[
{
"path": ".gitignore",
"content": "__pycache__"
},
{
"path": "aco.py",
"content": "import random\nimport numpy as np\nimport math\nimport time\nimport os\n\nclass ACO():\n def __init__(self, vehicle_num, target_num,vehicle_speed, target, time_lim):\n self.num_type_ant = vehicle_num\n self.num_city = target_num+1 #number of cities\n self.group = 200\n self.num_ant = self.group*self.num_type_ant #number of ants\n self.ant_vel = vehicle_speed\n self.cut_time = time_lim\n self.oneee = np.zeros((4,1))\n self.target = target\n self.alpha = 1 #pheromone \n self.beta = 2 \n self.k1 = 0.03\n self.iter_max = 150\n #matrix of the distances between cities \n def distance_matrix(self):\n dis_mat = []\n for i in range(self.num_city):\n dis_mat_each = []\n for j in range(self.num_city):\n dis = math.sqrt(pow(self.target[i][0]-self.target[j][0],2)+pow(self.target[i][1]-self.target[j][1],2))\n dis_mat_each.append(dis)\n dis_mat.append(dis_mat_each)\n return dis_mat\n def run(self):\n print(\"ACO start, pid: %s\" % os.getpid())\n start_time = time.time()\n #distances of nodes\n dis_list = self.distance_matrix()\n dis_mat = np.array(dis_list)\n value_init = self.target[:,2].transpose()\n delay_init = self.target[:,3].transpose() \n pheromone_mat = np.ones((self.num_type_ant,self.num_city,self.num_city))\n #velocity of ants\n path_new = [[0]for i in range (self.num_type_ant)]\n count_iter = 0\n while count_iter < self.iter_max:\n path_sum = np.zeros((self.num_ant,1))\n time_sum = np.zeros((self.num_ant,1))\n value_sum = np.zeros((self.num_ant,1))\n path_mat=[[0]for i in range (self.num_ant)]\n value = np.zeros((self.group,1))\n atten = np.ones((self.num_type_ant,1)) * 0.2\n for ant in range(self.num_ant):\n ant_type = ant % self.num_type_ant\n visit = 0\n if ant_type == 0:\n unvisit_list=list(range(1,self.num_city))#have not visit\n for j in range(1,self.num_city):\n #choice of next city\n trans_list=[]\n tran_sum=0\n trans=0\n #if len(unvisit_list)==0:\n #print('len(unvisit_list)==0')\n for k in range(len(unvisit_list)): # to decide which node to visit\n trans +=np.power(pheromone_mat[ant_type][visit][unvisit_list[k]],self.alpha)*np.power(value_init[unvisit_list[k]]*self.ant_vel[ant_type]/(dis_mat[visit][unvisit_list[k]]*delay_init[unvisit_list[k]]),self.beta)\n #trans +=np.power(pheromone_mat[ant_type][unvisit_list[k]],self.alpha)*np.power(0.05*value_init[unvisit_list[k]],self.beta)\n trans_list.append(trans)\n tran_sum = trans \n rand = random.uniform(0,tran_sum)\n for t in range(len(trans_list)):\n if(rand <= trans_list[t]):\n visit_next = unvisit_list[t]\n break\n else: \n continue\n path_mat[ant].append(visit_next)\n path_sum[ant] += dis_mat[path_mat[ant][j-1]][path_mat[ant][j]]\n time_sum[ant] += path_sum[ant] / self.ant_vel[ant_type] + delay_init[visit_next]\n if time_sum[ant] > self.cut_time:\n time_sum[ant]-=path_sum[ant] / self.ant_vel[ant_type] + delay_init[visit_next] \n path_mat[ant].pop() \n break\n value_sum[ant] += value_init[visit_next]\n unvisit_list.remove(visit_next)#update\n visit = visit_next\n if (ant_type) == self.num_type_ant-1:\n small_group = int(ant/self.num_type_ant)\n for k in range (self.num_type_ant):\n value[small_group]+= value_sum[ant-k]\n #iteration\n if count_iter == 0:\n value_new = max(value)\n value = value.tolist()\n for k in range (0,self.num_type_ant):\n path_new[k] = path_mat[value.index(value_new)*self.num_type_ant+k]\n path_new[k].remove(0)\n else:\n if max(value) > value_new:\n value_new = max(value)\n value = value.tolist()\n for k in range (0,self.num_type_ant):\n path_new[k] = path_mat[value.index(value_new)*self.num_type_ant+k]\n path_new[k].remove(0)\n\n #update pheromone\n pheromone_change = np.zeros((self.num_type_ant,self.num_city,self.num_city))\n for i in range(self.num_ant):\n length = len(path_mat[i])\n m = i%self.num_type_ant\n n = int(i/self.num_type_ant)\n for j in range(length-1): \n pheromone_change[m][path_mat[i][j]][path_mat[i][j+1]]+= value_init[path_mat[i][j+1]]*self.ant_vel[m]/(dis_mat[path_mat[i][j]][path_mat[i][j+1]]*delay_init[path_mat[i][j+1]])\n atten[m] += (value_sum[i]/(np.power((value_new-value[n]),4)+1))/self.group\n\n for k in range (self.num_type_ant):\n pheromone_mat[k]=(1-atten[k])*pheromone_mat[k]+pheromone_change[k]\n count_iter += 1\n\n print(\"ACO result:\", path_new)\n end_time = time.time()\n print(\"ACO time:\", end_time - start_time)\n return path_new, end_time - start_time\n\n"
},
{
"path": "evaluate.py",
"content": "import numpy as np\nimport matplotlib.pyplot as plt\nimport random\nimport pandas as pd\nimport copy\nfrom multiprocessing import Pool\nfrom ga import GA\nfrom aco import ACO\nfrom pso import PSO\n\nclass Env():\n def __init__(self, vehicle_num, target_num, map_size, visualized=True, time_cost=None, repeat_cost=None):\n self.vehicles_position = np.zeros(vehicle_num,dtype=np.int32)\n self.vehicles_speed = np.zeros(vehicle_num,dtype=np.int32)\n self.targets = np.zeros(shape=(target_num+1,4),dtype=np.int32)\n if vehicle_num==5:\n self.size='small'\n if vehicle_num==10:\n self.size='medium'\n if vehicle_num==15:\n self.size='large'\n self.map_size = map_size\n self.speed_range = [10, 15, 30]\n #self.time_lim = 1e6\n self.time_lim = self.map_size / self.speed_range[1]\n self.vehicles_lefttime = np.ones(vehicle_num,dtype=np.float32) * self.time_lim\n self.distant_mat = np.zeros((target_num+1,target_num+1),dtype=np.float32)\n self.total_reward = 0\n self.reward = 0\n self.visualized = visualized\n self.time = 0\n self.time_cost = time_cost\n self.repeat_cost = repeat_cost\n self.end = False\n self.assignment = [[] for i in range(vehicle_num)]\n self.task_generator()\n \n def task_generator(self):\n for i in range(self.vehicles_speed.shape[0]):\n choose = random.randint(0,2)\n self.vehicles_speed[i] = self.speed_range[choose]\n for i in range(self.targets.shape[0]-1):\n self.targets[i+1,0] = random.randint(1,self.map_size) - 0.5*self.map_size # x position\n self.targets[i+1,1] = random.randint(1,self.map_size) - 0.5*self.map_size # y position\n self.targets[i+1,2] = random.randint(1,10) # reward\n self.targets[i+1,3] = random.randint(5,30) # time consumption to finish the mission \n for i in range(self.targets.shape[0]):\n for j in range(self.targets.shape[0]):\n self.distant_mat[i,j] = np.linalg.norm(self.targets[i,:2]-self.targets[j,:2])\n self.targets_value = copy.deepcopy((self.targets[:,2]))\n \n def step(self, action):\n count = 0\n for j in range(len(action)):\n k = action[j]\n delta_time = self.distant_mat[self.vehicles_position[j],k] / self.vehicles_speed[j] + self.targets[k,3]\n self.vehicles_lefttime[j] = self.vehicles_lefttime[j] - delta_time\n if self.vehicles_lefttime[j] < 0:\n count = count + 1\n continue\n else:\n if k == 0:\n self.reward = - self.repeat_cost\n else:\n self.reward = self.targets[k,2] - delta_time * self.time_cost + self.targets[k,2]\n if self.targets[k,2] == 0:\n self.reward = self.reward - self.repeat_cost\n self.vehicles_position[j] = k\n self.targets[k,2] = 0\n self.total_reward = self.total_reward + self.reward\n self.assignment[j].append(action)\n if count == len(action):\n self.end = True\n \n def run(self, assignment, algorithm, play, rond):\n self.assignment = assignment\n self.algorithm = algorithm\n self.play = play\n self.rond = rond\n self.get_total_reward()\n if self.visualized:\n self.visualize() \n \n def reset(self):\n self.vehicles_position = np.zeros(self.vehicles_position.shape[0],dtype=np.int32)\n self.vehicles_lefttime = np.ones(self.vehicles_position.shape[0],dtype=np.float32) * self.time_lim\n self.targets[:,2] = self.targets_value\n self.total_reward = 0\n self.reward = 0\n self.end = False\n \n def get_total_reward(self):\n for i in range(len(self.assignment)):\n speed = self.vehicles_speed[i]\n for j in range(len(self.assignment[i])):\n position = self.targets[self.assignment[i][j],:4]\n self.total_reward = self.total_reward + position[2]\n if j == 0:\n self.vehicles_lefttime[i] = self.vehicles_lefttime[i] - np.linalg.norm(position[:2]) / speed - position[3]\n else:\n self.vehicles_lefttime[i] = self.vehicles_lefttime[i] - np.linalg.norm(position[:2]-position_last[:2]) / speed - position[3]\n position_last = position\n if self.vehicles_lefttime[i] > self.time_lim:\n self.end = True\n break\n if self.end:\n self.total_reward = 0\n break\n \n def visualize(self):\n if self.assignment == None:\n plt.scatter(x=0,y=0,s=200,c='k')\n plt.scatter(x=self.targets[1:,0],y=self.targets[1:,1],s=self.targets[1:,2]*10,c='r')\n plt.title('Target distribution')\n plt.savefig('task_pic/'+self.size+'/'+self.algorithm+ \"-%d-%d.png\" % (self.play,self.rond))\n plt.cla()\n else:\n plt.title('Task assignment by '+self.algorithm +', total reward : '+str(self.total_reward)) \n plt.scatter(x=0,y=0,s=200,c='k')\n plt.scatter(x=self.targets[1:,0],y=self.targets[1:,1],s=self.targets[1:,2]*10,c='r')\n for i in range(len(self.assignment)):\n trajectory = np.array([[0,0,20]])\n for j in range(len(self.assignment[i])):\n position = self.targets[self.assignment[i][j],:3]\n trajectory = np.insert(trajectory,j+1,values=position,axis=0) \n plt.scatter(x=trajectory[1:,0],y=trajectory[1:,1],s=trajectory[1:,2]*10,c='b')\n plt.plot(trajectory[:,0], trajectory[:,1]) \n plt.savefig('task_pic/'+self.size+'/'+self.algorithm+ \"-%d-%d.png\" % (self.play,self.rond))\n plt.cla()\n \ndef evaluate(vehicle_num, target_num, map_size):\n if vehicle_num==5:\n size='small'\n if vehicle_num==10:\n size='medium'\n if vehicle_num==15:\n size='large'\n re_ga=[[] for i in range(10)]\n re_aco=[[] for i in range(10)]\n re_pso=[[] for i in range(10)]\n for i in range(10):\n env = Env(vehicle_num,target_num,map_size,visualized=True)\n for j in range(10):\n p=Pool(3)\n ga = GA(vehicle_num,env.vehicles_speed,target_num,env.targets,env.time_lim)\n aco = ACO(vehicle_num,target_num,env.vehicles_speed,env.targets,env.time_lim)\n pso = PSO(vehicle_num,target_num ,env.targets,env.vehicles_speed,env.time_lim)\n ga_result=p.apply_async(ga.run)\n aco_result=p.apply_async(aco.run)\n pso_result=p.apply_async(pso.run)\n p.close()\n p.join()\n ga_task_assignmet = ga_result.get()[0]\n env.run(ga_task_assignmet,'GA',i+1,j+1)\n re_ga[i].append((env.total_reward,ga_result.get()[1]))\n env.reset()\n aco_task_assignmet = aco_result.get()[0]\n env.run(aco_task_assignmet,'ACO',i+1,j+1)\n re_aco[i].append((env.total_reward,aco_result.get()[1]))\n env.reset()\n pso_task_assignmet = pso_result.get()[0]\n env.run(pso_task_assignmet,'PSO',i+1,j+1)\n re_pso[i].append((env.total_reward,pso_result.get()[1]))\n env.reset()\n x_index=np.arange(10)\n ymax11=[]\n ymax12=[]\n ymax21=[]\n ymax22=[]\n ymax31=[]\n ymax32=[]\n ymean11=[]\n ymean12=[]\n ymean21=[]\n ymean22=[]\n ymean31=[]\n ymean32=[]\n for i in range(10):\n tmp1=[re_ga[i][j][0] for j in range(10)]\n tmp2=[re_ga[i][j][1] for j in range(10)]\n ymax11.append(np.amax(tmp1))\n ymax12.append(np.amax(tmp2))\n ymean11.append(np.mean(tmp1))\n ymean12.append(np.mean(tmp2))\n tmp1=[re_aco[i][j][0] for j in range(10)]\n tmp2=[re_aco[i][j][1] for j in range(10)]\n ymax21.append(np.amax(tmp1))\n ymax22.append(np.amax(tmp2))\n ymean21.append(np.mean(tmp1))\n ymean22.append(np.mean(tmp2))\n tmp1=[re_pso[i][j][0] for j in range(10)]\n tmp2=[re_pso[i][j][1] for j in range(10)]\n ymax31.append(np.amax(tmp1))\n ymax32.append(np.amax(tmp2))\n ymean31.append(np.mean(tmp1))\n ymean32.append(np.mean(tmp2))\n rects1=plt.bar(x_index,ymax11,width=0.1,color='b',label='ga_max_reward')\n rects2=plt.bar(x_index+0.1,ymax21,width=0.1,color='r',label='aco_max_reward')\n rects3=plt.bar(x_index+0.2,ymax31,width=0.1,color='g',label='pso_max_reward')\n plt.xticks(x_index+0.1,x_index)\n plt.legend()\n plt.title('max_reward_for_'+size+'_size')\n plt.savefig('max_reward_'+size+'.png')\n plt.cla()\n \n rects1=plt.bar(x_index,ymax12,width=0.1,color='b',label='ga_max_time')\n rects2=plt.bar(x_index+0.1,ymax22,width=0.1,color='r',label='aco_max_time')\n rects3=plt.bar(x_index+0.2,ymax32,width=0.1,color='g',label='pso_max_time')\n plt.xticks(x_index+0.1,x_index)\n plt.legend()\n plt.title('max_time_for_'+size+'_size')\n plt.savefig('max_time_'+size+'.png')\n plt.cla()\n \n rects1=plt.bar(x_index,ymean11,width=0.1,color='b',label='ga_mean_reward')\n rects2=plt.bar(x_index+0.1,ymean21,width=0.1,color='r',label='aco_mean_reward')\n rects3=plt.bar(x_index+0.2,ymean31,width=0.1,color='g',label='pso_mean_reward')\n plt.xticks(x_index+0.1,x_index)\n plt.legend()\n plt.title('mean_reward_for_'+size+'_size')\n plt.savefig('mean_reward_'+size+'.png')\n plt.cla()\n \n rects1=plt.bar(x_index,ymean12,width=0.1,color='b',label='ga_mean_time')\n rects2=plt.bar(x_index+0.1,ymean22,width=0.1,color='r',label='aco_mean_time')\n rects3=plt.bar(x_index+0.2,ymean32,width=0.1,color='g',label='pso_mean_time')\n plt.xticks(x_index+0.1,x_index)\n plt.legend()\n plt.title('mean_time_for_'+size+'_size')\n plt.savefig('mean_time_'+size+'.png')\n plt.cla()\n \n t_ga=[]\n r_ga=[]\n t_aco=[]\n r_aco=[]\n t_pso=[]\n r_pso=[]\n for i in range(10):\n for j in range(10):\n t_ga.append(re_ga[i][j][1])\n r_ga.append(re_ga[i][j][0])\n t_aco.append(re_aco[i][j][1])\n r_aco.append(re_aco[i][j][0])\n t_pso.append(re_pso[i][j][1])\n r_pso.append(re_pso[i][j][0])\n dataframe = pd.DataFrame({'ga_time':t_ga,'ga_reward':r_ga,'aco_time':t_aco,'aco_reward':r_aco,'pso_time':t_pso,'pso_reward':r_pso})\n dataframe.to_csv(size+'_size_result.csv',sep=',')\n \n \nif __name__=='__main__':\n # small scale\n evaluate(5,30,5e3)\n # medium scale\n evaluate(10,60,1e4)\n # large scale\n evaluate(15,90,1.5e4)\n"
},
{
"path": "ga.py",
"content": "import numpy as np\nimport random\nimport time\nimport os\n\n\nclass GA():\n def __init__(self, vehicle_num, vehicles_speed, target_num, targets, time_lim):\n # vehicles_speed,targets in the type of narray\n self.vehicle_num = vehicle_num\n self.vehicles_speed = vehicles_speed\n self.target_num = target_num\n self.targets = targets\n self.time_lim = time_lim\n self.map = np.zeros(shape=(target_num+1, target_num+1), dtype=float)\n self.pop_size = 50\n self.p_cross = 0.6\n self.p_mutate = 0.005\n for i in range(target_num+1):\n self.map[i, i] = 0\n for j in range(i):\n self.map[j, i] = self.map[i, j] = np.linalg.norm(\n targets[i, :2]-targets[j, :2])\n self.pop = np.zeros(\n shape=(self.pop_size, vehicle_num-1+target_num-1), dtype=np.int32)\n self.ff = np.zeros(self.pop_size, dtype=float)\n for i in range(self.pop_size):\n for j in range(vehicle_num-1):\n self.pop[i, j] = random.randint(0, target_num)\n for j in range(target_num-1):\n self.pop[i, vehicle_num+j -\n 1] = random.randint(0, target_num-j-1)\n self.ff[i] = self.fitness(self.pop[i, :])\n self.tmp_pop = np.array([])\n self.tmp_ff = np.array([])\n self.tmp_size = 0\n\n def fitness(self, gene):\n ins = np.zeros(self.target_num+1, dtype=np.int32)\n seq = np.zeros(self.target_num, dtype=np.int32)\n ins[self.target_num] = 1\n for i in range(self.vehicle_num-1):\n ins[gene[i]] += 1\n rest = np.array(range(1, self.target_num+1))\n for i in range(self.target_num-1):\n seq[i] = rest[gene[i+self.vehicle_num-1]]\n rest = np.delete(rest, gene[i+self.vehicle_num-1])\n seq[self.target_num-1] = rest[0]\n i = 0 # index of vehicle\n pre = 0 # index of last target\n post = 0 # index of ins/seq\n t = 0\n reward = 0\n while i < self.vehicle_num:\n if ins[post] > 0:\n i += 1\n ins[post] -= 1\n pre = 0\n t = 0\n else:\n t += self.targets[pre, 3]\n past = self.map[pre, seq[post]]/self.vehicles_speed[i]\n t += past\n if t < self.time_lim:\n reward += self.targets[seq[post], 2]\n pre = seq[post]\n post += 1\n return reward\n\n def selection(self):\n roll = np.zeros(self.tmp_size, dtype=float)\n roll[0] = self.tmp_ff[0]\n for i in range(1, self.tmp_size):\n roll[i] = roll[i-1]+self.tmp_ff[i]\n for i in range(self.pop_size):\n xx = random.uniform(0, roll[self.tmp_size-1])\n j = 0\n while xx > roll[j]:\n j += 1\n self.pop[i, :] = self.tmp_pop[j, :]\n self.ff[i] = self.tmp_ff[j]\n\n def mutation(self):\n for i in range(self.tmp_size):\n flag = False\n for j in range(self.vehicle_num-1):\n if random.random() < self.p_mutate:\n self.tmp_pop[i, j] = random.randint(0, self.target_num)\n flag = True\n for j in range(self.target_num-1):\n if random.random() < self.p_mutate:\n self.tmp_pop[i, self.vehicle_num+j -\n 1] = random.randint(0, self.target_num-j-1)\n flag = True\n if flag:\n self.tmp_ff[i] = self.fitness(self.tmp_pop[i, :])\n\n def crossover(self):\n new_pop = []\n new_ff = []\n new_size = 0\n for i in range(0, self.pop_size, 2):\n if random.random() < self.p_cross:\n x1 = random.randint(0, self.vehicle_num-2)\n x2 = random.randint(0, self.target_num-2)+self.vehicle_num\n g1 = self.pop[i, :]\n g2 = self.pop[i+1, :]\n g1[x1:x2] = self.pop[i+1, x1:x2]\n g2[x1:x2] = self.pop[i, x1:x2]\n new_pop.append(g1)\n new_pop.append(g2)\n new_ff.append(self.fitness(g1))\n new_ff.append(self.fitness(g2))\n new_size += 2\n self.tmp_size = self.pop_size+new_size\n self.tmp_pop = np.zeros(\n shape=(self.tmp_size, self.vehicle_num-1+self.target_num-1), dtype=np.int32)\n self.tmp_pop[0:self.pop_size, :] = self.pop\n self.tmp_pop[self.pop_size:self.tmp_size, :] = np.array(new_pop)\n self.tmp_ff = np.zeros(self.tmp_size, dtype=float)\n self.tmp_ff[0:self.pop_size] = self.ff\n self.tmp_ff[self.pop_size:self.tmp_size] = np.array(new_ff)\n\n def run(self):\n print(\"GA start, pid: %s\" % os.getpid())\n start_time = time.time()\n cut = 0\n count = 0\n while count < 500:\n self.crossover()\n self.mutation()\n self.selection()\n new_cut = self.tmp_ff.max()\n if cut < new_cut:\n cut = new_cut\n count = 0\n gene = self.tmp_pop[np.argmax(self.tmp_ff)]\n else:\n count += 1\n\n ins = np.zeros(self.target_num+1, dtype=np.int32)\n seq = np.zeros(self.target_num, dtype=np.int32)\n ins[self.target_num] = 1\n for i in range(self.vehicle_num-1):\n ins[gene[i]] += 1\n rest = np.array(range(1, self.target_num+1))\n for i in range(self.target_num-1):\n seq[i] = rest[gene[i+self.vehicle_num-1]]\n rest = np.delete(rest, gene[i+self.vehicle_num-1])\n seq[self.target_num-1] = rest[0]\n task_assignment = [[] for i in range(self.vehicle_num)]\n i = 0 # index of vehicle\n pre = 0 # index of last target\n post = 0 # index of ins/seq\n t = 0\n reward = 0\n while i < self.vehicle_num:\n if ins[post] > 0:\n i += 1\n ins[post] -= 1\n pre = 0\n t = 0\n else:\n t += self.targets[pre, 3]\n past = self.map[pre, seq[post]]/self.vehicles_speed[i]\n t += past\n if t < self.time_lim:\n task_assignment[i].append(seq[post])\n reward += self.targets[seq[post], 2]\n pre = seq[post]\n post += 1\n print(\"GA result:\", task_assignment)\n end_time = time.time()\n print(\"GA time:\", end_time - start_time)\n return task_assignment, end_time - start_time\n\n"
},
{
"path": "large_size_result.csv",
"content": 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"path": "medium_size_result.csv",
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},
{
"path": "pso.py",
"content": "# coding: utf-8\nimport numpy as np\nimport random\nimport math\nimport cmath\nimport time\nimport os\n# ----------------------Optimization scheme----------------------------------\n# Optimization ideas：\n# 1. Increase the convergence factor k；\n# 2. Dynamic change of inertia factor W；\n# 3. Using PSO local search algorithm(Ring method)\n# 4. The probability of position variation is added\n# ----------------------Set PSO Parameter---------------------------------\n\n\nclass PSO():\n def __init__(self, uav_num, target_num, targets, vehicles_speed, time_lim):\n self.uav_num = uav_num\n self.dim = target_num\n self.targets = targets\n self.vehicles_speed = vehicles_speed\n self.time_all = time_lim\n self.pN = 2*(self.uav_num+self.dim) # Number of particles\n self.max_iter = 0 # Number of iterations\n # Target distance list (dim+1）*（dim+1)\n self.Distance = np.zeros((target_num+1, target_num+1))\n self.Value = np.zeros(target_num+1) # Value list of targets 1*dim+1\n self.Stay_time = []\n # UAV flight speed matrix\n self.w = 0.8\n self.c1 = 2\n self.c2 = 2\n self.r1 = 0.6\n self.r2 = 0.3\n self.k = 0 # Convergence factor\n self.wini = 0.9\n self.wend = 0.4\n\n self.X = np.zeros((self.pN, self.dim+self.uav_num-1)\n ) # Position of all particles\n self.V = np.zeros((self.pN, self.dim+self.uav_num-1)\n ) # Velocity of all particles\n # The historical optimal position of each individual\n self.pbest = np.zeros((self.pN, self.dim+self.uav_num-1))\n self.gbest = np.zeros((1, self.dim+self.uav_num-1))\n # Global optimal position\n self.gbest_ring = np.zeros((self.pN, self.dim+self.uav_num-1))\n # Historical optimal fitness of each individual\n self.p_fit = np.zeros(self.pN)\n self.fit = 0 # Global optimal fitness\n self.ring = []\n self.ring_fit = np.zeros(self.pN)\n # variation parameter\n self.p1 = 0.4 # Probability of mutation\n self.p2 = 0.5 # Proportion of individuals with variation in population\n self.p3 = 0.5 # Proportion of locations where variation occurs\n self.TEST = []\n self.test_num = 0\n self.uav_best = []\n\n self.time_out = np.zeros(self.uav_num)\n \n self.cal_time = 0\n # ------------------Get Initial parameter------------------\n\n def fun_get_initial_parameter(self):\n self.max_iter = 40*(self.uav_num+self.dim)\n if self.max_iter > 4100:\n self.max_iter = 4100\n\n # Get Stay_time Arrary & Distance Arrary & Value Arrary\n Targets = self.targets\n self.Stay_time = Targets[:, 3]\n self.Distance = np.zeros((self.dim+1, self.dim+1))\n self.Value = np.zeros(self.dim+1)\n for i in range(self.dim+1):\n self.Value[i] = Targets[i, 2]\n for j in range(i):\n self.Distance[i][j] = (\n Targets[i, 0]-Targets[j, 0])*(Targets[i, 0]-Targets[j, 0])\n self.Distance[i][j] = self.Distance[i][j] + \\\n (Targets[i, 1]-Targets[j, 1])*(Targets[i, 1]-Targets[j, 1])\n self.Distance[i][j] = math.sqrt(self.Distance[i][j])\n self.Distance[j][i] = self.Distance[i][j]\n # ------------------Transfer_Function---------------------\n\n def fun_Transfer(self, X):\n # Converting continuous sequence X into discrete sequence X_path\n X1 = X[0:self.dim]\n X_path = []\n l1 = len(X1)\n for i in range(l1):\n m = X1[i]*(self.dim-i)\n m = math.floor(m)\n X_path.append(m)\n # Converting the continuous interpolation sequence X into discrete interpolation sequence X_rank\n X2 = X[self.dim:]\n l1 = len(X2)\n X_rank = []\n for i in range(l1):\n\n m = X2[i]*(self.dim+1)\n\n m1 = math.floor(m)\n X_rank.append(m1)\n # Rank and Complement\n c = sorted(X_rank)\n l1 = len(c)\n Rank = []\n Rank.append(0)\n for i in range(l1):\n Rank.append(c[i])\n Rank.append(self.dim)\n # Get Separate_Arrary\n Sep = []\n for i in range(l1+1):\n sep = Rank[i+1]-Rank[i]\n Sep.append(sep)\n return X_path, Sep\n\n # -------------------Obtain the Real Flight Path Sequence of Particles--------------------------\n def position(self, X):\n Position_All = list(range(1, self.dim+1))\n X2 = []\n for i in range(self.dim):\n m1 = X[i]\n m1 = int(m1)\n X2.append(Position_All[m1])\n del Position_All[m1]\n return X2\n # ---------------------Fitness_Computing Function-----------------------------\n\n def function(self, X):\n X_path, Sep = self.fun_Transfer(X)\n\n # Obtain the Real Flight Path Sequence of Particles\n X = self.position(X_path)\n # Get the search sequence of each UAV\n UAV = []\n l = 0\n for i in range(self.uav_num):\n UAV.append([])\n k = Sep[i]\n for j in range(k):\n UAV[i].append(X[l])\n l = l+1\n\n # Calculate Fitness\n fitness = 0\n for i in range(self.uav_num):\n k = Sep[i]\n t = 0\n for j in range(k):\n m1 = UAV[i][j]\n\n if j == 0:\n t = t+self.Distance[0, m1] / \\\n self.vehicles_speed[i]+self.Stay_time[m1]\n else:\n m1 = UAV[i][j]\n m2 = UAV[i][j-1]\n t = t+self.Distance[m1][m2] / \\\n self.vehicles_speed[i]+self.Stay_time[m1]\n if t <= self.time_all:\n fitness = fitness+self.Value[m1]\n return fitness\n # ----------------------------variation-------------------------------------------\n\n def variation_fun(self):\n p1 = np.random.uniform(0, 1) # Probability of mutation\n if p1 < self.p1:\n for i in range(self.pN):\n # Proportion of individuals with variation in population\n p2 = np.random.uniform(0, 1)\n if p2 < self.p2:\n # Numbers of locations where variation occurs\n m = int(self.p3*(self.dim+self.uav_num-1))\n for j in range(m):\n replace_position = math.floor(\n np.random.uniform(0, 1)*(self.dim+self.uav_num-1))\n replace_value = np.random.uniform(0, 1)\n self.X[i][replace_position] = replace_value\n # Update pbest & gbest\n for i in range(self.pN):\n temp = self.function(self.X[i])\n self.ring_fit[i] = temp\n if temp > self.p_fit[i]:\n self.p_fit[i] = temp\n self.pbest[i] = self.X[i]\n # Update gbest\n if self.p_fit[i] > self.fit:\n self.gbest = self.X[i]\n self.fit = self.p_fit[i]\n\n # ---------------------Population Initialization----------------------------------\n\n def init_Population(self):\n # Initialization of position(X), speed(V), history optimal(pbest) and global optimal(gbest)\n for i in range(self.pN):\n x = np.random.uniform(0, 1, self.dim+self.uav_num-1)\n self.X[i, :] = x\n v = np.random.uniform(0, 0.4, self.dim+self.uav_num-1)\n self.V[i, :] = v\n self.pbest[i] = self.X[i]\n\n tmp = self.function(self.X[i])\n self.p_fit[i] = tmp\n if tmp > self.fit:\n self.fit = tmp\n self.gbest = self.X[i]\n # Calculate the convergence factor k\n phi = self.c1+self.c2\n k = abs(phi*phi-4*phi)\n k = cmath.sqrt(k)\n k = abs(2-phi-k)\n k = 2/k\n self.k = k\n # Initialize ring_matrix\n for i in range(self.pN):\n self.ring.append([])\n self.ring[i].append(i)\n # Initialize test_set\n self.TEST = np.zeros((self.test_num, self.dim+self.uav_num-1))\n for i in range(self.test_num):\n test = np.random.uniform(0, 1, self.dim+self.uav_num-1)\n self.TEST[i, :] = test\n\n # ----------------------Update Particle Position----------------------------------\n\n def iterator(self):\n fitness = []\n fitness_old = 0\n k = 0\n for t in range(self.max_iter):\n w = (self.wini-self.wend)*(self.max_iter-t)/self.max_iter+self.wend\n self.w = w\n # Variation\n self.variation_fun()\n l1 = len(self.ring[0])\n # Local PSO algorithm\n # Update ring_arrary\n if l1 < self.pN:\n if not(t % 2):\n k = k+1\n for i in range(self.pN):\n m1 = i-k\n if m1 < 0:\n m1 = self.pN+m1\n m2 = i+k\n if m2 > self.pN-1:\n m2 = m2-self.pN\n self.ring[i].append(m1)\n self.ring[i].append(m2)\n # Update gbest_ring\n l_ring = len(self.ring[0])\n for i in range(self.pN):\n fitness1 = 0\n for j in range(l_ring):\n m1 = self.ring[i][j]\n fitness2 = self.ring_fit[m1]\n if fitness2 > fitness1:\n self.gbest_ring[i] = self.X[m1]\n fitness1 = fitness2\n # Update velocity\n for i in range(self.pN):\n self.V[i] = self.k*(self.w * self.V[i] + self.c1 * self.r1 * (self.pbest[i] - self.X[i])) + \\\n self.c2 * self.r2 * (self.gbest_ring[i] - self.X[i])\n # Update position\n self.X[i] = self.X[i] + self.V[i]\n\n # Global PSO algorithm\n else:\n # Update velocity\n for i in range(self.pN):\n self.V[i] = self.k*(self.w * self.V[i] + self.c1 * self.r1 * (self.pbest[i] - self.X[i])) + \\\n self.c2 * self.r2 * (self.gbest - self.X[i])\n # Update position\n self.X[i] = self.X[i] + self.V[i]\n\n # Set position boundary\n for i in range(self.pN):\n for j in range(self.dim+self.uav_num-1):\n if self.X[i][j] >= 1:\n self.X[i][j] = 0.999\n if self.X[i][j] < 0:\n self.X[i][j] = 0\n # Update pbest & gbest\n for i in range(self.pN):\n temp = self.function(self.X[i])\n self.ring_fit[i] = temp\n if temp > self.p_fit[i]:\n self.p_fit[i] = temp\n self.pbest[i] = self.X[i]\n # Update gbest\n if self.p_fit[i] > self.fit:\n self.gbest = self.X[i]\n self.fit = self.p_fit[i]\n self.uav_best = self.fun_Data()\n\n # print\n fitness.append(self.fit)\n if self.fit == fitness_old:\n continue\n else:\n fitness_old = self.fit\n return fitness\n\n # ---------------------Data_Processing Function---------------------------\n def fun_Data(self):\n X_path, Sep = self.fun_Transfer(self.gbest)\n # Obtain the Real Flight Path Sequence of Particles\n X = self.position(X_path)\n # Get the search sequence of each UAV\n UAV = []\n l = 0\n for i in range(self.uav_num):\n UAV.append([])\n k = Sep[i]\n for j in range(k):\n UAV[i].append(X[l])\n l = l+1\n # Calculate UAV_Out\n UAV_Out = []\n for i in range(self.uav_num):\n k = Sep[i]\n t = 0\n UAV_Out.append([])\n for j in range(k):\n m1 = UAV[i][j]\n if j == 0:\n t = t+self.Distance[0, m1] / \\\n self.vehicles_speed[i]+self.Stay_time[m1]\n else:\n m2 = UAV[i][j-1]\n t = t+self.Distance[m2][m1] / \\\n self.vehicles_speed[i]+self.Stay_time[m1]\n if t <= self.time_all:\n UAV_Out[i].append(m1)\n self.time_out[i] = t\n return UAV_Out\n # ---------------------TEST Function------------------------------\n\n def fun_TEST(self):\n Test_Value = []\n for i in range(self.test_num):\n Test_Value.append(self.function(self.TEST[i]))\n return Test_Value\n # ---------------------Main----------------------------------------\n\n def run(self):\n print(\"PSO start, pid: %s\" % os.getpid())\n start_time = time.time()\n self.fun_get_initial_parameter()\n self.init_Population()\n fitness = self.iterator()\n end_time = time.time()\n #self.cal_time = end_time - start_time\n #self.task_assignment = self.uav_best\n print(\"PSO result:\", self.uav_best)\n print(\"PSO time:\", end_time - start_time)\n return self.uav_best, end_time - start_time\n \n\n"
},
{
"path": "readme.md",
"content": "# Multi-UAV Task Assignment Benchmark\n## 多无人机任务分配算法测试基准\n\n## Introduction\nA benchmark for multi-UAV task assignment is presented in order to evaluate different algorithms. An extended Team Orienteering Problem is modeled for a kind of multi-UAV task assignment problem. Three intelligent algorithms, i.e., Genetic Algorithm, Ant Colony Optimization and Particle Swarm Optimization are implemented to solve the problem. A series of experiments with different settings are conducted to evaluate three algorithms. The modeled problem and the evaluation results constitute a benchmark, which can be used to evaluate other algorithms used for multi-UAV task assignment problems.\n\nNotice that three algorithms run at three CPU cores respectively, which means that there is no parallel optimization in this benchmark.\n\n![](\"./task_pic/large/ACO-1-1.png\")
\n\n![](\"./mean_reward_large.png\")
\n\n![](\"./mean_time_large.png\")
\n\nPlease refer to the paper to see more detail.\n\nK. Xiao, J. Lu, Y. Nie, L. Ma, X. Wang and G. Wang, \"A Benchmark for Multi-UAV Task Assignment of an Extended Team Orienteering Problem,\" 2022 China Automation Congress (CAC), Xiamen, China, 2022, pp. 6966-6970, doi: 10.1109/CAC57257.2022.10054991.\n\nArXiv preprint **[ arXiv:2003.09700](https://arxiv.org/abs/2009.00363)** \n\n\n## Usage\n\n### 1. Algorithm input and output\n\nAlgorithm input includes vehicle number (scalar), speeds of vehicles ($n\\times1$ array), target number (scalar $n$), targets ($(n+1)\\times4$ array, the first line is depot, the first column is x position, the second column is y position, the third column is reward and the forth column is time consumption to finish the mission), time limit (scalar). The code below is the initialization of the class GA in `ga.py`.\n\n```python\ndef __init__(self, vehicle_num, vehicles_speed, target_num, targets, time_lim)\n```\n\nThere should be a function called `run()` in the algorithm class, and the function should return task assignment plan(array, e.g. [[28, 19, 11], [25, 22, 7, 16, 17, 23], [21, 26, 12, 9, 6, 3], [5, 15, 1], [18, 20, 29]], each subset is a vehicle path) and computational time usage (scalar). \n\n### 2. Evaluate\n\nYou can replace one algorithm below with another algorithm in `evaluate.py`, and then `python evaluate.py`. If you don't want to evaluate three algorithm together, you should modify the code properly( this is easy). \n\n```python\nga = GA(vehicle_num,env.vehicles_speed,target_num,env.targets,env.time_lim)\naco = ACO(vehicle_num,target_num,env.vehicles_speed,env.targets,env.time_lim)\npso = PSO(vehicle_num,target_num ,env.targets,env.vehicles_speed,env.time_lim)\nga_result=p.apply_async(ga.run)\naco_result=p.apply_async(aco.run)\npso_result=p.apply_async(pso.run)\np.close()\np.join()\nga_task_assignmet = ga_result.get()[0]\nenv.run(ga_task_assignmet,'GA',i+1,j+1)\nre_ga[i].append((env.total_reward,ga_result.get()[1]))\nenv.reset()\naco_task_assignmet = aco_result.get()[0]\nenv.run(aco_task_assignmet,'ACO',i+1,j+1)\nre_aco[i].append((env.total_reward,aco_result.get()[1]))\nenv.reset()\npso_task_assignmet = pso_result.get()[0]\nenv.run(pso_task_assignmet,'PSO',i+1,j+1)\nre_pso[i].append((env.total_reward,pso_result.get()[1]))\n```\n\n### 3. About reinforcement learning\n\nIn `Env()` in `evaluate.py`, function `step` is used for reinforcement learning. Because this is still being developed, we cannot supply a demo. If your algorithm is reinforcement learning, you can try to train it with `Env()`. Your pull request and issue are welcome.\n\n## Enhancement\n\nThis [repository](https://github.com/dietmarwo/Multi-UAV-Task-Assignment-Benchmark) does great enhancement and you can use it for high performance. Thanks to [dietmarwo](https://github.com/dietmarwo) for the nice work.\n\n1) GA uses [numba](https://numba.pydata.org/) for a dramatic speedup. Parameters are adapted so that the\n execution time remains the same: popsize 50 -> 300, iterations 500 -> 6000\n For this reason GA now performs much better compared to the original version.\n\n2) Experiments are configured so that wall time for small size is balanced. This means:\n increased effort for GA, decreased effort for ACO. For medium / large \n problem size you see which algorithms scale badly: Increase execution time superlinear\n in relation to the problem size. Avoid these for large problems. \n\n3) Adds a standard continuous optimization algorithm: [BiteOpt](https://github.com/avaneev/biteopt) \n from Aleksey Vaneev - using the same fitness function as GA.py. \n BiteOpt is the only algorithm included which works well with a large problem size. \n It is by far the simplest implementation, only the fitness function needs\n to be coded, since we can apply a continuous optimization library \n [fcmaes](https://github.com/dietmarwo/fast-cma-es). Execute \"pip install fcmaes\" to use it. \n\n4) Uses NestablePool to enable BiteOpt multiprocessing: Many BiteOpt optimization runs\n are performed in parallel and the best result is returned. Set workers=1 \n if you want to test BiteOpt single threaded. \n \n5) All results are created using an AMD 5950x 16 core processor\n utilizing all cores: 29 parallel BiteOpt threads, the other 3 algorithms remain single threaded. \n\n6) Added test_bite.py where you can monitor the progress of BiteOpt applied to the problem.\n\n7) Added test_mode.py where you can monitor the progress of fcmaes-MODE applied to the problem and compare it\n to BiteOpt for the same instance. fcmaes-MODE is a multi-objective optimizer applied to a \n multi-objective variant of the problem.\n Objectives are: reward (to be maximized), maximal time (to be minimized), energy (to be minimized).\n The maximal time constraint from the single objective case is still valid.\n Energy consumption is approximated by `sum(dt*v*v)`\n\n\n \n\n"
},
{
"path": "small_size_result.csv",
"content": 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