CN103857046B - Self-adaptive resource distribution method of cognition OFDM network based on spectrum filling - Google Patents

Abstract

The invention discloses a self-adaptive resource distribution method of a cognition OFDM network based on spectrum filling. The self-adaptive resource distribution method of the cognition OFDM network based on spectrum filling comprises the following steps that a cognition base station detects a spectrum hole and the noise power on a null sub-carrier; the cognition base station obtains channel state information of each cognition user and a real-time service transmission speed requirement; the cognition base station distributes the frequency and power resources in the cognition OFDM network based on spectrum filling by utilizing a quantum particle swarm method; the cognition base station initializes the parameters of the quantum particle swarm method; each particle position vector in a group is initialized and comprises the frequency and power resources; the adaptation value of each particle in the group and the particle with the minimum adaptation degree in the group are determined; the position of each particle in the group is determined again according to each particle position vector and the position vector of the particle with the minimum adaptation degree. The self-adaptive resource distribution method of the cognition OFDM network based on spectrum filling distributes the resources from the two dimensions of frequency and power and can efficiently use the wireless resources in the cognition OFDM network based on spectrum filling.

Description

Based on the cognitive OFDM network self-adaptings resource allocation methods that frequency spectrum is filled

Technical field

The invention belongs to communication technical field, particularly a kind of cognitive OFDM network self-adaptings resource filled based on frequency spectrum Distribution method.

Background technology

In recent years, developing rapidly with wireless communication technology so that limited frequency spectrum resource is increasingly deficient.But the U.S. The research report of Federal Communications Committee shows：The service condition of frequency spectrum resource is extremely uneven, is embodied in：Distribute at present The mandate frequency range availability of frequency spectrum there was only 15%-85%, and the availability of frequency spectrum of below 3GHz frequency ranges only has 35%.This is due to length The static spectral method of salary distribution since phase causes the frequency spectrum of many suitable transmission of wireless signals often in idle state, therefore makes With regard to the significant wastage of frequency spectrum resource.Under this background, J.Mitola doctors III proposed cognitive radio in 1999 Concept；It is, by continuous cognition PERCOM peripheral communication environmental information, to recognize the frequency spectrum cavity-pocket in current environment, realize other and award Power recycling of the frequency spectrum resource on spatial domain, time domain and frequency domain, so as to effectively improve the availability of frequency spectrum.In J.Mitola III On the basis of doctor's research work, the research worker of Virginia, US engineering college clearly proposed cognition wireless first in 2005 The concept of network, cognition wireless network are the extensions of cognitive radio technology, and it not only payes attention to the reasonable utilization of frequency spectrum resource, more Focus on the optimization of overall performance of network.

As the frequency spectrum cavity-pocket that cognition wireless network finds is likely located at very wide spectral range, and be it is discontinuous, Therefore research worker is improved to OFDM technology, it is proposed that suitable for the NC-OFDM technologies of cognition network.Based on NC-OFDM The access way of technology is not that all subcarriers in an OFDM symbol can be used by cognitive user, authorized user's meeting Which part subcarrier is taken, in order to avoid interfering to authorized user, these occupied subcarriers can not be used for transmitting Data, by the cognition wireless network based on NC-OFDM technologies, the cognitive OFDM networks referred to as filled based on frequency spectrum.

It is very deficient based on the Radio Resource in the cognitive OFDM networks that frequency spectrum is filled, and these Radio Resources become restriction The main aspect of systematic function, it is therefore desirable to these Radio Resources are closed in the cognitive OFDM networks filled based on frequency spectrum The distribution of reason.The cognitive OFDM Internet resources distribution technique filled based on frequency spectrum is from cognition network and authorizes interference between network The angle of coordination is set out and studies the dynamic sub carrier and power distributing technique of cognition network, its objective is do not affecting authorized user In the case of network proper communication, as efficiently as possible using these Radio Resources reaching higher spectrum efficiency, while also Need to ensure the satisfaction of cognitive user.But compared with conventional OFDM networks, the cognitive OFDM networks tool filled based on frequency spectrum Have many special natures so that existing resource allocation methods are difficult to meet its requirement, especially meet green communications requirement and The cognitive OFDM network resource allocation methods filled based on frequency spectrum of real-time traffic demands still lack research.

Patent 1（Resource allocation methods in cognitive OFDM system based on quality of service, Nanjing Univ. of Posts and Telecommunications, Publication number CN102291352A, application number CN201110236043.6, applying date 2011.08.17）Disclose a kind of based on business Resource allocation methods in the cognitive OFDM system of quality, the method is in the total transmit power of secondary user and primary user Interference threshold limit under, according to the demand percentage of secondary user, adaptive bit and power allocation is carried out to real time business, until Meet its rate requirement, finally by remaining resource allocation to non-real-time service.The method is recognizing for Underlay patterns Know the resource allocation methods of OFDM network designs, do not have to design the cognitive OFDM Internet resources distribution side based on frequency spectrum fill pattern Method.

Patent 2（A kind of channel allocation method limited based on conflict threshold, Nanjing Univ. of Posts and Telecommunications, publication number CN103326984A, application number CN201310279834.6, applying date 2013.07.04）Disclose a kind of based on conflict threshold limit Conflict threshold is combined by the channel allocation method of system, the method with effective throughput, according to the transmission number of each sub-channels According to bag size and its idle probability distribution parameters, selection meets the minimal number subchannel of its data requirement, more so as to allow Secondary user send data.But the method only considers the dynamically distributes of resource from frequency category, it is impossible to enter one from power dimension Step optimization resource utilization.In addition, above-mentioned two method is all with maximize handling capacity as optimization aim, it is impossible to meet cognitive The green communications resource allocation requirements of OFDM networks.

The content of the invention

It is an object of the invention to provide self adaptation in a kind of cognitive OFDM networks efficiently, reliably based on frequency spectrum filling Resource allocation methods, from two dimension dynamic on-demand Resources allocation of frequency and power, fully excavate the cognition filled based on frequency spectrum Available frequency spectrum cavity-pocket resource in OFDM networks.

The technical solution for realizing the object of the invention is：A kind of cognitive OFDM network self-adaptings money filled based on frequency spectrum Source distribution method, comprises the following steps：

Step 1, the noise power on cognitive base station detection frequency spectrum cavity-pocket and idle sub-carrier；

Step 2, cognitive base station obtain each cognitive user channel condition information and real-time service transmission rate requirement；

Step 3, cognitive base station is using quantum particle swarm method to the frequency in the cognitive OFDM networks filled based on frequency spectrum It is allocated with power resource, comprises the following steps：

Step 3.1, cognitive base station initialize the parameter of quantum particle swarm method；

Step 3.2, initializes each the particle position vector in population, and the position vector includes frequency and power resource；

Step 3.3, determine each particle in population adaptive value and population in the minimum particle of fitness；

Step 3.4, redefines in population according to the particle position vector that each particle position vector sum fitness is minimum The position of each particle；

Step 3.5, repeat step 3.3~step 3.4N_gIt is secondary, the optimal solution of output frequency and power resource allocation, N_gRepresent The iterationses of quantum particle swarm method.

Compared with prior art, its remarkable advantage is the present invention：（1）Using flexible subcarrier and power distribution method, Meet the demand of cognitive user real time business resource allocation；（2）Filled out from two dimension dynamically distributes of frequency and power based on frequency spectrum The cognitive OFDM networks sub-carriers filled and power resource, can use in fully having excavated the cognitive OFDM networks filled based on frequency spectrum Frequency spectrum cavity-pocket resource；（3）Frequency that can be in the cognitive OFDM networks filled based on frequency spectrum of efficient utilization and power resource, be Promote to provide technical support based on the cognitive OFDM networks green communications that frequency spectrum is filled.

Description of the drawings

Fig. 1 is the flow chart of the cognitive OFDM network self-adaptings resource allocation methods that the present invention is filled based on frequency spectrum.

Fig. 2 obtains each cognitive user channel condition information and real-time service transmission rate requirement for cognitive base station in the present invention Schematic diagram.

Fig. 3 is the flow chart that cognitive base station carries out resource allocation using quantum particle swarm method in the present invention.

Fig. 4 is the cognitive OFDM networks sub-carriers occupancy situation that the present invention is filled based on frequency spectrum.

Specific embodiment

Below in conjunction with the accompanying drawings and specific embodiment is described in further detail to the present invention.

With reference to Fig. 1, cognitive OFDM network self-adapting resource allocation methods of the present invention based on frequency spectrum filling, including following step Suddenly：

Step 1, the noise power on cognitive base station detection frequency spectrum cavity-pocket and idle sub-carrier, specially：Filled out based on frequency spectrum The cognitive base station detection filled in cognitive OFDM networks authorizes the radio spectrum resources in network, at the information in cognitive base station Reason module analysis obtain the spectrum utilization status information in current grant network, and formation can characterize the actual parameter of frequency spectrum cavity-pocket Report that frequency spectrum detection is reported and is sent to by way of broadcast based on frequency by cognitive base station with the frequency spectrum detection of reflection frequency spectrum cavity-pocket Cognitive user in spectrum filling cognition OFDM networks, cognitive base station are detected in idle sub-carrier according to the examining report of frequency spectrum cavity-pocket Noise power.

Step 2, cognitive base station obtain each cognitive user channel condition information and real-time service transmission rate requirement, specifically For：Cognitive user estimates previous based on frequency spectrum filling cognition OFDM network downstream links by the message processing module of cognitive terminal The channel condition information of OFDM symbol, cognitive user is by filling the letter that cognitive OFDM network uplink will be estimated based on frequency spectrum Channel state information feeds back to cognitive base station, and cognitive user is by filling cognitive OFDM network uplink by real-time industry based on frequency spectrum Business transmission rate requirements feed back to cognitive base station, as shown in Figure 2.

Step 3, cognitive base station is using quantum particle swarm method to the frequency in the cognitive OFDM networks filled based on frequency spectrum It is allocated with power resource, comprises the following steps with reference to Fig. 3：

Step 3.1, cognitive base station initialize the parameter of quantum particle swarm method, specially：

（1）Number of particles N in initialization quantum particle swarm method population_pAnd N_p∈ [500,600], quantum particle swarm method Iterationses N_gAnd N_g∈ [1000,1200], moment t cognitive user m subcarrier distribution indicator variable on sub-carrierkAndMoment t cognitive user m power distribution indicator variable on sub-carrierkAnd

（2）Moment t cognitive user m transfer rate on sub-carrierk is determined using formula (1)

In formula (1), W represents the cognitive OFDM network bandwidths filled based on frequency spectrum, and K represents the cognition filled based on frequency spectrum OFDM network number of sub carrier wave, N₀The noise power on subcarrier is represented,Represent moment t cognitive user m on sub-carrierk Channel gain；T express times are indexed, and m represents that cognitive user is indexed, and k represents sub-carrier indices；

（3）The desired positions vector of i-th particle when initializing the gen time iterationWith the gen time iteration kind The desired positions vector of all particles in groupInitialization iterationses sequence number gen=1.

Step 3.2, initializes each the particle position vector in population, and the position vector includes frequency and power resource, Specially：

Each particle position vector x=[c, p] in initialization population, wherein vector c represents that subcarrier distribution indicates to become Amount, vectorial c meet formula (2) and formula (3)：

Vectorial p represents power distribution indicator variable, and vectorial p meets formula (4)：

Wherein, K_tThe available idle sub-carrier set of moment t is represented,Represent the real-time service transmission of cognitive user m Rate requirement, M represent the sum of cognitive user.

Step 3.3, determine each particle in population adaptive value and population in the minimum particle of fitness, specially：

Adaptive value f (the x of each particle in population are determined using formula (5)_i(t))；

IfOrder

IfOrder

Wherein, f (x_i(gen) adaptive value of i-th particle in the gen time iteration) is represented,Represent i-th The adaptive value of individual particle desired positions vector in the gen time iteration,Represent the population in the gen time iteration The adaptive value of middle desired positions vector particle.

Step 3.4, redefines in population according to the particle position vector that each particle position vector sum fitness is minimum The position of each particle, specially：

It is i-th particle in the gen+1 time iteration using the latest position of each particle in formula (6) Population Regeneration Position x_i(gen+1)：

Wherein, x_op(gen) representWithBetween random site, and using formula (7) determine x_op(gen)：

x_bestThe meansigma methodss of all particle desired positions in population are represented, and x is determined using formula (8)_best：

Wherein α, β are x_i(gen+1) Dynamic gene of position vector, μ is selective factor B, ξ₁RepresentPosition to The weight of amount, ξ₂RepresentThe weight of position vector, β, μ, ξ₁And ξ₂Randomly generate and α ∈ between interval [0,1] [0.6,0.65]。

Step 3.5, repeat step 3.3~step 3.4N_gIt is secondary, the optimal solution of output frequency and power resource allocation, N_gRepresent The iterationses of quantum particle swarm method.

Embodiment 1

With reference to the cognitive OFDM network self-adaptings resource allocation methods that Fig. 1~4, the present invention are filled based on frequency spectrum, step is：

Step 1, the noise power on cognitive base station detection frequency spectrum cavity-pocket and idle sub-carrier.

There is N=16 authorized user in authorizing network, authorize network system bandwidth W=8MHz, available subcarrier number K= 128, the noise N of cognitive user on subcarrier k₀=1 × 10^-8W；Frequency band is transferred to frequency spectrum cavity-pocket from authorized user's seizure condition Probability is 0.8, and frequency band be transferred to mandate to need the probability for using to be 0.8 from cognitive user seizure condition；Filled based on frequency spectrum Cognitive OFDM networks in cognitive base station detection authorize network in radio spectrum resources, at the information in cognitive base station Reason module analysis obtain the spectrum utilization status information in current grant network, and formation can characterize the actual parameter of frequency spectrum cavity-pocket Report that frequency spectrum detection is reported and is sent to by way of broadcast based on frequency by cognitive base station with the frequency spectrum detection of reflection frequency spectrum cavity-pocket Cognitive user in the cognitive OFDM networks of spectrum filling, cognitive base station detect idle sub-carrier according to the examining report of frequency spectrum cavity-pocket On noise power；

Fig. 4 be based on frequency spectrum fill cognitive OFDM networks subcarrier occupancy situation, cognitive base station usable spectrum resource K_t ={ 8 × (kt-1)+1~8 × kt, kt=1,3,5,7,9,11,13,15 }, f_c=990MHz, Δ f=62.5KHz, F₁= 0.5MHz,F₂=1MHz, F₃=1.5MHz, F₄=2MHz；f_cThe cognitive OFDM networks initial frequency filled based on frequency spectrum is represented, F₁、F₂、F₃And F₄The relative initial frequency of frequency band 1, frequency band 2, frequency band 3 and frequency band 4 is represented, Δ f represents each subcarrier spacing.

Step 2, cognitive base station obtain each cognitive user channel condition information and real-time service transmission rate requirement.

The communication radius of cognitive base station are R=2Km, have M=8 cognitive user in cognition network, and cognitive base station is to cognition The channel condition information of telex network link is constant within an OFDM symbol time；Cognitive user is by cognitive terminal Message processing module estimates the channel condition information of the previous OFDM symbol of cognitive OFDM network downstreams link filled based on frequency spectrum, Cognitive user is by the cognitive OFDM network uplink filled based on frequency spectrum by the information feedback estimated to cognition Real-time service transmission rate requirement is fed back by base station, cognitive user by the cognitive OFDM network uplink filled based on frequency spectrum To cognitive base station, Fig. 2 channel condition informations for needed for the cognitive OFDM Internet resources distribution filled based on frequency spectrum of the present invention are estimated Meter schematic diagram.

Step 3, cognitive base station is using quantum particle swarm method to the frequency in the cognitive OFDM networks filled based on frequency spectrum It is allocated with power resource.

Each cognitive user sends the minimum transmission rate demand information required for real time business to cognitive base stationFig. 3 is that cognitive base station performs the resource allocation methods flow process based on quantum particle swarm：

First, cognitive base station initializes the parameter of quantum particle swarm method, initializes N_p=560, N_g=1100, WithDetermined using formula (1)InitializationWithInitialization gen=1；

Then, each the particle position vector in population is initialized, the position vector includes frequency and power resource, initially Change each particle position vector x=[c, the p] in population, vectorial c represents subcarrier distribution indicator variable, and vectorial c meets formula (2) and formula (3), vectorial p represents power distribution indicator variable, and vectorial p meets formula (4), K_tRepresent the moment t available free time T easet ofasubcarriers,The real-time service transmission rate requirement of cognitive user m is represented, M=3 represents the sum of cognitive user；

Secondly, determine each particle in population adaptive value and population in the minimum particle of fitness, using formula (5) really Determine the adaptive value f (x of each particle in population_i(t))；IfOrderSuch as ReallyOrder

Again, each in population is redefined according to the minimum particle position vector of each particle position vector sum fitness The position of particle, using latest position x of each particle in formula (6) Population Regeneration_i(gen+1) x is determined using formula (7),_op (gen) x is determined using formula (8),_best；

Finally, gen ← gen+1, repeat the above steps N are made_gIt is secondary, outputAs optimal solution.

In sum, the present invention is for real-time traffic demands in the cognitive OFDM networks filled based on frequency spectrum, with minimum It is optimization aim based on the cognitive OFDM network launches power that frequency spectrum is filled, it is proposed that dynamic resource allocation method, meets cognitive User's minimum transmission rate demand；During the method can fully excavate the cognitive OFDM networks filled based on frequency spectrum, available frequency spectrum is empty Hole resource, from two dimension dynamic on-demand Resources allocation of frequency and power, is the cognitive OFDM networks for promoting to fill based on frequency spectrum Green communications provide technical support.

Claims (7)

1. it is a kind of based on frequency spectrum fill cognitive OFDM network self-adaptings resource allocation methods, it is characterised in that including following step Suddenly：

Step 1, the noise power on cognitive base station detection frequency spectrum cavity-pocket and idle sub-carrier；

Step 2, cognitive base station obtain each cognitive user channel condition information and real-time service transmission rate requirement；

Step 3, cognitive base station is using quantum particle swarm method to the frequency and work(in the cognitive OFDM networks filled based on frequency spectrum Rate resource is allocated, and comprises the following steps：

Step 3.1, cognitive base station initialize the parameter of quantum particle swarm method；

Step 3.2, initializes each the particle position vector in population, and the position vector includes frequency and power resource；

Step 3.3, determine each particle in population adaptive value and population in the minimum particle of fitness；

Step 3.4, redefines each in population according to the particle position vector that each particle position vector sum fitness is minimum The position of particle；

Step 3.5, repeat step 3.3~step 3.4N_gIt is secondary, the optimal solution of output frequency and power resource allocation, N_gRepresent quantum The iterationses of particle swarm optimization.

2. it is according to claim 1 based on frequency spectrum fill cognitive OFDM network self-adaptings resource allocation methods, its feature It is, the noise power on cognitive base station detection frequency spectrum cavity-pocket and idle sub-carrier described in step 1, specially：Based on frequency spectrum Cognitive base station detection in filling cognition OFDM networks authorizes the radio spectrum resources in network, the information in cognitive base station Processing module analysis obtains the spectrum utilization status information in current grant network, and formation can characterize effective ginseng of frequency spectrum cavity-pocket Frequency spectrum detection is reported to be sent to by way of broadcast and is based on by the frequency spectrum detection report of number and reflection frequency spectrum cavity-pocket, cognitive base station Cognitive user in frequency spectrum filling cognition OFDM networks, cognitive base station detect idle sub-carrier according to the examining report of frequency spectrum cavity-pocket On noise power.

3. it is according to claim 1 based on frequency spectrum fill cognitive OFDM network self-adaptings resource allocation methods, its feature It is that the cognitive base station described in step 2 obtains each cognitive user channel condition information and real-time service transmission rate requirement, specifically For：Cognitive user estimates previous based on frequency spectrum filling cognition OFDM network downstream links by the message processing module of cognitive terminal The channel condition information of OFDM symbol, cognitive user is by filling the letter that cognitive OFDM network uplink will be estimated based on frequency spectrum Channel state information feeds back to cognitive base station, and cognitive user is by filling cognitive OFDM network uplink by real-time industry based on frequency spectrum Business transmission rate requirements feed back to cognitive base station.

4. it is according to claim 1 based on frequency spectrum fill cognitive OFDM network self-adaptings resource allocation methods, its feature It is that the cognitive base station described in step 3.1 initializes the parameter of quantum particle swarm method, specially：

(1) initialize number of particles N in quantum particle swarm method population_pAnd N_p∈ [500,600], quantum particle swarm method repeatedly For times N_gAnd N_g∈ [1000,1200], moment t cognitive user m subcarrier distribution indicator variable on sub-carrierkAndMoment t cognitive user m power distribution indicator variable on sub-carrierkAnd

(2) moment t cognitive user m transfer rate on sub-carrierk is determined using formula (1)

$b_{m,k}^t=\frac{W}{K}{\log }_2(1+\frac{p_{m,k}^t{h}_{m,k}^t}{N_0})---\left(1\right)$

In formula (1), W represents the cognitive OFDM network bandwidths filled based on frequency spectrum, and K represents the cognitive OFDM nets filled based on frequency spectrum String bag carrier number, N₀The noise power on subcarrier is represented,Represent that channel increases moment t cognitive user m on sub-carrierk Benefit；T express times are indexed, and m represents that cognitive user is indexed, and k represents sub-carrier indices；

(3) the desired positions vector of i-th particle when initializing the gen time iterationIn the gen time iteration population The desired positions vector of all particlesInitialization iterationses sequence number gen=1.

5. it is according to claim 1 based on frequency spectrum fill cognitive OFDM network self-adaptings resource allocation methods, its feature It is, each the particle position vector in the initialization population described in step 3.2, specially：

Each particle position vector in initialization populationIt is wherein vectorialSubcarrier distribution indicator variable is represented, to AmountMeet formula (2) and formula (3)：

$c_{m,k}^t=\left\{\begin{array}{cc}\[0,1\],& k\∈K_t\\ \left\{0\right\},& else\end{array}\right.,\∀m,k,t---\left(2\right)$

$\underset{m=1}{\overset{M}{\mathrm{\Σ}}}{c}_{m,k}^t\≤1,c_{m,k}^t\≥0,\∀k,t---\left(3\right)$

VectorRepresent power distribution indicator variable, vectorMeet formula (4)：

$\underset{k=1}{\overset{K}{\mathrm{\Σ}}}{c}_{m,k}^t{b}_{m,k}^t\≥R_m^{\min }---\left(4\right)$

Wherein, K_tThe available idle sub-carrier set of moment t is represented,Represent that the real-time service transmission speed of cognitive user m is needed Ask, M represents the sum of cognitive user.

6. it is according to claim 1 based on frequency spectrum fill cognitive OFDM network self-adaptings resource allocation methods, its feature It is, the minimum particle of fitness in the adaptive value of each particle and population in the determination population described in step 3.3, specially：

The adaptive value of each particle in population is determined using formula (5)

$f\left(x_i\left(gen\right)\right)=\underset{m=1}{\overset{M}{\mathrm{\Σ}}}\underset{k=1}{\overset{K}{\mathrm{\Σ}}}{c}_{m,k}^t{p}_{m,k}^t---\left(5\right)$

IfOrder

IfOrder

Wherein,Adaptive value of i-th particle in the gen time iteration is represented,Represent i-th particle The adaptive value of desired positions vector in the gen time iteration,Represent best in population in the gen time iteration The adaptive value of position vector particle.

7. it is according to claim 1 based on frequency spectrum fill cognitive OFDM network self-adaptings resource allocation methods, its feature It is to be redefined in population according to the minimum particle position vector of each particle position vector sum fitness described in step 3.4 The position of each particle, specially：

It is position of i-th particle in the gen+1 time iteration using the latest position of each particle in formula (6) Population Regeneration

$x_i\left(gen+1\right)=\left\{\begin{array}{ccc}{x}_{op}\left(gen\right)+\mathrm{\&alpha;}|x_{best}-x_i\left(gen\right)|\ln \left(1/\mathrm{\&beta;}\right),& if& \mathrm{\&mu;}\&GreaterEqual;0.5\\ x_{op}\left(gen\right)-\mathrm{\&alpha;}|x_{best}-x_i\left(gen\right)|\ln \left(1/\mathrm{\&beta;}\right),& if& \mathrm{\&mu;}<0.5\end{array}\right.---\left(6\right)$

Wherein,RepresentWithBetween random site, and using formula (7) determine

$x_{op}\left(gen\right)=\frac{{\mathrm{\&xi;}}_1{x}_i^{best}\left(gen\right)+{\mathrm{\&xi;}}_2{x}_g^{best}\left(gen\right)}{{\mathrm{\&xi;}}_1+{\mathrm{\&xi;}}_2}---\left(7\right)$

The meansigma methodss of all particle desired positions in population are represented, and is determined using formula (8)

$x_{best}=\frac{1}{N_p}\underset{i=1}{\overset{N_p}{\&Sigma;}}{x}_i^{best}\left(gen\right)---\left(8\right)$

Wherein α, β areThe Dynamic gene of position vector, μ is selective factor B, ξ₁RepresentPosition vector Weight, ξ₂RepresentThe weight of position vector, β, μ, ξ₁And ξ₂Randomly generate between interval [0,1] and α ∈ [0.6, 0.65]。