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GeezerVison is showing github.com — Container-Loading-Problem/PackingAlgorithm.py at master · Nivedha-Ramesh/Container-Loading-Problem Expand file tree/ Copy pathPackingAlgorithm.pyMore file actions 327 lines (287 loc) · 13.6 KB / Copy pathPackingAlgorithm.pyFile metadata and controls327 lines (287 loc) · 13.6 KB 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 import random from copy import deepcopy from operator import itemgetter class PackingAlgorithm: def __init__(self, box_params, truck_dimension, total_value, population_size=100, k=2, generations=100, pc=0.8, pm1=0.2, pm2=0.02, rotation=6): self.box_params = box_params self.truck_dimension = truck_dimension self.total_value = total_value self.population_size = population_size self.generations = generations self.pc = pc self.numP = int(self.pc * self.population_size) self.k = k self.pm1 = pm1 self.pm2 = pm2 self.rotation = rotation self.avg_fitness = [] self.population = self.generate_population() def generate_population(self): """ This function uses the dimensions of the boxes to create a diploid chromosome for every individual in the population, It consists of 'order', which is a permutation as the boxes order and a list of their rotation values """ population = {} keys = list(self.box_params.keys()) for i in range(self.population_size): random.shuffle(keys) population[i] = {"order": deepcopy(keys), "rotate": [random.randint(0, self.rotation - 1) for _ in range(len(self.box_params))]} return population def evaluate(self, pop): """ Evaluates each individual's fitness in the population by packing boxes into a truck container. Utilizes the space utilization, the number of boxes packed, and the total value of the boxes as fitness criteria. """ container_vol = self.truck_dimension[0] * self.truck_dimension[1] * self.truck_dimension[2] fitness = {} for key, individual in pop.items(): dblf = [[0, 0, 0] + self.truck_dimension] occupied_vol, number_boxes, value = 0, 0, 0 result = [] # Iterate over each box in the individual's solution (order and rotation). for box_number, r in zip(individual['order'], individual['rotate']): dblf = sorted(dblf, key=itemgetter(3)) dblf = sorted(dblf, key=itemgetter(5)) dblf = sorted(dblf, key=itemgetter(4)) for pos in dblf: current = deepcopy(pos) space_vol = pos[3] * pos[4] * pos[5] box_vol = self.box_params[box_number][3] box_value = self.box_params[box_number][4] if r == 0: l, w, h = self.box_params[box_number][0:3] elif r == 1: w, l, h = self.box_params[box_number][0:3] elif r == 2: l, h, w = self.box_params[box_number][0:3] elif r == 3: h, l, w = self.box_params[box_number][0:3] elif r == 4: h, w, l = self.box_params[box_number][0:3] else: w, h, l = self.box_params[box_number][0:3] # Check if the box fits in the current space. if space_vol >= box_vol and pos[3] >= l and pos[4] >= w and pos[5] >= h: result.append(pos[0:3] + [l, w, h]) occupied_vol += box_vol number_boxes += 1 value += box_value top_space = [pos[0], pos[1], pos[2] + h, l, w, pos[5] - h] beside_space = [pos[0], pos[1] + w, pos[2], l, pos[4] - w, pos[5]] front_space = [pos[0] + l, pos[1], pos[2], pos[3] - l, pos[4], pos[5]] dblf.remove(current) dblf.append(top_space) dblf.append(beside_space) dblf.append(front_space) break ft = [round((occupied_vol / container_vol * 100),2), round((number_boxes / len(self.box_params) * 100),2), round((value / self.total_value * 100),2)] pop[key]['fitness'] = deepcopy(ft) pop[key]['result'] = result fitness[key] = ft return pop, fitness def select_parents(self): """ Selects parents for crossover using tournament selection based on rank and crowding distance. """ parents = {} individuals = deepcopy(list(self.population.values())) for index in range(self.numP): pool = random.sample(individuals, self.k) pool.sort(key=lambda ind: (ind['Rank'])) # Get the highest ranked individual(s) top_rank = pool[0]['Rank'] highest_ranked = [ind for ind in pool if ind['Rank'] == top_rank] if len(highest_ranked) == 1:\ best = highest_ranked[0] else: pool.sort(key=lambda ind: (ind['crowding_distance'])) best = pool[0] parents[index] = best individuals.remove(best) return parents def recombine(self, parents): """ Performs crossover on pairs of parents to generate offspring. Each offspring inherits a combination of 'order' and 'rotate' traits from its parents. """ offsprings = {} parent_keys = list(parents.keys()) random.shuffle(parent_keys) for x in range(0, len(parents), 2): k1 = random.choice(parent_keys) o1 = deepcopy(parents[k1]['order']) r1 = deepcopy(parents[k1]['rotate']) parent_keys.remove(k1) k2 = random.choice(parent_keys) o2 = deepcopy(parents[k2]['order']) r2 = deepcopy(parents[k2]['rotate']) parent_keys.remove(k2) i = random.randint(1, int(len(o1) / 2) + 1) j = random.randint(i + 1, int(len(o1) - 1)) co1, co2 = [-1] * len(o1), [-1] * len(o2) cr1, cr2 = [-1] * len(r1), [-1] * len(r2) co1[i:j + 1], co2[i:j + 1] = o1[i:j + 1], o2[i:j + 1] cr1[i:j + 1], cr2[i:j + 1] = r1[i:j + 1], r2[i:j + 1] pos = (j + 1) % len(o2) for k in range(len(o2)): if o2[k] not in co1 and co1[pos] == -1: co1[pos] = o2[k] pos = (pos + 1) % len(o2) pos = (j + 1) % len(o2) for k in range(len(o1)): if o1[k] not in co2 and co2[pos] == -1: co2[pos] = o1[k] pos = (pos + 1) % len(o1) pos = (j + 1) % len(o2) for k in range(len(r2)): if cr1[pos] == -1: cr1[pos] = r2[k] pos = (pos + 1) % len(r2) pos = (j + 1) % len(o2) for k in range(len(r1)): if cr2[pos] == -1: cr2[pos] = r1[k] pos = (pos + 1) % len(r1) offsprings[x], offsprings[x + 1] = {'order': deepcopy(co1), 'rotate': deepcopy(cr1)}, {'order': deepcopy(co2), 'rotate': deepcopy(cr2)} return offsprings def mutate(self, offsprings): """ Performs mutation on the offspring population to introduce variability. """ for child in offsprings.values(): order = child['order'] rotate = child['rotate'] # First level of mutation: Order Inversion Mutation if random.uniform(0, 1) <= self.pm1: i = random.randint(1, int(len(order) / 2) + 1) j = random.randint(i + 1, int(len(order) - 1)) order[i:j + 1] = order[j:i - 1:-1] rotate[i:j + 1] = rotate[j:i - 1:-1] # Second level of mutation: Rotation Mutation for i in range(len(rotate)): if random.uniform(0, 1) <= self.pm2: rotate[i] = random.randint(0, self.rotation - 1) return offsprings def merge_populations(self, offsprings): """ Merges the current population with the offsprings. """ combined_population = deepcopy(self.population) key = len(combined_population) for _, value in offsprings.items(): combined_population[key] = value key += 1 return combined_population def select_survivors(self, offsprings): """ Selects survivors for the next generation based on NSGA-II sorting and crowding distance. """ offsprings, _ = self.evaluate(offsprings) combined_population = self.merge_populations(offsprings) fronts = self.non_dominated_sort(combined_population) for front in fronts: self.calculate_crowding_distance(combined_population, front) self.population = self.select_based_on_nsga2(combined_population) def non_dominated_sort(self, combined_population): """ Performs non-dominated sorting on the combined population to identify fronts. Returns: list: A list of fronts, where each front is a list of individual keys in that front. """ fronts = [[]] for p_key in combined_population.keys(): p = combined_population[p_key] p['dominated'] = [] p['dom_count'] = 0 for q_key in combined_population.keys(): q = combined_population[q_key] if self.dominates(p['fitness'], q['fitness']): p['dominated'].append(q_key) elif self.dominates(q['fitness'], p['fitness']): p['dom_count'] += 1 if p['dom_count'] == 0: p['Rank'] = 1 fronts[0].append(p_key) i = 0 while fronts[i]: next_front = [] for p_key in fronts[i]: p = combined_population[p_key] for q_key in p['dominated']: q = combined_population[q_key] q['dom_count'] -= 1 if q['dom_count'] == 0: q['Rank'] = i + 2 next_front.append(q_key) i += 1 fronts.append(next_front) return fronts[:-1] def calculate_crowding_distance(self, combined_population, front): """ Calculates the crowding distance for each individual within a front. """ for p_key in front: combined_population[p_key]['crowding_distance'] = 0 for i in range(len(combined_population[next(iter(front))]['fitness'])): # Sort the front based on each objective's value front.sort(key=lambda x: combined_population[x]['fitness'][i]) combined_population[front[0]]['crowding_distance'] = float('inf') combined_population[front[-1]]['crowding_distance'] = float('inf') for j in range(1, len(front) - 1): distance = combined_population[front[j + 1]]['fitness'][i] - combined_population[front[j - 1]]['fitness'][i] combined_population[front[j]]['crowding_distance'] += distance def select_based_on_nsga2(self, combined_population): """ Selects individuals for the next generation based on their rank and crowding distance. """ new_population = {} current_size = 0 i=1 while len(new_population) < self.population_size: group = [ind for ind in combined_population.values() if ind['Rank'] == i] if len(group) <= self.population_size - len(new_population): j = 0 for index in range(len(new_population), len(new_population)+len(group)): new_population[index] = group[j] j += 1 else: group = sorted(group, key=lambda x: x['crowding_distance'], reverse=True) j = 0 for index in range(len(new_population), self.population_size): new_population[index] = group[j] j += 1 i += 1 return new_population def dominates(self, individual1, individual2): """ Determines if one individual dominates another based on fitness values. Returns: bool: True if `individual1` dominates `individual2`, False otherwise. """ better_in_all = all(m >= n for m, n in zip(individual1, individual2)) better_in_at_least_one = any(m > n for m, n in zip(individual1, individual2)) return better_in_all and better_in_at_least_one def calc_average_fitness(self, individuals): fitness_sum = [0.0, 0.0, 0.0] count = 0 for key, value in individuals.items(): if value['Rank'] == 1: count += 1 fitness_sum[0] += value['fitness'][0] fitness_sum[1] += value['fitness'][1] fitness_sum[2] += value['fitness'][2] averaged = [sum_value / count if count > 0 else 0 for sum_value in fitness_sum] self.avg_fitness.append(averaged) def optimize(self): self.population, _ = self.evaluate(self.population) fronts = self.non_dominated_sort(self.population) for front in fronts: self.calculate_crowding_distance(self.population, front) for _ in range(self.generations): parents = self.select_parents() offsprings = self.recombine(parents) offsprings = self.mutate(offsprings) self.select_survivors(offsprings) self.calc_average_fitness(self.population) |
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