CN111818535B - Wireless local area network three-dimensional optimization deployment method fusing multi-population optimization algorithm - Google Patents

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

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CN111818535B
CN111818535B CN202010506922.5A CN202010506922A CN111818535B CN 111818535 B CN111818535 B CN 111818535B CN 202010506922 A CN202010506922 A CN 202010506922A CN 111818535 B CN111818535 B CN 111818535B
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唐震洲
刘鹏
李方靖
孟欣
郭瑞琪
易家欢
应子怡
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Wenzhou University
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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 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, one wireless AP is deployed in each guest room in a hotel, one wireless AP is deployed in each classroom in a school, and the model and the transmission power of each AP are the same. 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 whole coverage using less APs, and AP transmit power optimization aiming 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-channel interference caused by high-density APs in a wireless local area network, people such as hangeul, xu Chuan, wang Qianyun, wang Xinheng, zhao Guofeng, and the like, an energy-saving mechanism based on a bayes game is proposed for optimization, an AP transmission power-load-energy consumption relation model is constructed by measuring and analyzing the energy consumption of general AP devices, then the relation model and network state information collected by a software defined network controller in real time are used for designing an energy consumption optimization model based on the bayes game, and finally, the energy consumption optimization model is solved by using a social selection function, so that an optimal dormant AP set and transmission power configuration rules under interference limitation are obtained, user traffic unloading and adjustment of AP transmission power are completed, and the honesty of AP participation in the game is ensured.
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:
s1, acquiring a wireless AP (access point) 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 subjected to grid discretization;
s2, setting the position coordinates x, y, z and the power p of the wireless AP as calculation variables;
s3, setting algorithm related parameters, including defining 4n fruit fly groups, and the size S of each fruit fly group p And maximum number of iterations I max (ii) a Wherein the content of the first and second substances,
the drosophila population is represented as follows:
Figure BDA0002526851210000031
the location information for each individual in the 4n population of fruit flies is given by the corresponding (X, Y) two-dimensional coordinates in equation (2):
Figure BDA0002526851210000032
wherein subscript letter f represents the variables introduced in the drosophila optimization;
the drosophila population position is initialized and calculated from the following equations (3), (4) and (5):
Figure BDA0002526851210000033
Figure BDA0002526851210000034
Figure BDA0002526851210000035
wherein p is max Is the maximum value of the AP transmitting power p; p is a radical of min Is the minimum value of the AP transmitting power p; rand () is the generation of a range of 0,1]A function of the random number above;
flight speed at initialization of Drosophila search:
Figure BDA0002526851210000036
s4, firstly, calculating the distance from the individual fruit fly j to the origin when the kth iteration is carried out:
Figure BDA0002526851210000037
wherein the positions of all drosophila individuals in the jth population at the kth iteration are calculated from equation (8):
Figure BDA0002526851210000041
then calculating the corresponding taste concentration judgment value:
Figure BDA0002526851210000042
and a step S5 of substituting the obtained taste concentration judgment value into an objective function (10) to calculate the fitness of each line:
Figure BDA0002526851210000043
wherein eta l For the penalty function under the condition of not meeting the constraint condition, the constraint optimization problem is expressed as
Figure BDA0002526851210000044
C ∑ Coverage of the target point for the wireless AP, and
Figure BDA0002526851210000045
c(AP i ,L j ) For the ith wireless access point AP i For the jth target test point L j And->
Figure BDA0002526851210000046
Beta is the signal attenuation in the propagation path, and
Figure BDA0002526851210000047
wherein it is present>
Figure BDA0002526851210000048
Is AP i Distance 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; d 0 Is a reference distance; α is a reference distance d 0 The power of (c); beta is a s Is 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;
s6, respectively finding and recording the fruit flies with the optimal taste concentration values in the population by using a formula (11) according to the fitness of the solved function
Figure BDA0002526851210000051
And its corresponding position->
Figure BDA0002526851210000052
And the Drosophila with the worst taste concentration value in the population
Figure BDA0002526851210000053
And its corresponding position->
Figure BDA0002526851210000054
/>
Figure BDA0002526851210000055
S7, calculating the fruit flies F in each row in the population respectively l Fitness and optimal fruit fly
Figure BDA0002526851210000056
Taste concentration difference between->
Figure BDA0002526851210000057
And the worst fruit fly>
Figure BDA0002526851210000058
Taste concentration difference between->
Figure BDA0002526851210000059
Figure BDA00025268512100000510
Step S8, if
Figure BDA00025268512100000511
Then the first fruit fly is divided into the worst fruit fly and/or the worst fruit fly>
Figure BDA00025268512100000512
In the poor subgroup as the center, then go to step S9; otherwise, dividing the fruit fly in the l row to be in the optimum fruit fly->
Figure BDA00025268512100000513
In the group of the better group as the center, then go to step S10;
and S9, flying the fruit flies in the poor subgroups to the optimal fruit fly positions by using vision, 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).
Figure BDA00025268512100000514
Figure BDA00025268512100000515
Where w is a non-negative inertial weight that decreases with increasing number of iterations, and is calculated using equation (15):
Figure BDA00025268512100000516
w max is the maximum inertial weight, w min For minimum inertial weight, k is the current number of iterations, I max The total number of iterations of the algorithm; c. C 1 ,c 2 Adjusting the maximum step length of learning for the learning factor of the particle; r is 1 ,r 2 Is 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;
s10, enabling the fruit fly individuals in the better population group to fly around global optimal information in a Levy mode, and in the kth iteration (k > 0), updating the position of the l fruit fly of the jth population as shown in a formula (16):
Figure BDA0002526851210000061
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 (ii) ofS11, entering iteration optimization, and repeatedly executing the step S4 to the step S10 until the iteration number reaches the maximum iteration number I max Or exit iteration after other conditions are met;
and S12, outputting the drosophila information with the global optimal taste concentration, namely obtaining the minimum total power and the 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.
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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 a limited AP deployed on the top of a floor in fig. 3, and the three-dimensional optimization deployment method for a wireless local area network, which is provided by the embodiment of the present invention and integrates multi-swarm optimization algorithms, 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:
the method comprises the following steps of S1, obtaining 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;
s2, setting the position coordinates x, y, z and the power p of the wireless AP as calculation variables;
s3, setting algorithm related parameters, including defining 4n fruit fly groups, and the size S of each fruit fly group p And maximum number of iterations I max (ii) a Wherein the content of the first and second substances,
the drosophila population is represented as follows:
Figure BDA0002526851210000071
the location information for each individual in the 4n population of fruit flies is given by the corresponding (X, Y) two-dimensional coordinates in equation (2):
Figure BDA0002526851210000072
wherein the subscript letter f represents the variables introduced in the drosophila optimization;
the drosophila population position is initialized and calculated from the following equations (3), (4) and (5):
Figure BDA0002526851210000073
Figure BDA0002526851210000081
Figure BDA0002526851210000082
wherein p is max Is the maximum value of the AP transmitting power p; p is a radical of min Is the minimum value of the AP transmitting power p; rand () is the generation of a range of 0,1]A function of the random number above;
flight speed at initialization of Drosophila search:
Figure BDA0002526851210000083
s4, firstly, calculating the distance from the individual fruit fly j to the origin when the kth iteration is carried out:
Figure BDA0002526851210000084
wherein the positions of all drosophila individuals in the jth population at the kth iteration are calculated from equation (8):
Figure BDA0002526851210000085
then calculating the corresponding taste concentration judgment value:
Figure BDA0002526851210000086
and a step (S5) for substituting the obtained taste concentration judgment value into an objective function (10) and calculating the fitness of each line:
Figure BDA0002526851210000087
wherein eta is l For the penalty function under the condition of not satisfying the constraint condition, the constraint optimization problem is expressed as
Figure BDA0002526851210000088
C ∑ Coverage of the target point for the wireless AP, and
Figure BDA0002526851210000091
c(APi,L j ) For the ith wireless access point AP i For jth target test point L j The probability of coverage of (a) is, and->
Figure BDA0002526851210000092
Beta is the signal attenuation in the propagation path, and
Figure BDA0002526851210000093
wherein +>
Figure BDA0002526851210000094
Is AP i Distance 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; d 0 Is a reference distance; α is a reference distance d 0 The power of (c); beta is a s Is 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;
s6, respectively finding and recording the fruit flies with the optimal taste concentration values in the population by using a formula (11) according to the fitness of the solved function
Figure BDA0002526851210000095
And its corresponding position>
Figure BDA0002526851210000096
And the Drosophila with the worst taste concentration value in the population
Figure BDA0002526851210000097
And its corresponding position->
Figure BDA0002526851210000098
Figure BDA0002526851210000099
S7, respectively calculating the fruit fly F of each row in the population l Fitness and optimal fruit fly
Figure BDA00025268512100000910
The taste concentration difference between->
Figure BDA00025268512100000911
And the worst fruit fly>
Figure BDA00025268512100000912
The taste concentration difference between->
Figure BDA00025268512100000913
Figure BDA00025268512100000914
Step S8, if
Figure BDA00025268512100000915
Then the first fruit fly is divided into the worst fruit fly and/or the worst fruit fly>
Figure BDA00025268512100000916
In the poor subgroup as the center, then go to step S9; otherwise, dividing the fruit fly in the l row to be in the optimum fruit fly->
Figure BDA00025268512100000917
In the group of the better group as the center, then go to step S10;
and S9, flying the fruit flies in the poor subgroups to the optimal fruit fly positions by using vision, 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).
Figure BDA0002526851210000101
/>
Figure BDA0002526851210000102
Where w is a non-negative inertial weight that decreases with increasing number of iterations, and is calculated using equation (15):
Figure BDA0002526851210000103
w max is the maximum inertial weight, w min For minimum inertial weight, k is the current iteration number, I max The total number of iterations of the algorithm; c. C 1 ,c 2 Adjusting the maximum step length of learning for the learning factor of the particle; r is 1 ,r 2 Is the interval [0,1]A random number of (c); x is a radical of a fluorine atom 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;
s10, enabling the fruit fly individuals in the better population group to fly around global optimal information in a Levy mode, and in the kth iteration (k > 0), updating the position of the l fruit fly of the jth population as shown in a formula (16):
Figure BDA0002526851210000104
wherein a is a parameter for controlling the step length of the individual fruit flies; levy (λ) is a function that yields a Levy flight distance;
step S11, enter intoIterative optimization is carried out, and the steps S4 to S10 are repeatedly executed until the iteration number reaches the maximum iteration number I max Or exit iteration after other conditions are met;
and S12, outputting 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., the three-dimensional space of 100m × 10 m) (e.g., the three-dimensional space is discretized into 100 meshes, and the center of the 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 the power p of the AP are set as calculation variables, and for convenience of calculation, a ratio method is used to unify the data variables. 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 drosophila population, population position, and iteration number are initialized.
In step S4, a drosophila olfactory search process is performed, and when each drosophila in the population searches by its olfactory sense, 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 the signal coverage and the signal attenuation in the propagation path is set, and the drosophila with better fitness is solved as the drosophila currently searched.
In step S6, the drosophila with the best taste concentration value and its corresponding location are found, as well as the drosophila with the worst taste concentration value and its corresponding location.
In step S7, each row of fruit flies F is calculated l And the difference in taste concentration between the fitness of (a) and the drosophila having the best value of taste concentration, and per row of drosophila F l And the difference in taste concentration between the drosophila having the worst value of taste concentration.
In step S8, the above two taste concentration differences are compared, and if the former is larger than the latter, the first fruit fly is divided into the worst fruit flies
Figure BDA0002526851210000111
After the poor subgroup as the center is selected, the step S9 is performed; if the former is less than or equal to the latter, dividing the fruit fly in the l row into the optimum fruit fly->
Figure BDA0002526851210000112
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, search according to the particle swarm search strategy under the guidance of the optimal individual, and update the flight speed and position.
In step S10, the individual drosophila in the better group fly Levy around the global optimal information and update the location.
In step S11, iteration optimization is carried out, and steps S4 to S10 are repeatedly executed until the iteration number reaches the maximum iteration number I max Or if other conditions are met, exit the iteration.
In step S12, the drosophila information with the global optimal flavor concentration, which is output in the last iteration of step S11, is output, that is, the minimum total power and corresponding position of the wireless AP 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 FOA algorithm as a main body and is applied to fruit fly groupsIn the iterative process, the distance Dist between the fruit fly individual i in the population and the optimal individual and the worst individual in the contemporary population are respectively calculated i_best And Dist i_worst If Dist i_best ＞Dist i_worst The individual drosophila i is divided into a worse subgroup centered on the worst individual, otherwise it is divided into a better subgroup centered on the best individual (the better and worse subgroups are re-divided at each iteration, both 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 deployment schematic diagram of the AP deployed on the top of the 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 the multi-swarm optimization algorithm, 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:
s1, acquiring a wireless AP (access point) 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 subjected to grid discretization;
s2, setting the position coordinates x, y, z and the power p of the wireless AP as calculation variables;
s3, setting algorithm related parameters, including defining 4n fruit fly groups, and the size S of each fruit fly group p And maximum number of iterations I max (ii) a Wherein the content of the first and second substances,
the drosophila population is represented as follows:
Figure FDA0002526851200000011
the location information for each individual in the 4n populations of drosophila is given by the corresponding (X, Y) two-dimensional coordinates in equation (2):
Figure FDA0002526851200000012
wherein the subscript letter f represents the variables introduced in the drosophila optimization;
the drosophila population position is initialized and calculated from the following equations (3), (4) and (5):
Figure FDA0002526851200000013
Figure FDA0002526851200000014
Figure FDA0002526851200000015
wherein p is max Is the maximum value of the AP transmitting power p; p is a radical of min Is the minimum value of the AP transmitting power p; rand () is the generation of a range of 0,1]A function of the random number above;
flight speed at the time of initializing fruit fly search:
Figure FDA0002526851200000021
s4, firstly, calculating the distance from the individual fruit fly j to the origin when the kth iteration is carried out:
Figure FDA0002526851200000022
wherein the positions of all drosophila individuals in the jth population at the kth iteration are calculated from equation (8):
Figure FDA0002526851200000023
then calculating a corresponding taste concentration judgment value:
Figure FDA0002526851200000024
and a step S5 of substituting the obtained taste concentration judgment value into an objective function (10) to calculate the fitness of each line:
Figure FDA0002526851200000025
wherein eta l For the penalty function under the condition of not satisfying the constraint condition, the constraint optimization problem is expressed as
Figure FDA0002526851200000026
C ∑ Coverage of the target point for the wireless AP, and +>
Figure FDA0002526851200000027
c(AP i ,L j ) For the ith wireless access point AP i For jth target test point L j And->
Figure FDA0002526851200000028
Beta is the signal attenuation in the propagation path and->
Figure FDA0002526851200000029
Wherein it is present>
Figure FDA00025268512000000210
Is AP i Distance 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; d 0 Is a reference distance; α is a reference distance d 0 The power of (d); beta is a beta s Is 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;
s6, respectively finding and recording the fruit flies with the optimal taste concentration value in the population by using a formula (11) according to the fitness of the solved function
Figure FDA0002526851200000031
And its corresponding position->
Figure FDA0002526851200000032
And the fruit flies in the population having the worst taste concentration value>
Figure FDA0002526851200000033
And its corresponding position->
Figure FDA0002526851200000034
Figure FDA0002526851200000035
S7, respectively calculating the fruit fly F of each row in the population l Fitness and optimal fruit fly
Figure FDA0002526851200000036
The taste concentration difference between->
Figure FDA0002526851200000037
And the worst fruit fly>
Figure FDA0002526851200000038
The taste concentration difference between->
Figure FDA0002526851200000039
/>
Figure FDA00025268512000000310
Step S8, if
Figure FDA00025268512000000311
Then the first fruit fly is divided into the worst fruit fly and/or the worst fruit fly>
Figure FDA00025268512000000312
In the poor subgroup as the center, then go to step S9; otherwise, dividing the fruit fly in the l row to be in the optimum fruit fly->
Figure FDA00025268512000000313
In the group of the better group as the center, then go to step S10;
s9, flying the fruit flies in the poor subgroup to the optimal fruit fly position by using vision, searching under the guidance of the optimal individual according to a particle swarm searching strategy, in the kth iteration (k > 0), firstly updating the flight speed of the l fruit flies in the jth subgroup according to a formula (13), and then updating the position of the l fruit flies according to a formula (14):
Figure FDA00025268512000000314
Figure FDA00025268512000000315
where w is a non-negative inertial weight that decreases with increasing number of iterations, and is calculated using equation (15):
Figure FDA0002526851200000041
w max is the maximum inertial weight, w min For minimum inertial weight, k is the current iteration number, I max The total number of iterations of the algorithm; c. C 1 ,c 2 Adjusting the maximum step length of learning for the learning factor of the particle; r is 1 ,r 2 Is 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;
s10, enabling the fruit fly individuals in the better population group to fly around global optimal information in a Levy mode, and in the kth iteration (k > 0), updating the position of the l fruit fly of the jth population as shown in a formula (16):
Figure FDA0002526851200000042
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 step S4 to the step S10 until the iteration number reaches the maximum iteration number I max Or the iteration is carried out after other conditions are met;
and S12, outputting drosophila information with global optimal taste concentration, namely obtaining the minimum total power and corresponding position of the wireless AP.
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