first commit

dynamicDeploy/.idea/dynamicDeploy.iml

+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>
\ No newline at end of file

dynamicDeploy/.idea/inspectionProfiles/Project_Default.xml

+<component name="InspectionProjectProfileManager">
+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
+    <inspection_tool class="PyPep8NamingInspection" enabled="true" level="WEAK WARNING" enabled_by_default="true">
+      <option name="ignoredErrors">
+        <list>
+          <option value="N802" />
+          <option value="N806" />
+          <option value="N803" />
+        </list>
+      </option>
+    </inspection_tool>
+  </profile>
+</component>
\ No newline at end of file

dynamicDeploy/.idea/inspectionProfiles/profiles_settings.xml

+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>
\ No newline at end of file

dynamicDeploy/.idea/misc.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.6" project-jdk-type="Python SDK" />
+</project>
\ No newline at end of file

dynamicDeploy/.idea/modules.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/dynamicDeploy.iml" filepath="$PROJECT_DIR$/.idea/dynamicDeploy.iml" />
+    </modules>
+  </component>
+</project>
\ No newline at end of file

dynamicDeploy/.idea/workspace.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ChangeListManager">
+    <list default="true" id="d8fba307-6469-4459-843c-5fa614b7698e" name="Changes" comment="" />
+    <option name="SHOW_DIALOG" value="false" />
+    <option name="HIGHLIGHT_CONFLICTS" value="true" />
+    <option name="HIGHLIGHT_NON_ACTIVE_CHANGELIST" value="false" />
+    <option name="LAST_RESOLUTION" value="IGNORE" />
+  </component>
+  <component name="MarkdownSettingsMigration">
+    <option name="stateVersion" value="1" />
+  </component>
+  <component name="ProjectId" id="2YvsyaE8wVdE3AeXnQCHS55ts8U" />
+  <component name="ProjectViewState">
+    <option name="hideEmptyMiddlePackages" value="true" />
+    <option name="showLibraryContents" value="true" />
+  </component>
+  <component name="PropertiesComponent">
+    <property name="RunOnceActivity.OpenProjectViewOnStart" value="true" />
+    <property name="RunOnceActivity.ShowReadmeOnStart" value="true" />
+  </component>
+  <component name="SpellCheckerSettings" RuntimeDictionaries="0" Folders="0" CustomDictionaries="0" DefaultDictionary="application-level" UseSingleDictionary="true" transferred="true" />
+  <component name="TaskManager">
+    <task active="true" id="Default" summary="Default task">
+      <changelist id="d8fba307-6469-4459-843c-5fa614b7698e" name="Changes" comment="" />
+      <created>1701417643165</created>
+      <option name="number" value="Default" />
+      <option name="presentableId" value="Default" />
+      <updated>1701417643165</updated>
+    </task>
+    <servers />
+  </component>
+</project>
\ No newline at end of file

dynamicDeploy/DE.py

+import time
+
+import numpy as np
+import random
+
+import Users
+import constants as C
+import ObjectiveFuction as OF
+
+import matplotlib.pyplot as plt
+
+import tool
+
+
+class Population:
+    def __init__(self, pre_uavs,users,r_c,r_p,dim, factor, rounds, size, object_func,uav_num, CR=0.75):
+        self.min_range = C.min_range
+        self.max_range = C.max_range
+        self.min_height=C.min_height
+        self.max_height=C.max_height
+        self.users=users
+        self.pre_uavs=pre_uavs
+        self.r_c=r_c
+        self.r_p=r_p
+        self.dimension = dim
+        self.factor = factor
+        self.rounds = rounds
+        self.size = size
+        self.cur_round = 1
+        self.best_pos=tool.getUavInitialPos(uav_num, users)
+        self.best=0
+        self.best_index=0
+        self.CR = CR
+        self.uav_num=uav_num
+        self.get_object_function_value = object_func
+        # 初始化种群
+        self.individuality = self.init_population()
+        self.init_alloc()
+        # self.object_function_values= [self.get_object_function_value(self.pre_uavs,v,self.users,self.r_c,self.r_p) for v in self.individuality]
+        self.object_function_values = [self.get_object_function_value(self.pre_uavs,v,self.users,self.r_p,self.p_all,self.b_all,self.c_association,self.p_association)
+                                       for v in self.individuality]
+        self.mutant = None
+
+
+    def init_population(self):
+        X = []
+        # users=Users.generate(100)#用户位置
+        for i in range(self.size):
+            uavs = []
+            if i==0:
+                uavs = tool.getUavInitialPos(self.uav_num, users)
+            else:
+                for index in range(self.uav_num):
+                    x = np.random.uniform(self.min_range, self.max_range)
+                    y = np.random.uniform(self.min_range, self.max_range)
+                    z = np.random.uniform(self.min_height, self.max_height)
+                    uavs.append([x, y, z])
+
+            X.append(uavs)
+        X = np.array(X)
+        return X
+
+    def init_alloc(self):
+        c_association, p_association, p_all, b_all = OF.objective_resource(self.pre_uavs, self.users, self.r_c)
+        self.c_association = c_association
+        self.p_association = p_association
+        self.p_all = p_all
+        self.b_all = b_all
+
+    def init_r_c(self):
+        r_c={}
+        for i in range(len(users)):
+            r_c[i] = random.randint(1,4)
+        return r_c
+
+    def mutate(self):
+        self.mutant = []
+        for i in range(self.size):
+            r0, r1, r2 = 0, 0, 0
+            while r0 == r1 or r1 == r2 or r0 == r2 or r0 == i:
+                r0 = random.randint(0, self.size - 1)
+                r1 = random.randint(0, self.size - 1)
+                r2 = random.randint(0, self.size - 1)
+            tmp = self.best_pos + (self.individuality[r1] - self.individuality[r2]) * self.factor
+            for i in range(len(tmp)):
+                for j in range(0,2):
+                    if tmp[i][j] > self.max_range:
+                        tmp[i][j]= self.max_range
+                    elif tmp[i][j] < self.min_range:
+                        tmp[i][j]= self.min_range
+                if tmp[i][2] > self.max_height:
+                    tmp[i][2] = self.max_height
+                elif tmp[i][2] < self.min_height:
+                    tmp[i][2] = self.min_height
+            # for t in range(self.dimension):
+            #     if tmp[t] > self.max_range or tmp[t] < self.min_range:
+            #         tmp[t] = random.uniform(self.min_range, self.max_range)
+            self.mutant.append(tmp)
+
+    def crossover_and_select(self):
+        for i in range(self.size):
+            Jrand = random.randint(0, self.dimension)
+            for j in range(self.dimension):
+                if random.random() > self.CR and j != Jrand:
+                    self.mutant[i][j] = self.individuality[i][j]
+                # tmp = self.get_object_function_value(self.pre_uavs,self.mutant[i],self.users,self.r_c,self.r_p)
+                tmp = self.get_object_function_value(self.pre_uavs,self.mutant[i], self.users, self.r_p, self.p_all, self.b_all, self.c_association, self.p_association)
+                if tmp > self.object_function_values[i]:
+                    self.individuality[i] = self.mutant[i]
+                    self.object_function_values[i] = tmp
+
+    def print_best(self):
+        self.best= max(self.object_function_values)
+        self.best_index = self.object_function_values.index(self.best)
+        self.best_pos=self.individuality[self.best_index]
+        print("轮数：" + str(self.cur_round))
+        print("最佳个体：" + str(self.best_pos))
+        print("目标函数值：" + str(self.best))
+        return self.best[0]
+
+
+    def evolution(self):
+        start_time = time.time()
+        best=[]
+        while self.cur_round <= self.rounds:
+            self.mutate()
+            self.crossover_and_select()
+            m=self.print_best()
+            best.append(m)
+            self.cur_round = self.cur_round + 1
+        x=[i for i in range(self.rounds)]
+        print(best)
+        end_time = time.time()
+        run_time = end_time - start_time
+        print(run_time)
+        plt.plot(best, label='折线图')
+        plt.show()
+
+
+# 测试部分
+if __name__ == "__main__":
+
+    users = Users.getUsers("./data.csv")
+    r_c={}
+    r_p={}
+    r=1
+    for i in range(len(users)):
+        r_c[i] = 4
+    for i in range(len(users)):
+        r_p[i] = r
+        r += 1
+    pre_uavs = tool.getUavInitialPos(4, users)
+    p = Population(pre_uavs,users,r_c,r_p,dim=4, factor=0.8, rounds=100, size=30, object_func=OF.objective,uav_num=4,)
+    p.evolution()
+
+

dynamicDeploy/GDOP.py

+import numpy as np
+from numpy.linalg import LinAlgError
+from numpy import seterr
+seterr(all='raise')
+
+
+def get_toa_gdop(bs_list, xyz):
+    """
+    compute the gdop of the toa
+    :param bs_list: the base station coordinate
+    :param xyz: the location coordinate of the tag
+    :return: the gdop value
+    """
+    bs_n = len(bs_list)
+    if bs_n < 3:
+        return None
+    h1 = np.mat(np.zeros((bs_n, 3)))
+
+    for i in range(0, len(bs_list)):
+        ri = np.sqrt((bs_list[i][0] - xyz[0]) ** 2 +
+                     (bs_list[i][1] - xyz[1]) ** 2)
+        if ri == 0:
+            h1[i, 0] = 0
+            h1[i, 1] = 0
+            h1[i, 2] = 1
+            # h1[i, 3] = 1
+
+        else:
+            h1[i, 0] = (bs_list[i][0] - xyz[0]) / ri
+            h1[i, 1] = (bs_list[i][1] - xyz[1]) / ri
+            h1[i, 2] = 1
+            # h1[i, 3] = 1
+
+    m_tmp = h1.T * h1
+    gdop_value = None
+
+    try:
+        g = m_tmp.I
+        gdop_value = np.sqrt(g[0, 0] + g[1, 1])
+        # print(gdop_value)
+    finally:
+        return gdop_value
+
+
+# 计算GDOP 不考虑时钟误差版本即DOP
+def calculate_gdop(uavs, estimate_value):
+    """
+    # :param point: 待定位点真实坐标
+    :param uavs: 无人机坐标集
+    :param estimate_value: 解算出的坐标
+    :return: gdop
+    """
+    # estimate_value.append(0)
+    estimate_value=[estimate_value[0],estimate_value[1],0]
+    var1 = np.array(uavs) - estimate_value
+    # 计算距离列向量
+    d_estimate = (np.sum(var1**2, axis=1)**0.5).reshape(-1, 1)
+    # 计算visible MATRIX
+    H = var1 / d_estimate
+    GDOP = float('inf')
+    try:
+        GDOP = np.trace(np.linalg.inv(np.dot(np.transpose(H), H))) ** 0.5
+    except:
+        GDOP=float('inf')
+    finally:
+        return GDOP
+
+
+
+if __name__ == '__main__':
+    bs_list=[[1,1,1],[2,3,4],[6,6,5]]
+    bs_list2 = [[1, 3, 3], [3, 1, 3], [5, 4, 3]]
+    xyz=[3,3.167,0]
+    print(calculate_gdop(bs_list,xyz))
+    print(get_toa_gdop(bs_list2,xyz))
\ No newline at end of file

dynamicDeploy/HHO.py

+import random
+import numpy
+import math
+from solution import solution
+import time
+
+
+def HHO(objf, lb, ub, dim, SearchAgents_no, Max_iter):
+    # dim=30
+    # SearchAgents_no=50
+    # lb=-100
+    # ub=100
+    # Max_iter=500
+
+    # initialize the location and Energy of the rabbit
+    Rabbit_Location = numpy.zeros(dim)
+    Rabbit_Energy = float("inf")  # change this to -inf for maximization problems
+
+    # Initialize the locations of Harris' hawks
+    X = numpy.random.uniform(0, 1, (SearchAgents_no, dim)) * (ub - lb) + lb
+
+    # Initialize convergence
+    convergence_curve = numpy.zeros(Max_iter)
+
+    ############################
+    s = solution()
+
+    print("HHO is now tackling  \"" + objf.__name__ + "\"")
+
+    timerStart = time.time()
+    s.startTime = time.strftime("%Y-%m-%d-%H-%M-%S")
+    ############################
+
+    t = 0  # Loop counter
+
+    # Main loop
+    while t < Max_iter:
+        for i in range(0, SearchAgents_no):
+
+            # Check boundries
+
+            X[i, :] = numpy.clip(X[i, :], lb, ub)
+
+            # fitness of locations
+            fitness = objf(X[i, :])
+
+            # Update the location of Rabbit
+            if fitness < Rabbit_Energy:  # Change this to > for maximization problem
+                Rabbit_Energy = fitness
+                Rabbit_Location = X[i, :].copy()
+
+        E1 = 2 * (1 - (t / Max_iter))  # factor to show the decreaing energy of rabbit
+
+        # Update the location of Harris' hawks
+        for i in range(0, SearchAgents_no):
+
+            E0 = 2 * random.random() - 1;  # -1<E0<1
+            Escaping_Energy = E1 * (E0)  # escaping energy of rabbit Eq. (3) in the paper
+
+            # -------- Exploration phase Eq. (1) in paper -------------------
+
+            if abs(Escaping_Energy) >= 1:
+                # Harris' hawks perch randomly based on 2 strategy:
+                q = random.random()
+                rand_Hawk_index = math.floor(SearchAgents_no * random.random())
+                X_rand = X[rand_Hawk_index, :]
+                if q < 0.5:
+                    # perch based on other family members
+                    X[i, :] = X_rand - random.random() * abs(X_rand - 2 * random.random() * X[i, :])
+
+                elif q >= 0.5:
+                    # perch on a random tall tree (random site inside group's home range)
+                    X[i, :] = (Rabbit_Location - X.mean(0)) - random.random() * ((ub - lb) * random.random() + lb)
+
+            # -------- Exploitation phase -------------------
+            elif abs(Escaping_Energy) < 1:
+                # Attacking the rabbit using 4 strategies regarding the behavior of the rabbit
+
+                # phase 1: ----- surprise pounce (seven kills) ----------
+                # surprise pounce (seven kills): multiple, short rapid dives by different hawks
+
+                r = random.random()  # probablity of each event
+
+                if r >= 0.5 and abs(Escaping_Energy) < 0.5:  # Hard besiege Eq. (6) in paper
+                    X[i, :] = (Rabbit_Location) - Escaping_Energy * abs(Rabbit_Location - X[i, :])
+
+                if r >= 0.5 and abs(Escaping_Energy) >= 0.5:  # Soft besiege Eq. (4) in paper
+                    Jump_strength = 2 * (1 - random.random());  # random jump strength of the rabbit
+                    X[i, :] = (Rabbit_Location - X[i, :]) - Escaping_Energy * abs(
+                        Jump_strength * Rabbit_Location - X[i, :])
+
+                # phase 2: --------performing team rapid dives (leapfrog movements)----------
+
+                if r < 0.5 and abs(Escaping_Energy) >= 0.5:  # Soft besiege Eq. (10) in paper
+                    # rabbit try to escape by many zigzag deceptive motions
+                    Jump_strength = 2 * (1 - random.random())
+                    X1 = Rabbit_Location - Escaping_Energy * abs(Jump_strength * Rabbit_Location - X[i, :]);
+
+                    if objf(X1) < fitness:  # improved move?
+                        X[i, :] = X1.copy()
+                    else:  # hawks perform levy-based short rapid dives around the rabbit
+                        X2 = Rabbit_Location - Escaping_Energy * abs(
+                            Jump_strength * Rabbit_Location - X[i, :]) + numpy.multiply(numpy.random.randn(dim),
+                                                                                        Levy(dim))
+                        if objf(X2) < fitness:
+                            X[i, :] = X2.copy()
+                if r < 0.5 and abs(Escaping_Energy) < 0.5:  # Hard besiege Eq. (11) in paper
+                    Jump_strength = 2 * (1 - random.random())
+                    X1 = Rabbit_Location - Escaping_Energy * abs(Jump_strength * Rabbit_Location - X.mean(0))
+
+                    if objf(X1) < fitness:  # improved move?
+                        X[i, :] = X1.copy()
+                    else:  # Perform levy-based short rapid dives around the rabbit
+                        X2 = Rabbit_Location - Escaping_Energy * abs(
+                            Jump_strength * Rabbit_Location - X.mean(0)) + numpy.multiply(numpy.random.randn(dim),
+                                                                                          Levy(dim))
+                        if objf(X2) < fitness:
+                            X[i, :] = X2.copy()
+
+        convergence_curve[t] = Rabbit_Energy
+        if (t % 1 == 0):
+            print(['At iteration ' + str(t) + ' the best fitness is ' + str(Rabbit_Energy)])
+        t = t + 1
+
+    timerEnd = time.time()
+    s.endTime = time.strftime("%Y-%m-%d-%H-%M-%S")
+    s.executionTime = timerEnd - timerStart
+    s.convergence = convergence_curve
+    s.optimizer = "HHO"
+    s.objfname = objf.__name__
+    s.best = Rabbit_Energy
+    s.bestIndividual = Rabbit_Location
+
+    return s
+
+
+def Levy(dim):
+    beta = 1.5
+    sigma = (math.gamma(1 + beta) * math.sin(math.pi * beta / 2) / (
+                math.gamma((1 + beta) / 2) * beta * 2 ** ((beta - 1) / 2))) ** (1 / beta)
+    u = 0.01 * numpy.random.randn(dim) * sigma
+    v = numpy.random.randn(dim)
+    zz = numpy.power(numpy.absolute(v), (1 / beta))
+    step = numpy.divide(u, zz)
+    return step
\ No newline at end of file

dynamicDeploy/PSO_comapre.py

+#归一化pso
+import math
+import time
+from itertools import combinations
+import random
+
+import numpy as np
+from matplotlib import pyplot as plt
+
+import Users
+from pso import PSO
+from improvedPSO import CPSO
+import tool
+import GDOP
+import ObjectiveFuction as OF
+from static_pso import SPSO
+
+plt.style.use('seaborn-whitegrid')
+
+# 设置字体为 Times New Roman
+plt.rcParams['font.family'] = 'Times New Roman'
+
+def drawResultAndIter(resultAndIter):
+    f, ax = plt.subplots(1, 1)
+    plt.plot(resultAndIter)
+    ax.set_xlabel('iteration')
+    ax.set_ylabel('fitness')
+    plt.show()
+def draw_compare(resultArr):
+    labels=['Adapted w','Fixed w','Linear w']
+    markers=['o','s','^']
+    line=['-','--',':','-.']
+    for i in range(len(resultArr)):
+        plt.plot(resultArr[i], label=labels[i],linestyle=line[i])
+    # 设置图例
+    plt.legend()
+
+    # 设置标题和坐标轴标签
+    # plt.title('Multiple Lines on One Plot')
+    plt.xlabel('x')
+    plt.ylabel('y')
+
+    # 显示图形
+    plt.show()
+
+
+def draw_c1_c2():
+    c1=[]
+    c2=[]
+    for i in range(100):
+        c1.append(OF.dynamic_c1(i,100))
+        c2.append(OF.dynamic_c2(i,100))
+    c1=np.array(c1)
+    c2=np.array(c2)
+    plt.plot(c1, label="c1")
+    plt.plot(c2, label="c2")
+    plt.show()
+def run_fixed_compare(users, k):
+    pre_uavs=tool.getUavInitialPos(k,users)
+    r_c = {}
+    r_p={}
+    for i in range(len(users)):
+        r_c[i] = 4
+    r=1
+    for i in range(len(users)):
+        r_p[i] = r
+        r+=1
+    pso=PSO(pre_uavs,users,r_c,r_p,k,)
+    X, V = pso.initParticle(users)
+    # print(X)
+    global_pos, global_best, result = pso.pso(X, V, OF.fixed_w,OF.objective)
+    print(global_pos, global_best)
+    # pso迭代曲线
+    return global_pos, global_best, result
+
+def draw_w(maxIter):
+    w=[]
+    for i in range(maxIter):
+        w.append(OF.adapted_w3(0.4,0.9,i,maxIter,1,))
+    plt.plot(w)
+    plt.show()
+
+
+def run_linear_compare(users, k,num,t):
+    pre_uavs=tool.getUavInitialPos(k,users)
+    r_c = {}
+    r_p={}
+    for i in range(len(users)):
+        r_c[i] = 2
+    r=1
+    for i in range(len(users)):
+        r_p[i] = 2
+        r+=1
+    pso=PSO(pre_uavs,users,r_c,r_p,k,num,t)
+    X, V = pso.initParticle(users)
+    # print(X)
+    c_association, p_association, p_all, b_all = OF.objective_resource_u(pre_uavs, users, r_c)
+    global_pos, global_best, result = pso.pso(X, V, OF.linear_w,OF.objective)
+    pso.re_init(pre_uavs,users,r_c,r_p,c_association,p_association,p_all,b_all)
+    print(global_pos, global_best)
+    # pso迭代曲线
+    return global_pos, global_best, result
+
+def run(users, k,num):
+    pre_uavs=tool.getUavInitialPos(k,users)
+    r_c = {}
+    r_p={}
+    r = 1
+    for i in range(len(users)):
+        r_c[i] = 2
+    r = 1
+    for i in range(len(users)):
+        r_p[i] = 2
+        r+=1
+    pso=SPSO(pre_uavs,users,r_c,r_p,k,num)
+    pso.init_alloc()
+    X, V = pso.initParticle(users)
+    # print(X)
+    # global_pos, global_best, result = pso.pso(X, V, adapted_w3,OF.objective_func,dynamic_c1,dynamic_c2)
+    global_pos, global_best, result = pso.pso(X, V, OF.linear_w, OF.objective, )
+    print(global_pos, global_best)
+    # pso迭代曲线
+    return global_pos, global_best, result
+
+def run_(users, k,num):
+    pre_uavs=tool.getUavInitialPos(k,users)
+    r_c = {}
+    r_p={}
+    r = 1
+    for i in range(len(users)):
+        r_c[i] = 2
+    r = 1
+    for i in range(len(users)):
+        r_p[i] = 2
+        r+=1
+    pso=PSO(pre_uavs,users,r_c,r_p,k,num)
+    pso.init_alloc()
+    X, V = pso.initParticle(users)
+    # print(X)
+    # global_pos, global_best, result = pso.pso(X, V, adapted_w3,OF.objective_func,dynamic_c1,dynamic_c2)
+    global_pos, global_best, result = pso.pso(X, V, OF.linear_w, OF.objective_func, )
+    print(global_pos, global_best)
+    # pso迭代曲线
+    return global_pos, global_best, result
+
+def run_all(users,k):
+    resultArr=[]
+    global_pos1, global_best1, result1 = run(users, k)
+    global_pos2, global_best2, result2 = run_fixed_compare(users, k)
+    global_pos3, global_best3, result3 = run_linear_compare(users, k)
+    resultArr.append(result1)
+    resultArr.append(result2)
+    resultArr.append(result3)
+    draw_compare(resultArr)
+
+
+
+if __name__ == '__main__':
+
+    users = Users.getUsers("./data3.csv")
+    # global_pos, global_best,result=run(users,3)
+    # print(fitness(users,global_pos))
+    # run_all(users,4)
+    # draw_c1_c2()
+
+    # draw_data()
+    # draw_w(150)
+    p_num=70
+    # # global_pos, global_best, result = run_fixed_compare(users, 4)
+    start_time = time.time()
+    # global_pos, global_best, result = run_(users, 5,p_num)
+    end_time1 = time.time()
+    global_pos1, global_best1, result1 = run_linear_compare(users, 5,p_num,200)
+    end_time2 = time.time()
+    run_time=end_time1-start_time
+    run_time1 = end_time2 - end_time1
+    # run_time2=end_time2 - start_time-run_time1
+    print("程序运行时间为：", run_time1)
+    # print(global_best[0])
+    # print(list(result))
+    print(list(result1))
+    # # print("最优解",global_best[0],global_best1[0])
+    # drawResultAndIter(result1)
+    # plt.show()
+    # arr=[]
+    # arr_t=[]
+    # for k in range(30,110,20):
+    # # k=70
+    #     t=250
+    # # for t in range(100, 350, 50):
+    #     num = []
+    #     sum_time = []
+    #     for i in range(1):
+    #         start_time = time.time()
+    #         global_pos, global_best, result = run_linear_compare(users, 5,k,t)
+    #         num.append(global_best[0])
+    #         print(global_best)
+    #         end_time = time.time()
+    #         run_time = end_time - start_time
+    #         sum_time.append(run_time)
+    #         print("程序运行时间为：", run_time)
+    #         # drawResultAndIter(result)
+    #         # plt.show()
+    #     print(sum(num)/len(num))
+    #     print(sum(sum_time)/len(sum_time))
+    #     arr.append(sum(num)/len(num))
+    #     arr_t.append(sum(sum_time)/len(sum_time))
+    # print(arr)
+    # print(arr_t)
+    # num=[-9657.993479469107, -11023.13972392451, -10511.070624574759, -10724.3448906409, -10623.367312657523,
+    #  -9802.662654304893, -7603.911448332553, -9695.134404543633, -9575.730714137575, -10454.63434179702]
+    # print(sum(num)/10)
+
+
+

dynamicDeploy/Users.py

+import math
+import random
+
+import numpy as np
+import csv
+import matplotlib.pyplot as plt
+from sklearn.cluster import KMeans
+import pandas as pd
+
+
+# 随机生成U个用户的位置
+def generate_in_file(num,filename):
+    data = np.random.randint(1, 2000, (num, 2))
+    f = open(filename, 'w', encoding='utf-8', newline="")
+    #  2.基于文件对象构建csv写入对象
+    csv_write = csv.writer(f)
+    #  4.写入csv文件
+    for d in data:
+        csv_write.writerow(d)
+    return data
+
+def generate(num):
+    data = np.random.randint(1, 1000, (num, 2))
+    return data
+
+def generateNormalDistribution(num):
+    mu, sigma = 500, 170  # 有关于正态分布的值μ，σ
+    x = sigma * np.random.randn(1, num) + mu
+    x = ([i for item in x for i in item])  # 在这个循环中我们能把二维的数组转换成一维的数组，y2同理
+    y = sigma * np.random.randn(1, num) + mu
+    y = ([i for item in y for i in item])
+    data = np.zeros((num, 2))
+    for i in range(num):
+        data[i] = np.array([x[i], y[i]])
+    f = open('../data_normal.csv', 'w', encoding='utf-8', newline="")
+    csv_write = csv.writer(f)
+    for d in data:
+        csv_write.writerow(d)
+    print(data)
+    return data
+
+
+# 用户按集群分布
+def generateGroupDistribution(num):
+    centers = [[500, 500], [1600, 400], [700, 1200], [1400, 1500]]
+    sigma = 100  # 有关于正态分布的值σ
+    group_num = 4
+    x_ = []
+    y_ = []
+    for i in range(group_num):
+        number = int(num / group_num)
+        print(centers[i])
+        x = sigma * np.random.randn(1, number) + centers[i][0]
+        y = sigma * np.random.randn(1, number) + centers[i][1]
+        x_.extend([i for item in x for i in item])
+        y_.extend([i for item in y for i in item])
+    data = np.zeros((num, 2))
+    for i in range(num):
+        data[i] = np.array([x_[i], y_[i]])
+    f = open('../data_group.csv', 'w', encoding='utf-8', newline="")
+    csv_write = csv.writer(f)
+    for d in data:
+        csv_write.writerow(d)
+    print(data)
+    return data
+
+
+def generateNormal(mean, sigma):
+    x = sigma * np.random.randn(1) + mean
+    return x
+
+
+# 从csv文件读用户位置
+def getUsers(filename):
+    data = pd.read_csv(filename, header=None)
+    list = data.values.tolist()
+    array = np.array(list)
+    return array
+
+
+def getDistanceFromCenter(user, x, y):
+    return math.sqrt((user[0] - x) ** 2 + (user[1] - y) ** 2)
+
+
+def draw(data):
+    plt.figure(num=1, figsize=(10, 10))
+    x = data[:, 0]
+    y = data[:, 1]
+    plt.scatter(x, y)
+    plt.xlim(0, 2001)
+    plt.ylim(0, 2001)
+    ax = plt.gca()
+    ax.spines['left'].set_position(('data', 0))
+    ax.spines['bottom'].set_position(('data', 0))
+    # x1 = np.linspace(300, 700, 500)
+    # center1 = 500 + np.sqrt(200 ** 2 - (x1 - 500) ** 2)
+    # center2 = 500 - np.sqrt(200 ** 2 - (x1 - 500) ** 2)
+    # plt.plot(x1, center1,c='b')
+    # plt.plot(x1, center2,c='b')
+    # x2 = np.linspace(200, 800, 500)
+    # center21 = 500 + np.sqrt(300 ** 2 - (x2 - 500) ** 2)
+    # center22 = 500 - np.sqrt(300 ** 2 - (x2 - 500) ** 2)
+    # plt.plot(x2, center21, c='r')
+    # plt.plot(x2, center22, c='r')
+    # x3 = np.linspace(100, 900, 500)
+    # center31 = 500 + np.sqrt(400 ** 2 - (x3 - 500) ** 2)
+    # center32 = 500 - np.sqrt(400 ** 2 - (x3 - 500) ** 2)
+    # plt.plot(x3, center31, c='g')
+    # plt.plot(x3, center32, c='g')
+    # model = KMeans(n_clusters=5, random_state=0).fit(data)
+    # center = model.cluster_centers_
+    # for c in center:
+    #     plt.scatter(c[0], c[1],s=200,marker='p')
+    return plt
+
+
+if __name__ == '__main__':
+    # data=generate_in_file(30,"./data3.csv")
+    list=getUsers("./data3.csv")
+    # print(list)
+
+    # data=generateGroupDistribution(100)
+    # list = getUsers("../data_group.csv")
+    draw(list).show()
+

dynamicDeploy/constants.py

+f_c = 2 * 10 ** 9
+noise = -140 #森林噪声（dbm）
+N0=-117 #视距链路噪声（dbm）
+pmax=0.1
+B_max=2.5
+snr_thre=1
+R_min=0.3  #0.1  1.2才能传视频
+G_max=20
+min_range=0
+max_range=2000
+min_height=50
+max_height=150
+speed=40
+time_slot=10 #基本间隔
+run_time=5
+P0=580.65
+P1=790
+# P0=199.4
+# P1=88.65
+U=200
+vr=7.2
+A=0.79
+v=1#用户速度
+r_change_slot=60#60s
+
+
+import numpy as np
+
+# aircraft weight in Newton
+W = 100
+# air density
+rho = 1.225
+# blade radius in meter
+R = 0.5
+# blade angular velocity in rad/s
+omega = 400
+# Fuselage equivalent flat plate area in m^2
+S_FP = 0.0151
+# number of blades
+b = 4
+# blade or aerofoil chord length
+c = 0.0157
+# incremental correction factor to induced power
+k = 0.1
+# profile drag coefficient
+delta = 0.012
+
+# rotor disc area
+A = np.pi * R**2
+# tip speed of the rotor blade
+U_tip = omega * R
+# rotor solidity
+sigma = b * c / (np.pi * R)
+# fuselage drag ratio
+d_0 = S_FP / sigma / A
+# mean rotor induced velocity in hover
+v_0 = np.sqrt(W / (2 * rho * A))
+
+# hovering blade profile power
+# P_0 = (delta / 8) * rho * sigma * A * omega**3 * R**2
+P_0=580.65
+# hovering induced power
+P_i = (1 + k) * (W**(1.5)) / np.sqrt(2 * rho * A)
+
+print(P_0,P_i)
+# flight speed
+# V_kmh = np.linspace(1, 100, 20)
+# V = V_kmh / 3.6
+# V=V_kmh
+# propulsion power
+# P_origin = P_0 * (1 + (3 * V**2) / (U_tip**2)) + P_i * (np.sqrt(1+(V**4/(4*(v_0**4))))-V**2/(2*(v_0**2)))**0.5 + (0.5 * d_0 * rho * sigma * A * V**3)
+# print(P_origin)
\ No newline at end of file

dynamicDeploy/data.csv

+902,280
+652,886
+635,96
+946,288
+668,947
+835,969
+197,709
+295,717
+285,112
+988,589
+314,375
+763,913
+840,328
+383,610
+297,882

dynamicDeploy/data1.csv

+897,356
+978,371
+660,94

dynamicDeploy/data2.csv

+122,240
+712,790
+517,522
+664,35
+522,76
+37,154
+150,541
+51,54
+680,52
+146,251
+670,601
+89,395
+592,302
+179,181
+105,429
+399,625
+184,24
+116,72
+918,650
+564,280

dynamicDeploy/data3.csv

+1507,121
+1326,1837
+1283,544
+197,1742
+681,1491
+1530,1252
+703,1380
+335,1732
+107,1760
+832,658
+1031,1152
+34,1436
+172,1881
+871,162
+1042,801
+1107,1613
+764,1894
+1745,1696
+1009,1818
+1925,1669
+1768,1455
+1450,864
+310,1879
+934,691
+696,176
+1335,1028
+1590,157
+1408,1289
+1624,1750
+337,826

dynamicDeploy/data4.csv

+379,347
+553,1551
+662,1800
+1467,988
+1102,1009
+565,1128
+801,1014
+862,906
+953,1215
+1079,708
+1800,1236
+1270,1679
+1177,1737
+761,1091
+1184,541
+1643,310
+1400,792
+1966,637
+498,1651
+380,115
+1365,1508
+1060,264
+1933,316
+1200,619
+1675,1259
+228,1465
+574,715
+599,379
+5,1624
+775,1520
+1053,461
+1572,1038
+1571,151
+1714,103
+1311,1902
+997,1879
+1151,86
+1041,414
+1583,29
+1257,1490
+412,1404
+1404,976
+994,139
+1735,1601
+1258,916
+1695,328
+1520,922
+943,349
+648,1322
+1340,443

dynamicDeploy/dynamicProcess.py

+import math
+import random
+
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.animation import FuncAnimation
+import pandas as pd
+import constants as C
+import Users
+
+
+def gauss_markov_model(positions, step_size, max_range, ):
+    # 使用高斯-马尔科夫模型生成随机位置变化，考虑更长时间范围的随机性
+    delta = np.random.normal(0, step_size, size=positions.shape)
+    new_positions = positions + delta
+
+    # 限制位置在给定范围内
+    new_positions = np.clip(new_positions, 0, max_range)
+    # print(new_positions)
+
+    return new_positions
+
+def generate_users_pos(users,slot,num):
+    U=[]
+    U.append(users)
+    for i in range(num):
+        users=gauss_markov_model(users, slot*C.v, C.max_range, )
+        U.append(users)
+    print(U)
+    return U
+def generate_users_pos_in_file(users,slot,num):
+    U=[]
+    U.append(users)
+    for i in range(num):
+        # users=gauss_markov_model(users, slot*C.v, C.max_range, )
+        users = random_pos(users, slot * C.v, C.max_range)
+        U.append(users)
+    print(U)
+    U=np.array(U)
+    np.save('dynamic_users2.npy', U)
+    # 从文件加载NumPy数组
+    return U
+def get_users_pos_from_file():
+    U = np.load('dynamic_users2.npy')
+    # 打印加载的数组
+    # print("Loaded Data:")
+    # print(U)
+    # u1=U[2][1]
+    # u2=U[3][1]
+    # print(math.sqrt((u1[0]-u2[0])**2+(u1[1]-u2[1])*2))
+
+    return U
+
+def random_pos(positions, step_size, max_range,):
+    angles = np.random.uniform(0,2*np.pi, len(positions))
+    # angels=np.full((1, len(positions)), np.pi/4)
+    # Generate random distances within the outer circle
+    distances = step_size
+
+    # Convert polar coordinates to Cartesian coordinates
+    x = positions[:, 0] + distances * np.cos(angles)
+    y = positions[:, 1] + distances * np.sin(angles)
+    new_positions=[]
+    for i in range(len(x)):
+        new_positions.append(np.array([x[i],y[i]]))
+    new_positions=np.array(new_positions)
+    new_positions = np.clip(new_positions, 0, max_range)
+    print(new_positions)
+    return new_positions
+
+def random_r(users):
+    r_c = {}
+    r_p={}
+    for i in range(len(users)):
+        r_c[i] = random.randint(1,4)
+        r_p[i]=random.randint(1,4)
+    return r_c,r_p
+
+def generate_random_r_in_file(users,num,filename):
+    #80% 1,2 20% 3,4
+    a=random.random()
+    dic={}
+    length=len(users)
+    for i in range(length):
+        dic[str(i)]=[]
+    df=pd.DataFrame(dic)
+    for i in range(num):
+        r=[]
+        for j in range(length):
+            a=random.random()
+            if a>0.8:
+                b=random.random()
+                if b>=0.8:
+                    r.append(3)
+                else:
+                    r.append(4)
+            else:
+                b = random.random()
+                if b >= 0.7:
+                    r.append(1)
+                else:
+                    r.append(2)
+        # r=[random.randint(1,4) for i in range(length)]
+        df.loc[len(df)] = r  # 通过 loc 添加一行
+    # print(df)
+    # 将 DataFrame 存入 CSV 文件
+    df.to_csv(filename, index=False)
+
+def read_csv_to_df(file_path):
+    df = pd.read_csv(file_path)
+    # 选择 DataFrame 中的一行（例如，第一行）
+
+    # 打印结果
+    # print(row_dict)
+    return df
+
+def get_r_c_and_r_p(df_c,df_p,index):
+    selected_c = df_c.iloc[index]
+    selected_p = df_p.iloc[index]
+    # 将选定的行转换为字典
+    r_c = {int(key): value for key, value in selected_c.to_dict().items()}
+    r_p = {int(key): value for key, value in selected_p.to_dict().items()}
+    return r_c,r_p
+
+
+users=Users.getUsers("./data3.csv")
+# generate_users_pos(users,10,5)
+# generate_random_r_in_file(users,50,'r_p.csv')
+# generate_users_pos_in_file(users,10,30)
+U=get_users_pos_from_file()
+# print(len(U))
+# df_c=read_csv_to_df('r_c.csv')
+# df_p=read_csv_to_df('r_p.csv')
+# r_c,r_p=get_r_c_and_r_p(df_c,df_p,0)
+# print(r_c,r_p)
+# U=get_users_pos_from_file()
+def update(frame, user_positions, sc, step_size, max_range,):
+    user_positions = gauss_markov_model(user_positions, step_size, max_range, )
+    # user_positions=random_pos(user_positions, step_size, max_range,)
+
+    sc.set_offsets(user_positions)
+    return sc,
+
+
+def create_dynamic_plot(num_users, max_range, step_size,):
+    # 初始化用户位置
+    user_positions = np.random.uniform(0, max_range, size=(num_users, 2))
+
+    # 创建散点图
+    fig, ax = plt.subplots()
+    sc = ax.scatter(user_positions[:, 0], user_positions[:, 1])
+
+    # 设置图形的标题和坐标轴标签
+    ax.set_title('Dynamic Point Plot with Gauss-Markov Model')
+    ax.set_xlabel('X-axis')
+    ax.set_ylabel('Y-axis')
+
+    # 使用FuncAnimation实现动态效果
+    animation = FuncAnimation(fig, update, frames=300, fargs=(user_positions, sc, step_size, max_range,), interval=100)
+
+    # 返回FuncAnimation对象
+    return animation
+def draw_dynamic_users():
+    # 设置参数
+    num_users = 15
+    max_range = 1000
+    step_size = 10
+    persistence_factor = 1
+
+    # 创建动态点位图
+    dynamic_plot = create_dynamic_plot(num_users, max_range, step_size,)
+
+    # 显示动画
+    plt.show()
+
+# draw_dynamic_users()
\ No newline at end of file

dynamicDeploy/km.py

+import numpy as np
+import random
+import time
+
+
+zero_threshold = 0.00000001
+
+
+class KMNode(object):
+    def __init__(self, id, exception=0, match=None, visit=False):
+        self.id = id
+        self.exception = exception
+        self.match = match
+        self.visit = visit
+
+
+class KuhnMunkres(object):
+    def __init__(self):
+        self.matrix = None
+        self.x_nodes = []
+        self.y_nodes = []
+        self.minz = float('inf')
+        self.x_length = 0
+        self.y_length = 0
+        self.index_x = 0
+        self.index_y = 1
+
+    def __del__(self):
+        pass
+
+    def set_matrix(self, x_y_values):
+        xs = set()
+        ys = set()
+        for x, y, value in x_y_values:
+            xs.add(x)
+            ys.add(y)
+
+        #选取较小的作为x
+        if len(xs) < len(ys):
+            self.index_x = 0
+            self.index_y = 1
+        else:
+            self.index_x = 1
+            self.index_y = 0
+            xs, ys = ys, xs
+
+        x_dic = {x: i for i, x in enumerate(xs)}
+        y_dic = {y: j for j, y in enumerate(ys)}
+        self.x_nodes = [KMNode(x) for x in xs]
+        self.y_nodes = [KMNode(y) for y in ys]
+        self.x_length = len(xs)
+        self.y_length = len(ys)
+
+        self.matrix = np.zeros((self.x_length, self.y_length))
+        for row in x_y_values:
+            x = row[self.index_x]
+            y = row[self.index_y]
+            value = row[2]
+            x_index = x_dic[x]
+            y_index = y_dic[y]
+            self.matrix[x_index, y_index] = value
+
+        for i in range(self.x_length):
+            self.x_nodes[i].exception = max(self.matrix[i, :])
+
+
+    def km(self):
+        for i in range(self.x_length):
+            while True:
+                self.minz = float('inf')
+                self.set_false(self.x_nodes)
+                self.set_false(self.y_nodes)
+
+                if self.dfs(i):
+                    break
+
+                self.change_exception(self.x_nodes, -self.minz)
+                self.change_exception(self.y_nodes, self.minz)
+
+    def dfs(self, i):
+        x_node = self.x_nodes[i]
+        x_node.visit = True
+        for j in range(self.y_length):
+            y_node = self.y_nodes[j]
+            if not y_node.visit:
+                t = x_node.exception + y_node.exception - self.matrix[i][j]
+                if abs(t) < zero_threshold:
+                    y_node.visit = True
+                    if y_node.match is None or self.dfs(y_node.match):
+                        x_node.match = j
+                        y_node.match = i
+                        return True
+                else:
+                    if t >= zero_threshold:
+                        self.minz = min(self.minz, t)
+        return False
+
+    def set_false(self, nodes):
+        for node in nodes:
+            node.visit = False
+
+    def change_exception(self, nodes, change):
+        for node in nodes:
+            if node.visit:
+                node.exception += change
+
+    def get_connect_result(self):
+        ret = []
+        for i in range(self.x_length):
+            x_node = self.x_nodes[i]
+            j = x_node.match
+            y_node = self.y_nodes[j]
+            x_id = x_node.id
+            y_id = y_node.id
+            value = self.matrix[i][j]
+
+            if self.index_x == 1 and self.index_y == 0:
+                x_id, y_id = y_id, x_id
+            ret.append((x_id, y_id, value))
+
+        return ret
+
+    def get_max_value_result(self):
+        ret = 0
+        for i in range(self.x_length):
+            j = self.x_nodes[i].match
+            ret += self.matrix[i][j]
+
+        return ret
+
+
+def run_kuhn_munkres(x_y_values):
+    process = KuhnMunkres()
+    process.set_matrix(x_y_values)
+    process.km()
+    return process.get_connect_result()
+
+
+# def test():
+#     values = []
+#     random.seed(0)
+#     for i in range(500):
+#         for j in range(1000):
+#             value = random.random()
+#             values.append((i, j, value))
+#
+#     return run_kuhn_munkres(values)
+
+if __name__ == '__main__':
+    s_time = time.time()
+    # ret = test()
+    # print("time usage: %s " % str(time.time() - s_time))
+    values = [
+        (1, (1,1), 3),
+        (1, (3,3), 4),
+        (2, (1,1), 2),
+        (2, (2,2), 1),
+        (2, (3,3), 3),
+        (3, (2,2), 4),
+        (3, (3,3), 5)
+    ]
+
+    print(run_kuhn_munkres(values))
\ No newline at end of file

dynamicDeploy/pso.py

+import math
+import Users
+import numpy as np
+import random
+from pprint import pprint
+import constants as C
+import tool
+import ObjectiveFuction as OF
+
+class PSO:
+    def __init__(self,pre_uavs,users,r_c,r_p,k,N=70,maxgen=200,):
+        #之后解析JSON，从JSON中读参数
+        self.N=N
+        self.k=k
+        self.pre_uavs=pre_uavs
+        self.users=users
+        self.d = 3  # 可行解维数
+        self.limit = [C.min_range,C.max_range]  # 边界位置限制
+        self.vlimit = [-500, 500]  # 边界速度限制
+        self.vlimit_h=[-10,10]
+        self.r_c = r_c
+        self.r_p=r_p
+        # self.height = 100
+        self.min_height = C.min_height
+        self.max_height = C.max_height
+        self.wmax = 0.9  # 惯性权重
+        self.wmin = 0.4
+        self.c1 = 1.496  # 自我学习因子
+        self.c2 = 1.496  # 群体学习因子
+        # self.c1 = 0.5  # 自我学习因子
+        # self.c2 = 0.5  # 群体学习因子
+        self.maxgen = maxgen
+        # self.users = Users.getUsers("./data.csv")
+        self.users = users
+        self.init_alloc()
+
+
+    # #todo：防止无人机位置碰撞
+    # def punish_function(self,uavs):
+    #     # user_arr = [1, 2, 5]
+    #     punish=0
+    #     for i in range(len(uavs)):
+    #         for j in range(i+1,len(uavs)):
+    #             dis=self.cal_tool.getDistance(uavs[i],uavs[j])
+    #             if dis<200:
+    #                 punish+=100
+    #
+    #     return punish
+    def punish_f(self,uavs):
+        punish=0
+        max_dis=(C.time_slot-C.run_time)*C.speed
+        for i in range(len(uavs)):
+            dis=tool.calculate_3d_distance_uavs(uavs[i],self.pre_uavs[i])
+            if dis>max_dis:
+                punish=float('-inf')
+        return punish
+
+    def re_init(self, pre_uavs, users, r_c, r_p, c_association, p_association, p_all, b_all, ):
+        self.pre_uavs = pre_uavs
+        self.user = users
+        self.r_c = r_c
+        self.r_p = r_p
+        self.c_association = c_association
+        self.p_association = p_association
+        self.p_all = p_all
+        self.b_all = b_all
+
+    # def init_r_c(self):
+    #     r_c={}
+    #     for i in range(len(self.users)):
+    #         r_c[i] = random.randint(1,4)
+    #     return r_c
+    # 初始化粒子群，k是无人机数量
+    def initParticle(self, users):
+        X = []
+        # users=Users.generate(100)#用户位置
+        V = np.random.uniform(self.vlimit[0], self.vlimit[1], (self.N, self.k, 3))
+        V2 = np.random.uniform(self.vlimit_h[0], self.vlimit_h[1], (self.N, self.k, 3))
+        for i in range(self.N):
+            uavs=[]
+            if i==0:
+                uavs = tool.getUavInitialPos(self.k, users)
+            else:
+                for index in range(self.k):
+                    x=np.random.randint(self.limit[0],self.limit[1])
+                    y=np.random.randint(self.limit[0],self.limit[1])
+                    # z=np.random.randint(self.min_height,self.max_height)
+                    z=C.min_height
+                    uavs.append([x,y,z])
+
+
+            X.append(uavs)
+        X = np.array(X)
+        return X, V,
+
+    def init_alloc(self,):
+        c_association, p_association, p_all, b_all = OF.objective_resource_avg(self.pre_uavs, self.users, self.r_c)
+        self.c_association = c_association
+        self.p_association = p_association
+        self.p_all = p_all
+        self.b_all = b_all
+
+
+    # 初始化个体和全局最佳历史位置和最佳适应度
+    def initBest(self, X,):
+        p_pos = X.copy()  # 每个个体的历史最佳位置
+        global_pos = np.zeros((self.k, 3))  # 种群的历史最佳位置5*3
+        p_best = np.full((self.N, 1), float('-inf'))  # 每个个体的历史最佳适应度
+        global_best = float('-inf')  # 种群历史最佳适应度
+        return p_pos, global_pos, p_best, global_best
+
+
+    def pso(self, X, V, update_w,get_fitness,):
+        # X, V, users = initParticlePos()
+        p_pos, global_pos, p_best, global_best = self.initBest(X)
+        result = np.zeros(self.maxgen)
+
+        fitness = np.zeros(self.N)
+        for iter in range(self.maxgen):
+            # fitness = self.getfitness(X, users, k,P)  # 计算所有粒子适应度
+
+            for i in range(len(X)):
+                # punish = self.punish_f(X[i])
+                # if punish == float('-inf'):
+                #     fitness[i] = punish
+                # else:
+                f, _, _ = get_fitness(self.pre_uavs, X[i], self.users, self.r_c, self.r_p, self.p_all, self.b_all,
+                                      self.c_association, self.p_association)
+                fitness[i] = f
+                # f,_,_=get_fitness(self.pre_uavs,X[i],self.users,self.r_c,self.r_p)
+                # f,_,_=get_fitness(self.pre_uavs, X[i], self.users, self.r_c,self.r_p, self.p_all, self.b_all, self.c_association,
+                #             self.p_association)
+                # fitness[i]= f+self.punish_f(X[i])
+            for i in range(self.N):
+                if fitness[i] > p_best[i]:
+                    p_best[i] = fitness[i]  # 更新个体历史最佳适应度
+                    p_pos[i] = X[i].copy()  # 更新个体历史最佳位置(取值)
+            if max(p_best) > global_best:
+                global_best = max(p_best)  # 更新群体历史最佳适应度
+                max_index = p_best.argmax()
+                global_pos = X[max_index].copy()  # 更新群体历史最佳位置
+
+
+            # 权重更新
+            # w = self.wmax - iter * (self.wmax - self.wmin) / self.maxgen
+            # w = (self.wmax +self.wmin)/2
+            w=update_w(self.wmin,self.wmax,iter,self.maxgen,iter,fitness)
+            for i in range(self.N):
+                V[i] = V[i] * w + self.c1 * random.random() * (p_pos[i] - X[i]) + self.c2 * random.random() * (
+                        global_pos - X[i])
+                # V2[i] = V2[i] * w + self.c1 * random.random() * (p_pos[i] - X[i]) + self.c2 * random.random() * (
+                #         global_pos - X[i])
+                # print(V[i])
+                for j in range(len(V[i])):
+                    for p in range(len(V[i][j])):
+                        if V[i][j][p] > self.vlimit[1]:
+                            V[i][j][p] = self.vlimit[1]
+                            # V[i][j][p] = self.vlimit[0]+np.random.rand(1)*(self.vlimit[1]-self.vlimit[0])
+                        elif V[i][j][p] < self.vlimit[0]:
+                            V[i][j][p] = self.vlimit[0]
+                        # if V2[i][j][p] > self.vlimit_h[1]:
+                        #     V2[i][j][p] = self.vlimit_h[1]
+                        #     # V[i][j][p] = self.vlimit[0]+np.random.rand(1)*(self.vlimit[1]-self.vlimit[0])
+                        # elif V2[i][j][p] < self.vlimit_h[0]:
+                        #     V2[i][j][p] = self.vlimit_h[0]
+                # print(V[i])
+
+            # 位置更新
+            X = X + V
+            # 边界位置处理
+            for i in range(self.N):
+                for j in range(len(X[i])):
+                    for p in range(2):
+                        if X[i][j][p] > self.limit[1]:
+                            X[i][j][p] = self.limit[1]
+                        elif X[i][j][p] < self.limit[0]:
+                            X[i][j][p] = self.limit[0]
+                    X[i][j][2]=self.min_height
+                    # if X[i][j][2] > self.max_height:
+                    #     X[i][j][2] = self.max_height
+                    # elif X[i][j][2] < self.min_height:
+                    #     X[i][j][2] = self.min_height
+
+            result[iter] = global_best
+            print(iter,global_pos,global_best)
+
+        # print(global_best)
+        # print(global_pos)
+
+        return global_pos,global_best,result
+
+
+
+if __name__ == '__main__':
+    uavs=[[868.34535881,315.18340086,50.        ],
+          [292.96443022,729.7612657,77.75450997],
+ [732.25467955,927.62861821,50.        ],
+ [303.40107629,240.31208079,150.        ]]
+    # 示例
+    # print(0.012 / 8 * 1.225 * 0.05 * 0.79 * 400 ** 3 * 0.5 ** 3)
+    # print((1+0.1)*100**1.5/math.sqrt(2*1.225*0.79))
+    best=[[335,772,67], [382,739,75], [1758,1683,137], [885,762,133], [403,1196,94], [1049,358,88]]
+
+
+
+
+
+
+

dynamicDeploy/r_c.csv

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dynamicDeploy/r_p.csv

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+2.0,4.0,1.0,2.0,2.0,2.0,1.0,2.0,4.0,4.0,1.0,4.0,2.0,4.0,1.0,4.0,1.0,3.0,1.0,4.0,2.0,1.0,2.0,1.0,2.0,2.0,2.0,2.0,4.0,2.0
+1.0,2.0,2.0,3.0,2.0,2.0,2.0,2.0,2.0,4.0,2.0,2.0,1.0,2.0,4.0,1.0,4.0,1.0,4.0,2.0,4.0,1.0,2.0,2.0,2.0,2.0,2.0,4.0,2.0,2.0
+4.0,2.0,2.0,2.0,2.0,4.0,2.0,2.0,2.0,4.0,1.0,4.0,2.0,4.0,2.0,4.0,2.0,2.0,4.0,2.0,2.0,2.0,2.0,2.0,4.0,4.0,1.0,2.0,1.0,4.0
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+1.0,2.0,4.0,1.0,4.0,2.0,2.0,2.0,1.0,2.0,2.0,1.0,3.0,3.0,1.0,2.0,2.0,2.0,2.0,2.0,2.0,2.0,2.0,2.0,2.0,1.0,2.0,2.0,2.0,2.0
+1.0,1.0,4.0,2.0,4.0,4.0,4.0,2.0,2.0,4.0,2.0,4.0,4.0,2.0,1.0,1.0,2.0,2.0,1.0,1.0,1.0,1.0,2.0,1.0,2.0,1.0,4.0,2.0,1.0,2.0
+4.0,2.0,4.0,2.0,2.0,3.0,3.0,2.0,4.0,3.0,2.0,4.0,2.0,2.0,2.0,2.0,4.0,3.0,2.0,1.0,4.0,1.0,3.0,2.0,4.0,2.0,1.0,4.0,2.0,2.0

dynamicDeploy/report.log

+2023-12-08 19:08:42,025 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9, 'k': 3, 'p': 2}
+2023-12-08 19:08:42,055 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 16.89331204358824, best pos: [-0.20657694 -1.67142459]
+2023-12-08 19:12:57,642 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9, 'k': 3, 'p': 2}
+2023-12-08 19:12:57,668 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 31.816129723241335, best pos: [1.84784183 1.87663107]
+2023-12-08 19:13:47,923 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9, 'k': 3, 'p': 2}
+2023-12-08 19:13:47,950 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 56.773199869733325, best pos: [3.23664274 0.52956792]

dynamicDeploy/run2.py

+#归一化pso
+import math
+import time
+from itertools import combinations
+
+import numpy as np
+from matplotlib import pyplot as plt
+
+import Users
+from pso import PSO
+from improvedPSO import CPSO
+import tool
+import GDOP
+import ObjectiveFuction as OF
+import constants as C
+import dynamicProcess as dynamic
+
+plt.style.use('seaborn-whitegrid')
+
+# 设置字体为 Times New Roman
+plt.rcParams['font.family'] = 'Times New Roman'
+
+
+def ipso_convex(users,k,r_c,r_p):
+    # users= Users.getUsers("./data.csv")
+    uavs=tool.getUavInitialPos(k,users)
+    pre_uavs=uavs
+    # r_c,r_p=dynamic.random_r(users)
+    c_association, p_association, p_all, b_all = OF.objective_resource_avg(uavs, users, r_c)
+    # c_association, p_association, p_all, b_all = OF.objective_resource(uavs, users, r_c)
+    pso = CPSO(pre_uavs, users, r_c, r_p, k, )
+    pso.re_init(pre_uavs, users, r_c, r_p, c_association, p_association, p_all, b_all)
+    X, V = pso.initParticle(users)
+    global_pos, global_best, result = pso.pso(X, V, OF.linear_w, OF.objective, )
+    c_association, p_association, p_all, b_all = OF.objective_resource(global_pos, users, r_c)
+    f, e, rate, gdop = OF.real_objective(pre_uavs, global_pos, users, r_c,r_p, p_all, b_all, c_association,
+                                         p_association, C.time_slot)
+    print("real-f",f)
+    r,g=OF.cal_satisfaction(global_pos,users,p_all,b_all,r_c,r_p, c_association,p_association)
+    print("pso_convex",global_best,r,g)
+    if f>global_best[0]:
+        return f,r,g
+    return global_best[0],r,g
+
+def ipso_avg(users,k,r_c,r_p):
+    uavs = tool.getUavInitialPos(k, users)
+    pre_uavs = uavs
+    # r_c,r_p=dynamic.random_r(users)
+    c_association, p_association, p_all, b_all = OF.objective_resource_avg(uavs, users, r_c)
+    pso = CPSO(pre_uavs, users, r_c, r_p, k, )
+    pso.re_init(pre_uavs, users, r_c, r_p, c_association, p_association, p_all, b_all)
+    X, V = pso.initParticle(users)
+    global_pos, global_best, result = pso.pso(X, V, OF.linear_w, OF.objective, )
+    f, e, rate, gdop = OF.real_objective(pre_uavs, global_pos, users, r_c, r_p, p_all, b_all, c_association,
+                                         p_association, C.time_slot)
+    r, g = OF.cal_satisfaction(global_pos, users, p_all, b_all, r_c, r_p, c_association, p_association)
+    print("pso_avg",global_best,r,g)
+    if global_best[0]<0:
+        return f,r,g
+    else:
+        return global_best[0],r,g
+
+def pso_convex(users,k,r_c,r_p):
+    uavs = tool.getUavInitialPos(k, users)
+    pre_uavs = uavs
+    # r_c,r_p=dynamic.random_r(users)
+    c_association, p_association, p_all, b_all = OF.objective_resource(uavs, users, r_c)
+    pso = PSO(pre_uavs, users, r_c, r_p, k, )
+    pso.re_init(pre_uavs, users, r_c, r_p, c_association, p_association, p_all, b_all)
+    X, V = pso.initParticle(users)
+    global_pos, global_best, result = pso.pso(X, V, OF.linear_w, OF.objective, )
+    print(global_best)
+
+def pso_avg(users,k,r_c,r_p):
+    uavs = tool.getUavInitialPos(k, users)
+    pre_uavs = uavs
+    # r_c,r_p=dynamic.random_r(users)
+    c_association, p_association, p_all, b_all = OF.objective_resource_avg(uavs, users, r_c)
+    pso = PSO(pre_uavs, users, r_c, r_p, k, )
+    pso.re_init(pre_uavs, users, r_c, r_p, c_association, p_association, p_all, b_all)
+    X, V = pso.initParticle(users)
+    global_pos, global_best, result = pso.pso(X, V, OF.linear_w, OF.objective, )
+    print(global_best)
+
+def kmeans_convex(users,k,r_c,r_p):
+    uavs = tool.getUavInitialPos(k, users)
+    pre_uavs = uavs
+    # r_c,r_p=dynamic.random_r(users)
+    c_association, p_association, p_all, b_all = OF.objective_resource(uavs, users, r_c)
+    f, e, rate, gdop = OF.real_objective(pre_uavs, uavs, users, r_c,r_p, p_all, b_all, c_association,
+                                         p_association, C.time_slot)
+    r, g = OF.cal_satisfaction(uavs, users, p_all, b_all, r_c, r_p, c_association, p_association)
+    # pso = PSO(pre_uavs, users, r_c, r_p, k, )
+    # pso.re_init(pre_uavs, users, r_c, r_p, c_association, p_association, p_all, b_all)
+    # X, V = pso.initParticle(users)
+    # global_pos, global_best, result = pso.pso(X, V, OF.linear_w, OF.objective, )
+    print("km_convex",f,r,g)
+    return f,r,g
+
+def kmeans_avg(users,k,r_c,r_p):
+    uavs = tool.getUavInitialPos(k, users)
+    pre_uavs = uavs
+    # r_c,r_p=dynamic.random_r(users)
+    c_association, p_association, p_all, b_all = OF.objective_resource_proportion(uavs, users, r_c)
+    f, e, rate, gdop = OF.real_objective(pre_uavs, uavs, users,r_c, r_p, p_all, b_all, c_association,
+                                         p_association, C.time_slot)
+    r, g = OF.cal_satisfaction(uavs, users, p_all, b_all, r_c, r_p, c_association, p_association)
+    # pso = PSO(pre_uavs, users, r_c, r_p, k, )
+    # pso.re_init(pre_uavs, users, r_c, r_p, c_association, p_association, p_all, b_all)
+    # X, V = pso.initParticle(users)
+    # global_pos, global_best, result = pso.pso(X, V, OF.linear_w, OF.objective, )
+    print("km_avg",f,r,g)
+    return f,r,g
+
+#
+# users= Users.getUsers("./data.csv")
+# df_c=dynamic.read_csv_to_df('r_c.csv')
+# df_p = dynamic.read_csv_to_df('r_p.csv')
+# index=0
+# r_c,r_p=dynamic.get_r_c_and_r_p(df_c,df_p,index)
+# # pso_convex(users,4,r_c,r_p)
+# # pso_avg(users,4,r_c,r_p)
+# kmeans_convex(users,4,r_c,r_p)
+# kmeans_avg(users,4,r_c,r_p)
+
+def compare():
+    k=4
+    r1,r2,r3,r4=[],[],[],[]
+    r_satisfaction,g_satisfaction=[[] for i in range(4)],[[] for i in range(4)]
+    num=17
+    sum1,sum2,sum3,sum4=0,0,0,0
+    time=10
+    for i in range(0,time):
+        users=Users.generate(num)
+        r_c,r_p=dynamic.random_r(users)
+        s=[0 for i in range(4)]
+        g = [0 for i in range(4)]
+        result1,s[0],g[0]=ipso_convex(users, k, r_c, r_p)
+        result2,s[1],g[1]=ipso_avg(users, k, r_c, r_p)
+        result3,s[2],g[2]=kmeans_convex(users, k, r_c, r_p)
+        result4,s[3],g[3]=kmeans_avg(users, k, r_c, r_p)
+        sum1 += result1
+        sum2 += result2
+        sum3 += result3
+        sum4 += result4
+
+        r1.append(result1)
+        r2.append(result2)
+        r3.append(result3)
+        r4.append(result4)
+        for i in range(4):
+            r_satisfaction[i].append(s[i])
+            g_satisfaction[i].append(g[i])
+    print(r1,r2,r3,r4)
+    print(r_satisfaction,g_satisfaction)
+    print(sum1/time,sum2 /time,sum3 / time,sum4 / time)
+
+
+compare()
+# [[0.8, 0.5714285714285714, 0.5555555555555556, 0.5454545454545454, 0.46153846153846156, 0.6, 0.5294117647058824, 0.42105263157894735], [1.0, 1.0, 1.0, 1.0, 0.8461538461538461, 0.8666666666666667, 0.8823529411764706, 0.7368421052631579], [1.0, 1.0, 1.0, 1.0, 0.9230769230769231, 1.0, 0.8823529411764706, 0.42105263157894735], [1.0, 1.0, 1.0, 0.9090909090909091, 0.9230769230769231, 0.9333333333333333, 0.8823529411764706, 0.8421052631578947]] [[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 0.6666666666666666, 0.7272727272727273, 0.7692307692307693, 0.8, 0.8823529411764706, 0.8421052631578947], [1.0, 1.0, 0.6666666666666666, 0.7272727272727273, 0.7692307692307693, 0.8, 0.8823529411764706, 0.8421052631578947]]

dynamicDeploy/solution.py

+class solution:
+    def __init__(self):
+        self.best = 0
+        self.bestIndividual=[]
+        self.convergence = []
+        self.optimizer=""
+        self.objfname=""
+        self.startTime=0
+        self.endTime=0
+        self.executionTime=0
+        self.lb=0
+        self.ub=0
+        self.dim=0
+        self.popnum=0
+        self.maxiers=0
\ No newline at end of file

featureRepository/.idea/featureRepository.iml

+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>
\ No newline at end of file

featureRepository/.idea/inspectionProfiles/Project_Default.xml

+<component name="InspectionProjectProfileManager">
+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
+    <inspection_tool class="PyPep8NamingInspection" enabled="true" level="WEAK WARNING" enabled_by_default="true">
+      <option name="ignoredErrors">
+        <list>
+          <option value="N802" />
+          <option value="N806" />
+          <option value="N803" />
+        </list>
+      </option>
+    </inspection_tool>
+  </profile>
+</component>
\ No newline at end of file

featureRepository/.idea/inspectionProfiles/profiles_settings.xml

+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>
\ No newline at end of file

featureRepository/.idea/modules.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/featureRepository.iml" filepath="$PROJECT_DIR$/.idea/featureRepository.iml" />
+    </modules>
+  </component>
+</project>
\ No newline at end of file

featureRepository/Geodetics/CMakeLists.txt

+set(CMAKE_INCLUDE_CURRENT_DIR ON)
+
+file(GLOB_RECURSE SOURCES src/*.cpp src/*.h)
+file(GLOB_RECURSE HEADERS include/*.h)
+
+add_library(Geodetics SHARED ${SOURCES} ${HEADERS})
+
+target_include_directories(Geodetics
+  PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/include
+  PRIVATE ${GDAL_ROOT}
+)
+
+target_compile_definitions(Geodetics PRIVATE GEODETICS_LIB)
+
+target_link_directories(Geodetics PRIVATE ${GDAL_ROOT}/lib/${BUILD_TYPE})
+
+target_link_libraries(Geodetics PRIVATE ${GDAL_LIB_NAME}.lib)
+
+
+add_custom_command(TARGET Geodetics POST_BUILD
+  COMMAND ${CMAKE_COMMAND} -E copy $<TARGET_FILE:Geodetics> ${CLIENTS_BIN_DIR}
+  #COMMAND ${CMAKE_COMMAND} -E copy $<TARGET_PDB_FILE:Geodetics> ${CLIENTS_BIN_DIR}
+  COMMAND ${CMAKE_COMMAND} -E copy $<TARGET_LINKER_FILE:Geodetics> ${CLIENTS_LIB_DIR}
+)
+
+
+install(TARGETS Geodetics RUNTIME DESTINATION "Geodetics/${CMAKE_INSTALL_BINDIR}")
+install(FILES ${GDAL_ROOT}/lib/${BUILD_TYPE}/gdal17.dll DESTINATION "Geodetics/${CMAKE_INSTALL_BINDIR}")
+install(FILES ${GDAL_ROOT}/lib/${BUILD_TYPE}/proj.dll DESTINATION "Geodetics/${CMAKE_INSTALL_BINDIR}")
+install(TARGETS Geodetics LIBRARY DESTINATION "Geodetics/${CMAKE_INSTALL_LIBDIR}")
+install(DIRECTORY include DESTINATION "Geodetics")

featureRepository/Geodetics/include/Geodetics/GeodeticsBase.h

+#pragma once
+
+#if defined(GEODETICS_LIB)
+#   define GEODETICS_API __declspec(dllexport)
+#else
+#   define GEODETICS_API __declspec(dllimport)
+#endif