CN109862573A - A kind of LTE mixed networking self planning method based on multi-objective particle swarm - Google Patents
A kind of LTE mixed networking self planning method based on multi-objective particle swarm Download PDFInfo
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Abstract
A kind of LTE mixed networking self planning method based on multi-objective particle swarm is claimed in the present invention.The business information of target area user is obtained first；Secondly, rebuilding multiple objective optimization functions in conjunction with the characteristics of the passback of LTE mixed networking ideal；Then user service information is input in multi-goal optimizing function, using improved discrete multi-objective particle swarm algorithm, concentrates fuzzy compromise to choose more excellent solution from Pareto solution to optimize the model；Finally obtain the base station selection coordinate of LTE mixed networking.The present invention improves the planning efficiency of LTE mixed networking.
Description
Technical field
LTE mixing group the invention belongs to communicate LTE mixed networking technical field, more particularly to based on multi-objective particle swarm
Net self planning method.
Background technique
With the continuous development of mobile communications network, frequency spectrum resource is more and more rare, and single system base station independence networking is controlled
In capacity limit, network demand can not be adapted to, and operator is to meet the throughput demand gradually increased, reasonable utilization money
Source, in each department iterative method LTE mixed networking.LTE mixed networking be existing high frequency TDD LTE is backsetted with frequency it is low
Frequency FDD LTE is deployed to somewhere simultaneously, and the base station of alien frequencies greatly reduces the interference between same layer by mixed networking, together
When can play the advantage of each system to greatest extent, complementation is covered the shortage, and is reduced cost of investment, is provided high quality net for user
Network service.More and more attention has been paid at present become increasingly its mixed networking base station selection self planning demand LTE mixed networking
It is high.
Base station selection plans the important parameter as base station deployment, has strong influence to the covering and capacity of network.
Mobile communications network complexity is higher and higher now, multi-standard base station and deposits, the mode of artificial siteselecting planning under many scenes
It is difficult to find an optimal solution, while operator requires reduced cost again, improves planning efficiency, traditional people with drive test information
Work network planning mode is difficult to adapt to demand, therefore communication base station addressing self planning is particularly important.Existing network is advised certainly
The target that the method for drawing often considers is not complete, and modified hydrothermal process is excessively complicated, and practical value is not high, can not be suitable for LTE and mix
Group network self planning off the net.LTE mixed networking base station self planning is different from traditional base station networking self planning, planning FDD and
Multiple targets need to be considered when the base station TDD simultaneously, need to develop LTE mixed networking base station self planning scheme at present, are operator
Efficient base station deployment scheme is provided.
Summary of the invention
Present invention seek to address that the above problem of the prior art.Propose it is a kind of can reduce cost, and LTE network can be improved
The LTE mixed networking self planning method based on multi-objective particle swarm of performance.Technical scheme is as follows:
A kind of LTE mixed networking self planning method based on multi-objective particle swarm comprising following steps:
Step 1: the business information of target area user is obtained, the service distribution of target area is obtained；
Step 2: in conjunction with the characteristics of the passback of LTE mixed networking ideal, i.e. FDD is extensively covered as macro base station, main provide,
TDD is as small base station deployment, the characteristics of main absorptive capacity, rebuilds and maximizes coverage rate, maximum network Energy Efficiency Ratio, maximum
Objective optimization function including network load and minimum cost；
Step 3: solving the discrete multi-objective particle swarm algorithm for concentrating crowding distance to sort using Pareto is improved, from
Pareto solution concentrates fuzzy compromise to choose more excellent solution to optimize the model, finally obtains the base station selection coordinate of LTE mixed networking.
Further, the step 1: obtaining the business information of target area user, obtains the business point of target area
Cloth specifically includes:
Firstly, target network P is meshed into a N number of pixel, is predicted according to business demand, N number of test point is divided again
For common test point N1A and hot spot region test point N2A, any one point on P can be with cartesian coordinate within a grid
Calibration, any one point are expressed as ri, coordinate is (xi, yi)。
Further, the characteristics of step 2 combination LTE mixed networking ideal returns feature, reconstructs multiple-objection optimization mould
Type specifically includes:
K layer network is disposed first in M candidate subset, a total of 2 layer network is TDD network and FDD network, k respectively
The network number of plies in total is indicated equal to 2, wherein there are two types of selection, a for each base stationkmIndicate the position kth layer m base station deployment feelings
Condition, " 1 " indicate that k layers of base station of construction on the position m, " 0 " indicate not building k layers of base station on the position m, and K × M indicates addressing space of matrices
Size,
The base station selection matrix of mixed networking can be obtained:
The characteristics of based on LTE mixed networking dual link, i.e., test point can connect the base station TDD and FDD simultaneously, and only consideration is
The no service rate for meeting test point obtains base station access and instigates function and signal-to-noise ratio is；
And Rk,n,m=Bk,n×log(1+SINRk,n,m)
Wherein Δk,n,mThe case where indicating the position kth layer m coverage test point n, Rk,n,mIndicate that test point n receives the k layers of position m
Locate the attainable service rate of base station institute, and the service rate of hot spot measuring point requires high, R than common test pointmin,nIt indicates to meet
The minimum-rate of test point n access demand, " 1 " indicate that test point n is covered by the k layers of position m, at this time Rk,n,m≥Rmin,n, " 0 " table
Show and do not build k layers of base station on the position m, at this time Rk,n,m≤Rmin,n, Bk,nIt is the bandwidth of k layers of base station of test point n connection；
The final access base station selection square of test point is obtained according to above-mentioned base station selected matrix and test point access indicator function
Battle array H are as follows:
Wherein bnmIndicate that test point n by the position m base station coverage condition, is possible to have the base station TDD or FDD base on the position m
It stands, or does not build base station, test point n is by any one base station covering, then it represents that test point n is capped；
Four objects of planning of last LTE mixed networking are respectively as follows:
1) coverage rate is maximizedWherein N1Indicate common test point number, N2Hot spot test
Point number；
2) maximum network Energy Efficiency RatioWherein Pk,mIt is expressed as the transmission power of the k layers of base station m；
3) maximum network loadsWherein Pth,mIt is expressed as base station m
The load obstruction thresholding that should reach when deployment, for limiting base station access test point access quantity.Ψk,n,mIt indicates in the m of base station
Load capacity accounts for the percentage of base station demand load, when this value is more than thresholding P in Practical Projectth,mWhen, can with load limitation because
Plain exp (Pth,m-Ψk,n,m) come adjust reduce access base station m load capacity；
4) minimum costWherein CkFor the cost unit price of kth layer base station.
Further, in the step 3 using improved discrete multi-objective particle swarm algorithm, solve and concentrate from Pareto
Fuzzy compromise chooses more excellent solution to optimize the model, finally obtains the base station selection coordinate of LTE mixed networking, comprising: firstly, changing
Precession state crowding distanceJ is self planning target sum, fj(i+1) and fjIt (i-1) is grain
J-th of target value of the front and back particle of sub- i；fjmaxAnd fjminFor the maximum of j-th of objective function of all particles in external document
Value and minimum value；
The degree of crowding of individual with adjacent body in external archive is calculated, it is then intensive with being removed after the sequence of new crowding distance
Apart from the smallest solution, then the crowding measure of remaining Pareto solution, cycle calculations are calculated, until the number of residue Pareto solution is
It is expected that the outer capacity S of setting；Finally according to formulaThe standard subordinating degree function of particle i is calculated, wherein uij
Expression standard subordinating degree function；
In the iterative formula of discrete particle cluster, the iterative formula of speed and position is respectively as follows:
WithRespectively indicate speed and position of the particle i in the d dimension space in t+1 generation；WithIt is respectively
Individual extreme value and global extremum of the particle i in t generation；r1And r2It is a random number between 0 and 1；c1With c1It is Studying factors,
2 are usually taken simultaneously；ω is inertia weight, uses the inertia weight of adaptive transformation herein, and ω is indicated are as follows:T is the number of current iteration, tmaxIt is the largest the number of iterations, ωmaxAnd ωminIt is respectively
ω minimum and maximum inertia weight, usually takes ωmax=0.9, ωmin=0.4.
Further, in the step 3, improved discrete multi-objective particle swarm algorithm specifically calculates step and includes:
Step 1, input data, input candidate base station number, test point information and access rate, functional boundary, dimension；
Step 2, initialization particle populations: setting population number and maximum number of iterations generate 0 according to the constraint relationship at random
The initial position at momentWith the initial velocity at 0 momentCalculate the objective function of each particle, the local optimum of particle
Changing position initialization isExternal archive is sky, and setting boundary maximum crowding distance is d；
Step 3, initialization external archive: willIt is once added thereto and is retained domination solution, is expressed as in external archive
Initial solution；
Step 4, iteration start, t=1；According to above-mentioned formulaCalculate external shelves
The crowding of all individuals in case, and using previously described roulette method, therefrom select an individual as the overall situation most
Excellent solution
Step 5 recalculates a according to previously described population iterative formula, the position x and speed v of more new particle
The fitness of body；
Step 6 update external archive: by carry out location updating after particle sequentially add external archive and according to it is crowded away from
From dominance relation is judged, if the individual being newly added dominates the individual in external archive, the new individual is added and deletes domination
Body；If new individual does not dominate the individual in external archive, it is added without；If can not compare, compare current external capacity S' and
It is expected that the outer capacity S of setting, if S'≤S, external archive is added in new individual, and S adds 1, when the solution in external archive is greater than rule
Definite value carries out Noninferior Solution Set update using above-mentioned circulation delet method；
The P of step 7 more new particlebest.If meeting maximum number of iterations, stop search, it is defeated according to external elite disaggregation
Compromise solution is found using above-mentioned fuzzy Decision Making Method in the optimal forward position Pareto out, and otherwise t=t+1, goes to step 4.
Further, wheel disc bet method specifically includes in the step 4: its basic thought are as follows: what each individual was selected
Probability is directly proportional to its fitness function value size, if group size is N, individual xiFitness be f (xi), then individual xi's
The probability of selection are as follows:And P (x1)+P(x2)+…+P(xN)=1, then cumulative distribution
Probability are as follows:Concrete operation step: calculating the select probability and cumulative distribution probability of each individual according to above formula,
The random number r between one [0,1] is generated with rand (), if r≤q1, then individual x1It is selected.If qk-1< r < qk(2≤k≤
N), then individual xkIt is selected.
It advantages of the present invention and has the beneficial effect that:
The invention proposes a kind of LTE mixed networking self planning method, innovative point has the following；
1, LTE mixed networking Optimized model is established.The present invention is using coverage rate, load factor, Energy Efficiency Ratio, cost as optimization aim
Structure is reconstructed LTE mixed networking layering multiple target base station planning model.The model comprehensively considers LTE mixing group in terms of four
The characteristic of net only takes into account the cost and coverage rate of networking, the present invention, which establishes model, to be had centainly compared to previous plan model
Superiority.
2, crowding distance sort method in multiple target grain algorithm is improved.Traditional crowded sequence is only to use particle and grain
Geometric distance sequence between son, will lead to the excellent particle of appropriateness value and comes back and be deleted.The present invention improves particle and gathers around
Distance-taxis method is squeezed, is sorted using the crowding distance of dynamic particle, is beneficial to outstanding particle and is selected in front.
In general the present invention fully takes into account the characteristic of LTE mixed networking, has established model more perfect, using changing
Into discrete multi-objective particle swarm algorithm, search the base station that coverage rate is high, at low cost, load is more, Energy Efficiency Ratio is small as far as possible
Addressing combination improves addressing efficiency, has certain values.
Detailed description of the invention
Fig. 1 is that the present invention provides LTE mixed networking self planning method flow of the preferred embodiment based on multi-objective particle swarm
Schematic diagram；
Fig. 2 is that the present invention provides preferred embodiment general frame figure
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, detailed
Carefully describe.Described embodiment is only a part of the embodiments of the present invention.
The technical solution that the present invention solves above-mentioned technical problem is:
It is as illustrated in fig. 1 and 2: traffic forecast being carried out to location first, obtains the capacity of hot spot region and normal areas
Situation, as soon as in addition resulting region is indicated with the central test point of region square, the covering and capacity situation of the test point
Indicate the covering and capacity situation of the region square；Secondly, being rebuild more in conjunction with the characteristics of the passback of LTE mixed networking ideal
A objective optimization function；Then user service information is input in multi-goal optimizing function, utilizes improved discrete multiple target
Particle swarm algorithm concentrates fuzzy compromise to choose more excellent solution to optimize the model from Pareto solution；Finally obtain LTE mixed networking
Base station selection coordinate.
Step 1: the business information of target area user is obtained, the service distribution of target area is obtained:
Firstly, target network P is meshed into a N number of pixel, predicted according to business demand, it can be by N number of test point
It is divided into common test point N again1A and hot spot region test point N2It is a.Any one point on P can be with cartesian coordinate in net
It is demarcated in lattice, any one point is expressed as ri, coordinate is (xi,yi)。
Step 2: in conjunction with the characteristics of the passback of LTE mixed networking ideal, multiple objective optimization functions are rebuild.
Firstly, disposing k layer network in M candidate subset, there are two types of selection, analyses to obtain mixed networking for each base station
Base station selection matrix:
The characteristics of based on LTE mixed networking dual link, test point can access any layer network, it is not necessary to access to test point
Base station number and type are limited, and only consider whether the service rate for meeting test point, and base station access can be obtained instigates function
For and signal-to-noise ratio.
And Rk,n,m=Bk,n×log(1+SINRk,n,m)
The final access base station selection square of test point is obtained according to above-mentioned base station selected matrix and test point access indicator function
Battle array H are as follows:
Four for finally obtaining LTE mixed networking return target target are as follows:
1) coverage rate is maximized
2) maximum network Energy Efficiency Ratio
3) maximum network loads
4) minimum cost
Finally obtain LTE mixed networking self planning model:
3, step 3: utilizing improved discrete multi-objective particle swarm algorithm, from Pareto solution concentrate fuzzy compromise choose compared with
Excellent solution optimizes the model, finally obtains the base station selection coordinate of LTE mixed networking.Further, described sharp in step 2
The characteristics of being returned with LTE mixed networking ideal rebuilds multiple objective optimization functions, specifically includes: solving from Pareto and concentrates mould
Paste compromise chooses more excellent solution to optimize the model, finally obtains the base station selection coordinate of LTE mixed networking.Firstly, improving dynamic
Crowding distanceThe degree of crowding of individual with adjacent body in external archive is calculated, so
The smallest solution of crowding measure is removed after the new crowding distance sequence of heel, then calculates the crowding measure of remaining Pareto solution, circulation
It calculates, until the number of residue Pareto solution is the outer capacity S of expected setting.Finally according to formulaIt calculates
The standard subordinating degree function of particle i.Solution procedure is as follows:
Step 1, input data, input candidate base station number, test point information and access rate, functional boundary, dimension；
Step 2, initialization particle populations: setting population number and maximum number of iterations generate 0 according to the constraint relationship at random
The initial position at momentWith the initial velocity at 0 momentCalculate the objective function of each particle, the local optimum of particle
Changing position initialization isExternal archive is sky, and setting boundary maximum crowding distance is d；
Step 3, initialization external archive: willIt is once added thereto and is retained domination solution, is expressed as in external archive
Initial solution；
Step 4, iteration start, t=1；According to above-mentioned formulaCalculate external shelves
The crowding of all individuals in case, and using previously described roulette method, therefrom select an individual as the overall situation most
Excellent solution
Step 5 recalculates a according to previously described population iterative formula, the position x and speed v of more new particle
The fitness of body；
Step 6 update external archive: by carry out location updating after particle sequentially add external archive and according to it is crowded away from
From dominance relation is judged, if the individual being newly added dominates the individual in external archive, the new individual is added and deletes domination
Body；If new individual does not dominate the individual in external archive, it is added without；If can not compare, compare current external capacity S' and
It is expected that the outer capacity S of setting, if S'≤S, external archive is added in new individual, and S adds 1, when the solution in external archive is greater than rule
Definite value carries out Noninferior Solution Set update using above-mentioned circulation delet method；
The P of step 7 more new particlebest.If meeting maximum number of iterations, stop search, it is defeated according to external elite disaggregation
Compromise solution is found using above-mentioned fuzzy Decision Making Method in the optimal forward position Pareto out, and otherwise t=t+1, goes to step 4.
The above embodiment is interpreted as being merely to illustrate the present invention rather than limit the scope of the invention.?
After the content for having read record of the invention, technical staff can be made various changes or modifications the present invention, these equivalent changes
Change and modification equally falls into the scope of the claims in the present invention.
Claims (6)
1. a kind of LTE mixed networking self planning method based on multi-objective particle swarm, which comprises the following steps:
Step 1: the business information of target area user is obtained, the service distribution of target area is obtained；
Step 2: in conjunction with the characteristics of the passback of LTE mixed networking ideal, i.e. FDD is as macro base station, and main to provide wide covering, TDD makees
For small base station deployment, the characteristics of main absorptive capacity, rebuilds and maximize coverage rate, maximum network Energy Efficiency Ratio, maximum network
Objective optimization function including load and minimum cost；
Step 3: it using the discrete multi-objective particle swarm algorithm for improving Pareto solution concentration crowding distance sequence, is solved from Pareto
It concentrates fuzzy compromise to choose more excellent solution to optimize the model, finally obtains the base station selection coordinate of LTE mixed networking.
2. a kind of LTE mixed networking self planning method based on multi-objective particle swarm according to claim 1, feature exist
In the step 1: the business information of target area user is obtained, the service distribution of target area is obtained, specifically includes:
Firstly, target network P is meshed into a N number of pixel, is predicted, N number of test point is divided into again general according to business demand
Logical test point N1A and hot spot region test point N2A, any one point on P can be marked within a grid with cartesian coordinate
Fixed, any one point is expressed as ri, coordinate is (xi, yi)。
3. a kind of LTE mixed networking self planning method based on multi-objective particle swarm according to claim 2, feature exist
The feature in, the step 2 combination LTE mixed networking ideal returns the characteristics of, reconstructs Model for Multi-Objective Optimization, specifically includes:
K layer network is disposed first in M candidate subset, a total of 2 layer network, is TDD network and FDD network respectively, and k is equal to
2 indicate the network number of plies in total, wherein there are two types of selection, a for each base stationkmIndicate the position kth layer m base station deployment situation, " 1 "
It indicates to build k layers of base station on the position m, " 0 " indicates not building k layers of base station on the position m, and K × M indicates addressing space of matrices size,
The base station selection matrix of mixed networking can be obtained:
The characteristics of based on LTE mixed networking dual link, i.e., test point can connect the base station TDD and FDD simultaneously, only consider whether full
The service rate of sufficient test point obtains base station access and instigates function and signal-to-noise ratio is；
And Rk,n,m=Bk,n×log(1+SINRk,n,m)
Wherein Δk,n,mThe case where indicating the position kth layer m coverage test point n, Rk,n,mIndicate that test point n receives base at the k layers of position m
It stands the attainable service rate of institute, and the service rate of hot spot measuring point requires high, R than common test pointmin,nIt indicates to meet test
The minimum-rate of point n access demand, " 1 " indicate that test point n is covered by the k layers of position m, at this time Rk,n,m≥Rmin,n, " 0 " indicates m
It sets and does not build k layers of base station, at this time Rk,n,m≤Rmin,n, Bk,nIt is the bandwidth of k layers of base station of test point n connection；
The final access base station selection matrix H of test point is obtained according to above-mentioned base station selected matrix and test point access indicator function
Are as follows:
Wherein bnmIndicate that test point n by the position m base station coverage condition, is possible to have the base station TDD or the base station FDD on the position m, or
Person does not build base station, and test point n is by any one base station covering, then it represents that test point n is capped；
Four objects of planning of last LTE mixed networking are respectively as follows:
1) coverage rate is maximizedWherein N1Indicate common test point number, N2Hot spot test point
Number；
2) maximum network Energy Efficiency RatioWherein Pk,mIt is expressed as the transmission power of the k layers of base station m；
3) maximum network loadsWherein Pth,mIt is expressed as base station m deployment
When should reach load obstruction thresholding, for limit base station access test point access quantity.Ψk,n,mIndicate the load in the m of base station
It measures and accounts for the percentage that base station demand loads, when this value is more than thresholding P in Practical Projectth,mWhen, load limiting factor exp can be used
(Pth,m-Ψk,n,m) come adjust reduce access base station m load capacity；
4) minimum costWherein CkFor the cost unit price of kth layer base station.
4. a kind of LTE mixed networking self planning method based on multi-objective particle swarm according to claim 3, feature exist
In, in the step 3 using improved discrete multi-objective particle swarm algorithm, from Pareto solution concentrate fuzzy compromise choose compared with
Excellent solution optimizes the model, finally obtains the base station selection coordinate of LTE mixed networking, comprising: firstly, improving dynamic crowding distanceJ is self planning target sum, fj(i+1) and fj(i-1) the front and back particle for being particle i
J-th of target value；fjmaxAnd fjminFor the maximum value and minimum value of j-th of objective function of all particles in external document；
The degree of crowding of individual with adjacent body in external archive is calculated, then with removing crowding measure after the sequence of new crowding distance
The smallest solution, then the crowding measure of remaining Pareto solution, cycle calculations are calculated, until the number of residue Pareto solution is to be expected
The outer capacity S of setting；Finally according to formulaThe standard subordinating degree function of particle i is calculated, wherein uijIt indicates
Standard subordinating degree function；
In the iterative formula of discrete particle cluster, the iterative formula of speed and position is respectively as follows:
WithRespectively indicate speed and position of the particle i in the d dimension space in t+1 generation；WithIt is particle i respectively
In the individual extreme value and global extremum in t generation；r1And r2It is a random number between 0 and 1；c1With c1It is Studying factors, it is usually same
When take 2；ω is inertia weight, uses the inertia weight of adaptive transformation herein, and ω is indicated are as follows:T is the number of current iteration, tmaxIt is the largest the number of iterations, ωmaxAnd ωminIt is respectively
ω minimum and maximum inertia weight, usually takes ωmax=0.9, ωmin=0.4.
5. a kind of LTE mixed networking self planning method based on multi-objective particle swarm according to claim 4, feature exist
In in the step 3, improved discrete multi-objective particle swarm algorithm specifically calculates step and includes:
Step 1, input data, input candidate base station number, test point information and access rate, functional boundary, dimension；
Step 2, initialization particle populations: setting population number and maximum number of iterations generated for 0 moment according to the constraint relationship at random
Initial positionWith the initial velocity at 0 momentCalculate the objective function of each particle, the suboptimization position of particle
It sets and is initialized asExternal archive is sky, and setting boundary maximum crowding distance is d；
Step 3, initialization external archive: willIt is once added thereto and is retained domination solution, is expressed as initial in external archive
Solution；
Step 4, iteration start, t=1；According to above-mentioned formulaIt calculates in external archive
The crowding of all individuals, and using the method for previously described roulette, therefrom select an individual as globally optimal solution
Step 5 recalculates individual according to previously described population iterative formula, the position x and speed v of more new particle
Fitness；
Step 6 updates external archive: the particle after progress location updating being sequentially added external archive and is sentenced according to crowding distance
Disconnected dominance relation is added the new individual and deletes domination individual if the individual being newly added dominates the individual in external archive；If
New individual does not dominate the individual in external archive, then is added without；If can not compare, compares current external capacity S' and expection is set
Fixed outer capacity S, if S'≤S, external archive is added in new individual, and S adds 1, when the solution in external archive is greater than specified value, is made
Noninferior Solution Set update is carried out with above-mentioned circulation delet method；
The P of step 7 more new particlebest.If meeting maximum number of iterations, stop search, is exported according to external elite disaggregation
Compromise solution is found using above-mentioned fuzzy Decision Making Method in the optimal forward position Pareto, and otherwise t=t+1, goes to step 4.
6. a kind of LTE mixed networking self planning method based on multi-objective particle swarm according to claim 5, feature exist
In wheel disc bet method specifically includes in the step 4: its basic thought are as follows: each individual selected probability and its fitness
Functional value size is directly proportional, if group size is N, individual xiFitness be f (xi), then individual xiSelection probability are as follows:And P (x1)+P(x2)+…+P(xN)=1, then cumulative distribution probability are as follows:Concrete operation step: the select probability and cumulative distribution probability of each individual are calculated according to above formula, uses rand
() generates the random number r between one [0,1], if r≤q1, then individual x1It is selected.If qk-1< r < qk(2≤k≤N), then
Individual xkIt is selected.
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| CN117119469A (en) * | 2023-10-18 | 2023-11-24 | 国网江苏省电力有限公司信息通信分公司 | Electric power wireless private network base station address selection method and device based on improved particle swarm optimization |
| CN117119469B (en) * | 2023-10-18 | 2024-01-05 | 国网江苏省电力有限公司信息通信分公司 | Electric power wireless private network base station address selection method and device based on improved particle swarm optimization |
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