CN111818535A - Wireless local area network three-dimensional optimization deployment method fusing multi-population optimization algorithm - Google Patents

Abstract

The invention provides a wireless local area network three-dimensional optimization deployment method integrating multi-population optimization algorithms, which comprises the steps of obtaining a wireless AP (access point) arrangement range, and setting obstacles and signal attenuation values; setting a calculation variable; initializing parameters; obtaining new positions, distances and taste concentration judgment values of individual drosophila melanogaster; substituting the target function into the target function to solve the fitness; according to the fitness, calculating the taste concentration difference between the fitness of each row of fruit flies in the population and the optimal and worst fruit flies respectively; comparing the two taste concentration difference values, dividing the taste concentration difference values into poor subgroups with worst fruit flies as the center, searching according to a particle swarm searching strategy and updating the positions; or the fly is divided into a better group taking the optimal fruit fly as the center to fly around the global optimal information in a Levy mode and update the position; and obtaining the minimum total power and the corresponding position of the wireless AP after the iteration is finished. By implementing the invention, the optimal AP position distribution and the transmitting power are given on the premise of ensuring the coverage area, the energy consumption is saved, and the network deployment cost is reduced.

Description

Wireless local area network three-dimensional optimization deployment method fusing multi-population optimization algorithm

Technical Field

The invention relates to the technical field of wireless local area networks, in particular to a wireless local area network three-dimensional optimization deployment method fusing multi-population optimization algorithms.

Background

In recent years, with the rapid development of mobile internet and the diversification of learning and office places, the traditional internet access manner becomes more and more inconvenient, and the limitation is particularly prominent in public office places with high mobility of people. A large number of Wireless Local Area Networks (WLANs) are deployed in crowded places such as campuses, factories, and companies, and various Wireless access points are widely available in our daily lives. Wireless APs (wireless access points) have basic functions of relaying, bridging, master-slave mode control and the like, but have a limited working range, and APs which are not placed in a reasonable arrangement cause a series of problems, and meanwhile, the distribution positions are too loose, which may cause unstable signal receiving and transmitting, and too tight, which may cause resource waste and bring serious inter-channel interference.

Most of the AP deployment schemes in the current engineering deployment implementation are traditional deployment schemes with uniformly distributed APs. For example, a wireless AP is deployed in each guest room in a hotel, a wireless AP is deployed in each classroom in a school, and each AP is the same in model and transmission power. In some slightly larger occasions, more basic devices are needed to ensure the communication quality. Without proper optimization, this results in wasted energy and redundancy of equipment.

The optimized AP deployment can eliminate coverage holes, reduce co-channel interference, reduce energy consumption and reduce deployment budget, so the AP deployment optimization is always an indispensable theme in WLAN optimization. For example, l.ma et al proposed an optimization scheme for deployment and transmit power of high density APs as early as 2013 to save energy and reduce frequency interference. Green AP clustering, herein using a fuzzy K-means algorithm, aims to guarantee the entire coverage using fewer APs, and AP transmit power optimization, which aims to save more energy and better reduce frequency interference, to save energy and reduce frequency interference. As another example, with the development of artificial intelligence, scholars such as y.zheng have proposed a QoS optimization multi-agent optimization algorithm for WLAN, where a multi-agent system is composed of a group of agents that collectively perform cooperative tasks, and in the multi-agent optimization algorithm, one AP is regarded as one agent, and all APs in a communication area constitute a distributed multi-agent system. On one hand, an agent can sense the environment and take reasonable measures to improve the QoS of the local environment; on the other hand, the system may globally control the number of agents to enhance optimization capabilities and avoid premature convergence. The cooperative search of the multiple intelligent agents not only effectively improves the search efficiency, but also effectively ensures the QoS of the local area network. In 2019, for example, in the case of energy consumption and co-frequency interference caused by high-density APs in a wireless local area network, people such as korean precious, xuchuan, royal, novacular, and zhao national front propose an energy-saving mechanism based on a bayesian game for optimization, an AP transmit power-load-energy consumption relation model is constructed by measuring and analyzing the energy consumption of general AP devices, network state information collected by a network controller in real time is defined by using the relation model and software, an energy consumption optimization model based on the bayesian game is designed, and finally, an energy consumption optimization model is solved by using a social selection function, so that an optimal dormant AP set and transmit power configuration rules under interference limitation are obtained, user traffic unloading and AP transmit power adjustment are completed, and meanwhile, the honesty of the APs participating in the game is guaranteed.

However, the existing AP deployment optimization has disadvantages and shortcomings, and mainly lies in that the used models are all uniform models, and each node has the same coverage radius, so that there is a deviation in wireless AP analysis and deployment in a non-uniform environment, and factors considered in AP position distribution are not comprehensive, so that the transmission power is not optimal, and a certain amount of waste in AP energy consumption and deployment cost is caused.

Therefore, a layered heterogeneous wireless sensor network optimization method is needed, which can provide optimal AP position distribution and transmission power, save energy consumption, and reduce network deployment cost on the premise of ensuring a coverage area.

Disclosure of Invention

The technical problem to be solved by the embodiments of the present invention is to provide a three-dimensional optimization deployment method for a wireless local area network that integrates multiple swarm optimization algorithms, which can provide optimal AP position distribution and transmission power on the premise of ensuring a coverage area, thereby saving energy consumption and reducing network deployment cost.

In order to solve the above technical problem, an embodiment of the present invention provides a wireless local area network three-dimensional optimization deployment method that integrates multiple swarm optimization algorithms, where the method includes the following steps:

step S1, acquiring a wireless AP arrangement range, carrying out grid discretization on the wireless AP arrangement range, and setting three types of obstacles and a signal attenuation value corresponding to each type of obstacle in the wireless AP arrangement range after the grid discretization;

step S2, setting the position coordinates x, y, z and power p of the wireless AP as calculation variables;

s3, setting algorithm related parameters, including defining 4n fruit fly groups, the size S of each fruit fly group_pAnd maximum number of iterations I_max(ii) a Wherein the content of the first and second substances,

the drosophila population is represented as follows:

the positional information for each individual in the 4n population of fruit flies is given by the corresponding (X, Y) two-dimensional coordinates in equation (2):

wherein the subscript letter f represents the variables introduced in the drosophila optimization;

the drosophila population position was initialized and calculated from the following equations (3), (4) and (5):

wherein p is_maxIs the maximum value of the AP transmitting power p; p is a radical of_minIs the minimum value of the AP transmitting power p; rand () is generated to lie in the interval 0,1]A function of the random number above;

flight speed at the time of initializing fruit fly search:

step S4, first calculate the distance from the drosophila individual j to the origin when the kth iteration is performed:

wherein the positions of all drosophila individuals in the jth population at the kth iteration are calculated from equation (8):

then calculating the corresponding taste concentration judgment value:

step S5, substituting the obtained taste concentration judgment value into an objective function (10), and calculating the fitness of each line:

wherein eta is_lFor the penalty function under the condition of not satisfying the constraint condition, the constraint optimization problem is expressed as

C_∑Coverage of the target point for the wireless AP, and

c(AP_i,L_j) For the ith wireless access point AP_iFor the jth target test point L_jIs covered with probability, and

beta is the signal attenuation in the propagation path, and

wherein the content of the first and second substances,

is AP_iDistance to test point location (x, y, z), γ being path loss exponent, representing the rate of increase of path loss with distance, which depends on the surrounding environment and building type; d₀Is a reference distance; α is a reference distance d₀The power of (d); beta is a_sIs the power loss caused by the indoor obstacle and adds the corresponding obstacle attenuation value when crossing the obstacle between the AP and the covered target point;

step S6, respectively finding and recording the fruit flies with the best taste concentration values in the population according to the fitness of the solved function by using a formula (11)

And their corresponding positions

And the Drosophila with the worst value of taste concentration in the population

And their corresponding positions

Step S7, calculating the fruit fly F of each row in the population respectively_lFitness and optimal fruit fly

Difference in taste concentration between

And worst fruit fly

Difference in taste concentration between

Step S8, if

Then divide the first row of fruit flies into the worst fruit flies

In a central poor subgroup, howeverGo back to step S9; otherwise, dividing the first row of fruit flies into the optimal fruit flies

In the preferred group of the center, go to step S10;

and S9, flying the fruit flies in the poor subgroups to the optimal fruit fly positions visually, searching under the guidance of the optimal individuals according to a particle swarm search strategy, and in the kth iteration (k >0), updating the flight speed of the l-th fruit fly of the jth subgroup according to a formula (13) and then updating the position of the l-th fruit fly according to a formula (14).

Where w is a non-negative inertial weight that decreases with increasing number of iterations, and is calculated using equation (15):

w_maxis the maximum inertial weight, w_minFor minimum inertial weight, k is the current iteration number, I_maxThe total number of iterations of the algorithm; c. C₁,c₂Adjusting the maximum step length of learning for the learning factor of the particle; r is₁,r₂Is the interval [0,1]A random number of (c); x is the number of_{f,j,l_pbest},y_{f,j,l_pbest}Is the self-passing optimal position coordinate recorded by the l fruit fly of the jth population;

step S10, making Levy flight around the global optimal information by the fruit fly individuals in the better population group, wherein in the k (k >0) iteration, the position of the l fruit fly of the j population is updated as shown in the formula (16):

wherein a is a parameter for controlling the step length of individual drosophila; levy (λ) is a function that yields a Levy flight distance;

step S11, entering iteration optimization, and repeatedly executing the steps S4 to S10 until the iteration number reaches the maximum iteration number I_maxOr exit iteration after other conditions are met;

and step S12, outputting the drosophila information with global optimal taste concentration, namely obtaining the minimum total power and corresponding position of the wireless AP.

The embodiment of the invention has the following beneficial effects:

the method starts from multiple angles such as signal intensity, coverage range, space environment and the like, a mathematical simulation model is established, a particle swarm search strategy and a twin swarm drosophila optimization algorithm of a Levy flight mechanism are fused to solve the optimal solution of the problem, and an optimal network deployment scheme is generated, so that optimal AP position distribution and transmitting power are given on the premise of ensuring the coverage range, energy consumption is saved, and network deployment cost is reduced.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is within the scope of the present invention for those skilled in the art to obtain other drawings based on the drawings without inventive exercise.

Fig. 1 is a flowchart of a three-dimensional optimization deployment method for a wireless local area network, which is provided by the embodiment of the present invention and integrates a multi-population optimization algorithm;

fig. 2 is a schematic diagram of a two-subgroup drosophila optimization algorithm fusing a particle swarm search strategy and a Levy flight mechanism in the wireless local area network three-dimensional optimization deployment method fusing multi-swarm optimization algorithms provided by the embodiment of the invention;

fig. 3 is a deployment schematic diagram of an undefined AP deployment height before application of the wireless local area network three-dimensional optimization deployment method with fusion of multi-population optimization algorithms provided in the embodiment of the present invention;

fig. 4 is a deployment diagram of limiting APs to be deployed on the top of a floor in fig. 3, and the three-dimensional optimization deployment method for the wireless local area network, which is provided by the embodiment of the invention and integrates a multi-population optimization algorithm, is applied.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings.

As shown in fig. 1, a proposed three-dimensional optimization deployment method for a wireless local area network that integrates multiple swarm optimization algorithms in an embodiment of the present invention includes the following steps:

step S1, acquiring a wireless AP arrangement range, carrying out grid discretization on the wireless AP arrangement range, and setting three types of obstacles and a signal attenuation value corresponding to each type of obstacle in the wireless AP arrangement range after the grid discretization;

step S2, setting the position coordinates x, y, z and power p of the wireless AP as calculation variables;

s3, setting algorithm related parameters, including defining 4n fruit fly groups, the size S of each fruit fly group_pAnd maximum number of iterations I_max(ii) a Wherein the content of the first and second substances,

the drosophila population is represented as follows:

the positional information for each individual in the 4n population of fruit flies is given by the corresponding (X, Y) two-dimensional coordinates in equation (2):

wherein the subscript letter f represents the variables introduced in the drosophila optimization;

the drosophila population position was initialized and calculated from the following equations (3), (4) and (5):

wherein p is_maxIs the maximum value of the AP transmitting power p; p is a radical of_minIs the minimum value of the AP transmitting power p; rand () is generated to lie in the interval 0,1]A function of the random number above;

flight speed at the time of initializing fruit fly search:

step S4, first calculate the distance from the drosophila individual j to the origin when the kth iteration is performed:

wherein the positions of all drosophila individuals in the jth population at the kth iteration are calculated from equation (8):

then calculating the corresponding taste concentration judgment value:

step S5, substituting the obtained taste concentration judgment value into an objective function (10), and calculating the fitness of each line:

wherein eta is_lFor the penalty function under the condition of not satisfying the constraint condition, the constraint optimization problem is expressed as

C_∑Coverage of the target point for the wireless AP, and

c(APi,L_j) For the ith wireless access point AP_iFor the jth target test point L_jIs covered with probability, and

beta is the signal attenuation in the propagation path, and

wherein the content of the first and second substances,

is AP_iDistance to test point location (x, y, z), γ being path loss exponent, representing the rate of increase of path loss with distance, which depends on the surrounding environment and building type; d₀Is a reference distance; α is a reference distance d₀The power of (d); beta is a_sIs the power loss caused by the indoor obstacle and adds the corresponding obstacle attenuation value when crossing the obstacle between the AP and the covered target point;

step S6, respectively finding and recording the fruit flies with the best taste concentration values in the population according to the fitness of the solved function by using a formula (11)

And their corresponding positions

And the Drosophila with the worst value of taste concentration in the population

And their corresponding positions

Step S7, calculating the fruit fly F of each row in the population respectively_lFitness and optimal fruit fly

Difference in taste concentration between

And worst fruit fly

Difference in taste concentration between

Step S8, if

Then divide the first row of fruit flies into the worst fruit flies

In the poor subgroup as the center, go to step S9; otherwise, dividing the first row of fruit flies into the optimal fruit flies

In the preferred group of the center, go to step S10;

and S9, flying the fruit flies in the poor subgroups to the optimal fruit fly positions visually, searching under the guidance of the optimal individuals according to a particle swarm search strategy, and in the kth iteration (k >0), updating the flight speed of the l-th fruit fly of the jth subgroup according to a formula (13) and then updating the position of the l-th fruit fly according to a formula (14).

Where w is a non-negative inertial weight that decreases with increasing number of iterations, and is calculated using equation (15):

w_maxis the maximum inertial weight, w_minFor minimum inertial weight, k is the current iteration number, I_maxThe total number of iterations of the algorithm; c. C₁,c₂Adjusting the maximum step length of learning for the learning factor of the particle; r is₁,r₂Is the interval [0,1]A random number of (c); x is the number of_{f,j,l_pbest},y_{f,j,l_pbest}Is the self-passing optimal position coordinate recorded by the l fruit fly of the jth population;

step S10, making Levy flight around the global optimal information by the fruit fly individuals in the better population group, wherein in the k (k >0) iteration, the position of the l fruit fly of the j population is updated as shown in the formula (16):

wherein a is a parameter for controlling the step length of individual drosophila; levy (λ) is a function that yields a Levy flight distance;

step S11, entering iteration optimization, and repeatedly executing the steps S4 to S10 until the iteration number reaches the maximum iteration number I_maxOr exit iteration after other conditions are met;

and step S12, outputting the drosophila information with global optimal taste concentration, namely obtaining the minimum total power and corresponding position of the wireless AP.

Specifically, in step S1, a network discretization process is performed on the wireless AP arrangement range (e.g., a three-dimensional space of 100m × 10 m) (e.g., the three-dimensional space is discretized into 100 meshes, and the center of each mesh is regarded as a coverage target point), and then three different types of obstacles (e.g., a bearing wall, a brick wall, and a partition layer) are introduced into the target area, where the different obstacles correspond to different signal attenuation values.

In step S2, the position coordinates x, y, z and power p of the AP are set as calculation variables, and for the sake of calculation, the data variables are unified by using a ratio method. The method comprises the following specific steps:

x_k＝(x_k-x_min)/(x_max-x_min)

y_k＝(y_k-y_min)/(y_max-y_min)

z_k＝(z_k-y_min)/(z_max-z_min)

p_k＝(p_k-p_min)/(p_max-p_min)

in step S3, parameters such as the drosophila population, the population position, and the number of iterations are initialized.

In step S4, a smell search process for the fruit flies is performed, and when each fruit fly in the population searches by its smell, it is given a random flight direction and distance. Since the origin position of the taste of food (reference parameter) is unknown, the distance of the individual drosophila from the origin is calculated first, and then the taste concentration determination value thereof is calculated.

In step S5, an objective function including signal coverage and signal attenuation values in the propagation path is set, and the drosophila with better fitness is solved as the drosophila currently searched.

In step S6, the fruit fly with the best taste concentration value and its corresponding location, and the fruit fly with the worst taste concentration value and its corresponding location are found.

In the step S7, in step S7,calculate each row of Drosophila F_lAnd the difference in taste concentration between the fitness of (a) and the drosophila having the best value of the taste concentration, and per row of drosophila F_lAnd the difference in taste concentration between the drosophila having the worst value of taste concentration.

In step S8, comparing the two taste concentration differences, if the former is greater than the latter, dividing the first fruit fly into the worst fruit fly

After the poor subgroup is centered, the process proceeds to step S9; if the former is less than or equal to the latter, dividing the first row of fruit flies into the optimal fruit flies

After the group of the center group is the preferred group, the process proceeds to step S10.

In step S9, the fruit flies in the poor subgroup fly to the optimal fruit fly position visually, and under the guidance of the optimal individual, the fruit flies are searched according to the particle swarm search strategy, and the flying speed and position are updated.

In step S10, the individual fruit flies in the better group fly Levy around the global optimum information and update the positions.

In step S11, iteration optimization is entered, and steps S4 to S10 are repeatedly executed until the iteration number reaches the maximum iteration number I_maxOr if other conditions are met, exit the iteration.

In step S12, the drosophila information with globally optimal taste concentration output in the last iteration of step S11 is output, i.e., the minimum total power of wireless APs and corresponding locations are obtained.

It should be noted that the invention integrates the particle swarm search strategy and the double-subgroup drosophila optimization algorithm (Levy _ PSO _ FOA) of the Levy flight mechanism. The algorithm takes an FOA algorithm as a main body, and in the fruit fly population iteration process, the distances Dist between fruit fly individuals i in the population and the optimal individuals and the worst individuals in the contemporary population are respectively calculated_{i_best}And Dist_{i_worst}If Dist_{i_best}＞Dist_{i_worst}Dividing the individual fruit fly i into worst onesAnd if not, dividing the poor subgroup with the body as the center into a better subgroup with the optimal body as the center (the better subgroup and the poor subgroup are divided again at each iteration, and the two subgroups dynamically change). And then according to different characteristics of the two subgroups, the poor subgroup is searched by a particle swarm search strategy under the guidance of the optimal individual, the better subgroup flies around the global optimal information in a Levy manner, and the two subgroups exchange information through the updating of the optimal individual and the recombination of the subgroups. FIG. 2 is a schematic diagram of Levy _ PSO _ FOA.

As shown in fig. 3, the deployment method is a deployment schematic diagram of the three-dimensional optimization deployment method for the wireless local area network integrated with the multi-population optimization algorithm, where the AP deployment height is not defined before application; fig. 4 is a schematic deployment diagram of the AP deployed on the top of the floor in fig. 3, in which the three-dimensional optimization deployment method for the wireless local area network, which is provided by the embodiment of the present invention and integrates multiple swarm optimization algorithms, is applied. As can be seen from comparison between fig. 3 and fig. 4, fig. 4 provides the optimal AP location distribution and transmission power on the premise of ensuring the coverage area in fig. 3, thereby saving energy consumption and reducing network deployment cost.

The embodiment of the invention has the following beneficial effects:

the method starts from multiple angles such as signal intensity, coverage range, space environment and the like, a mathematical simulation model is established, a particle swarm search strategy and a twin swarm drosophila optimization algorithm of a Levy flight mechanism are fused to solve the optimal solution of the problem, and an optimal network deployment scheme is generated, so that optimal AP position distribution and transmitting power are given on the premise of ensuring the coverage range, energy consumption is saved, and network deployment cost is reduced.

It will be understood by those skilled in the art that all or part of the steps in the method for implementing the above embodiments may be implemented by relevant hardware instructed by a program, and the program may be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc.

While the invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not to be limited to the disclosed embodiment, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.

Claims (1)

1. A wireless local area network three-dimensional optimization deployment method fusing multi-population optimization algorithms is characterized by comprising the following steps:

step S1, acquiring a wireless AP arrangement range, carrying out grid discretization on the wireless AP arrangement range, and setting three types of obstacles and a signal attenuation value corresponding to each type of obstacle in the wireless AP arrangement range after the grid discretization;

step S2, setting the position coordinates x, y, z and power p of the wireless AP as calculation variables;

s3, setting algorithm related parameters, including defining 4n fruit fly groups, the size S of each fruit fly group_pAnd maximum number of iterations I_max(ii) a Wherein the content of the first and second substances,

the drosophila population is represented as follows:

the positional information for each individual in the 4n population of fruit flies is given by the corresponding (X, Y) two-dimensional coordinates in equation (2):

wherein the subscript letter f represents the variables introduced in the drosophila optimization;

the drosophila population position was initialized and calculated from the following equations (3), (4) and (5):

wherein p is_maxIs the maximum value of the AP transmitting power p; p is a radical of_minIs the minimum value of the AP transmitting power p; rand () is generated to lie in the interval 0,1]A function of the random number above;

flight speed at the time of initializing fruit fly search:

step S4, first calculate the distance from the drosophila individual j to the origin when the kth iteration is performed:

wherein the positions of all drosophila individuals in the jth population at the kth iteration are calculated from equation (8):

then calculating the corresponding taste concentration judgment value:

step S5, substituting the obtained taste concentration judgment value into an objective function (10), and calculating the fitness of each line:

wherein eta is_lFor the penalty function under the condition of not satisfying the constraint condition, the constraint optimization problem is expressed as

C_∑Coverage of the target point for the wireless AP, and

c(AP_i,L_j) For the ith wireless access point AP_iFor the jth target test point L_jIs covered with probability, and

beta is the signal attenuation in the propagation path, and

wherein the content of the first and second substances,

is AP_iDistance to test point location (x, y, z), γ being path loss exponent, representing the rate of increase of path loss with distance, which depends on the surrounding environment and building type; d₀Is a reference distance; α is a reference distance d₀The power of (d); beta is a_sIs the power loss caused by the indoor obstacle and adds the corresponding obstacle attenuation value when crossing the obstacle between the AP and the covered target point;

step S6, respectively finding and recording the fruit flies with the best taste concentration values in the population according to the fitness of the solved function by using a formula (11)

And their corresponding positions

And the Drosophila with the worst value of taste concentration in the population

And their corresponding positions

Step S7, calculating the fruit fly F of each row in the population respectively_lFitness and optimal fruit fly

Difference in taste concentration between

And worst fruit fly

Difference in taste concentration between

Step S8, if

Then divide the first row of fruit flies into the worst fruit flies

In the poor subgroup as the center, go to step S9; otherwise, dividing the first row of fruit flies into the optimal fruit flies

In the preferred group of the center, go to step S10;

step S9, flying the fruit flies in the poor subgroup to the optimal fruit fly position by vision, searching according to the particle swarm search strategy under the guidance of the optimal individual, in the kth iteration (k >0), the l-th fruit fly of the jth population updates the flight speed of the fruit fly according to the formula (13) firstly, and then updates the position of the fruit fly according to the formula (14):

where w is a non-negative inertial weight that decreases with increasing number of iterations, and is calculated using equation (15):

w_maxis the maximum inertial weight, w_minFor minimum inertial weight, k is the current iteration number, I_maxThe total number of iterations of the algorithm; c. C₁,c₂Adjusting the maximum step length of learning for the learning factor of the particle; r is₁,r₂Is the interval [0,1]A random number of (c); x is the number of_{f,j,l_pbest},y_{f,j,l_pbest}Is the self-passing optimal position coordinate recorded by the l fruit fly of the jth population;

step S10, making Levy flight around the global optimal information by the fruit fly individuals in the better population group, wherein in the k (k >0) iteration, the position of the l fruit fly of the j population is updated as shown in the formula (16):

wherein a is a parameter for controlling the step length of individual drosophila; levy (λ) is a function that yields a Levy flight distance;

step S11, entering iteration optimization, and repeatedly executing the steps S4 to S10 until the iteration number reaches the maximum iteration number I_maxOr exit iteration after other conditions are met;

and step S12, outputting the drosophila information with global optimal taste concentration, namely obtaining the minimum total power and corresponding position of the wireless AP.

Images