invention, and is not pair
The restriction of embodiments of the present invention.For those of ordinary skill in the field, may be used also on the basis of the above description
To make other changes in different forms.There is no necessity and possibility to exhaust all the enbodiments.It is all this
All any modification, equivalent and improvement made within the spirit and principle of invention etc., should be included in the claims in the present invention
Protection domain within.
Claims (6)
- A kind of 1. lighting location calculation optimization method based on Genetic Particle Swarm mixing, it is characterised in that the calculation optimization side Method is particle cluster algorithm and the hybrid algorithm of genetic algorithm, is comprised the following steps：S1. result (the x just calculated according to the earth ellipsoid calculation formula0,y0), solution space scope delimited as x ∈ [x using A radiuses0-A,x0 + A], y ∈ [y0-A,y0+ A], and delimit random generation solution particle in solution space；S2. after step S1, population is made to search for the optimal adaptation of particle in x-y two-dimensional spaces using the first fitness function Functional value and optimum position are spent, or population is made using the second fitness function while utilizes time and azimuth information search particle Optimal fitness function value and optimum position；S3. after step S2, foundation step S2 optimization information updating flying speed of partcles and particle position parameter simultaneously optimize grain Sub- inertial factor so that population has preferable ability of searching optimum in initial search phase, has in the search phase in later stage Preferable local search ability；S4. after step S3, the mixed strategy design alternative operator of adoption rate selection and optimal save strategy, adoption rate selection plan Slightly ensure the selected maximum probability of the individual with minimum fitness, compared using optimum maintaining strategy after new population generation The minimum fitness individual gone out in new and old population, and substitute the maximum adaptation degree in new population individual with the individual；S5. after step S4, single-point cross method design crossover operator is used to determine the position in crosspoint and gene swapping Method；And the fitness of particle is calculated by the first fitness function in step S2 or the second fitness function；When fitness reaches When reaching maximum to desired value or iterations, lightning strike accident position, program determination is calculated；Otherwise, S6 is gone to step；S6. after step S5, design mutation operator is with the gene replacement method at the position of definitive variation point and variable position；And The fitness of particle is calculated by the first fitness function in step S2 or the second fitness function；When fitness reaches desired value Or lightning strike accident position, program determination is calculated when reaching maximum in iterations；Otherwise, S4 is gone to step.
- 2. the lighting location calculation optimization method according to claim 1 based on Genetic Particle Swarm mixing, it is characterised in that First fitness function described in step S2It is calculated as follows：<mrow> <msubsup> <mi>&chi;</mi> <mn>1</mn> <mn>2</mn> </msubsup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>m</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mn>1</mn> <msubsup> <mi>&sigma;</mi> <mi>T</mi> <mn>2</mn> </msubsup> </mfrac> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msup> <mrow> <mo>(</mo> <msub> <mi>t</mi> <mi>i</mi> </msub> <mo>-</mo> <mfrac> <mrow> <msub> <mi>d</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>m</mi> </msub> <mo>)</mo> </mrow> </mrow> <mi>c</mi> </mfrac> <mo>-</mo> <msub> <mi>t</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow><mrow> <msub> <mi>t</mi> <mn>0</mn> </msub> <mo>=</mo> <mfrac> <mn>1</mn> <mi>n</mi> </mfrac> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <mrow> <mo>(</mo> <mrow> <msub> <mi>t</mi> <mi>i</mi> </msub> <mo>-</mo> <mfrac> <mrow> <msub> <mi>d</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>m</mi> </msub> <mo>)</mo> </mrow> </mrow> <mi>c</mi> </mfrac> </mrow> <mo>)</mo> </mrow> </mrow>Wherein, di(Xm) for particle current location to the distance of i-th of acquisition station, t0The thunder and lightning time of origin derived for each station Average value, tiThe thunder and lightning time of origin derived for i-th of acquisition station, n are the number of particle, and c is inertial factor.
- 3. the lighting location calculation optimization method according to claim 1 based on Genetic Particle Swarm mixing, it is characterised in that Second fitness function described in step S2It is calculated as follows：<mfenced open = "" close = ""> <mtable> <mtr> <mtd> <mrow> <msubsup> <mi>&chi;</mi> <mn>2</mn> <mn>2</mn> </msubsup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>m</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mn>1</mn> <msubsup> <mi>&sigma;</mi> <mi>T</mi> <mn>1</mn> </msubsup> </mfrac> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msup> <mrow> <mo>(</mo> <mrow> <msub> <mi>t</mi> <mi>i</mi> </msub> <mo>-</mo> <mfrac> <mrow> <msub> <mi>d</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>m</mi> </msub> <mo>)</mo> </mrow> </mrow> <mi>c</mi> </mfrac> <mo>-</mo> <msub> <mi>t</mi> <mn>0</mn> </msub> </mrow> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>+</mo> <mfrac> <mn>1</mn> <msubsup> <mi>&sigma;</mi> <mi>a</mi> <mn>2</mn> </msubsup> </mfrac> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msup> <mrow> <mo>(</mo> <mrow> <msub> <mi>&phi;</mi> <mrow> <mi>m</mi> <mi>i</mi> </mrow> </msub> <mo>-</mo> <mi>arctan</mi> <mfrac> <mrow> <mo>(</mo> <mrow> <msub> <mi>y</mi> <mi>m</mi> </msub> <mo>-</mo> <msub> <mi>y</mi> <mi>i</mi> </msub> </mrow> <mo>)</mo> </mrow> <mrow> <mo>(</mo> <mrow> <msub> <mi>x</mi> <mi>m</mi> </msub> <mo>-</mo> <msub> <mi>x</mi> <mi>i</mi> </msub> </mrow> <mo>)</mo> </mrow> </mfrac> </mrow> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </mtd> </mtr> </mtable> </mfenced>Wherein, di(Xm) for particle current location to the distance of i-th of acquisition station, t0The thunder and lightning time of origin derived for each station Average value, tiThe thunder and lightning time of origin derived for i-th acquisition station, n are the number of particle, and c is inertial factor, (xm,ym)、 (xi,yi) for the location of m-th of particle, i-th particle.
- 4. the lighting location calculation optimization method according to claim 1 based on Genetic Particle Swarm mixing, it is characterised in that Flying speed of partcles described in step S3, the particle position parameter, the particle inertia factor optimize as follows：Note total number of particles is M, XmFor particle current location, m=1,2 ..., M, PmFor particle history optimal location, P is rememberedgFor particle The current global optimum position of group, then the renewal equation of each iteration of particle be：Vm(k+1)=ω × Vm(k)+c1×Rm1×(pm(k)-Xm(k))+c2×Rm2×(pg(k)-Xm(k))Xm(k+1)=Xm(k)+Vm(k+1)Wherein, VmFor flying speed of partcles, k is current renewal step number；ω is the inertial factor between 0.1~0.9；c1With c2For Studying factors, the acceleration that expression particle moves towards itself optimal location of the particle and global optimum position respectively, one As take c1=c2=2；Rm1And Rm2The diagonal matrix of respectively D × D dimensions, each diagonal element is between [0,1] Random number；Inertial factor is calculated as follows：<mrow> <mi>&omega;</mi> <mo>=</mo> <mrow> <mo>(</mo> <msub> <mi>&omega;</mi> <mn>1</mn> </msub> <mo>-</mo> <msub> <mi>&omega;</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> <mo>&times;</mo> <mfrac> <mrow> <mo>(</mo> <msub> <mi>k</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> <mo>-</mo> <mi>k</mi> <mo>)</mo> </mrow> <msub> <mi>k</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> </mfrac> <mo>+</mo> <msub> <mi>&omega;</mi> <mn>2</mn> </msub> <mo>.</mo> </mrow>Wherein, kmaxFor total iterations, ω1=0.9 and ω2=0.4 is respectively the initial inertia factor and final inertial factor.
- 5. the lighting location calculation optimization method according to claim 1 based on Genetic Particle Swarm mixing, it is characterised in that Probability P selected individual i in step S4iIt is calculated as follows：<mrow> <msub> <mi>P</mi> <mi>i</mi> </msub> <mo>=</mo> <msubsup> <mi>F</mi> <mi>i</mi> <mrow> <mo>-</mo> <mn>1</mn> </mrow> </msubsup> <mo>/</mo> <msubsup> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>K</mi> </msubsup> <msubsup> <mi>F</mi> <mi>i</mi> <mrow> <mo>-</mo> <mn>1</mn> </mrow> </msubsup> </mrow>Wherein, K represents Population Size, FiFor particle individual adaptation degree.
- 6. the lighting location calculation optimization method according to claim 1 based on Genetic Particle Swarm mixing, it is characterised in that Used in step S5 single-point cross method design crossover operator method for：One crossover location of stochastic production, then with two Father's chromosome intercourses the chromosome of crossover location leading portion using crossover location as boundary, so as to produce two new daughter chromosomes.
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| CN109374986A (en) * | 2018-09-19 | 2019-02-22 | 中国气象局气象探测中心 | A kind of Lightning Location Method and system based on clustering and grid search |
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| CN113656743A (en) * | 2021-08-12 | 2021-11-16 | 贵州省建筑设计研究院有限责任公司 | Weather big data-based accurate calculation method for expected lightning strike geodetic times of building year |
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| CN115619808A (en) * | 2022-10-31 | 2023-01-17 | 南京航空航天大学 | Electrode plate attaching method and system |
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| CN108227718A (en) * | 2018-01-30 | 2018-06-29 | 安徽宇锋智能科技有限公司 | A kind of automatic transporting trolley path planning method adaptively switched |
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| CN109002634A (en) * | 2018-08-01 | 2018-12-14 | 温州大学 | Overhead distributionnetwork arrester installation site optimization method based on hybrid simulation technology |
| CN109374986A (en) * | 2018-09-19 | 2019-02-22 | 中国气象局气象探测中心 | A kind of Lightning Location Method and system based on clustering and grid search |
| CN109374986B (en) * | 2018-09-19 | 2021-07-09 | 中国气象局气象探测中心 | Thunder and lightning positioning method and system based on cluster analysis and grid search |
| CN110765586A (en) * | 2019-09-30 | 2020-02-07 | 中国人民解放军空军预警学院 | Radar networking optimization station distribution method based on improved particle swarm optimization |
| CN110765586B (en) * | 2019-09-30 | 2023-09-26 | 中国人民解放军空军预警学院 | Radar networking optimization station arrangement method based on improved particle swarm algorithm |
| CN113656743A (en) * | 2021-08-12 | 2021-11-16 | 贵州省建筑设计研究院有限责任公司 | Weather big data-based accurate calculation method for expected lightning strike geodetic times of building year |
| CN113989062A (en) * | 2021-11-18 | 2022-01-28 | 中国船舶重工集团公司第七0四研究所 | Deep sea mining comprehensive control system resource scheduling optimization method |
| CN115619808A (en) * | 2022-10-31 | 2023-01-17 | 南京航空航天大学 | Electrode plate attaching method and system |
| CN115619808B (en) * | 2022-10-31 | 2023-12-01 | 南京航空航天大学 | Electrode slice attaching method and system |
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