60 lines
1.8 KiB
Python
60 lines
1.8 KiB
Python
import math
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import random
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from model.base_model import Model
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class MyDPDModel(Model):
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def __init__(self):
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super().__init__()
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def init(self, nodes):
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"""
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Put your initialization here.
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"""
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super().init(nodes)
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def getMST(self, node):
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MST = []
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distances = []
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for i in range(0, self.N):
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if i != node:
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MST.append(i)
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distances.append(self.dist(node, i))
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return [x for _,x in sorted(zip(distances, MST))]
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def fit(self, max_it=1000):
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"""
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Put your iteration process here.
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"""
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MST_solutions = []
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# Depth First: Set one city as starting point, iterate to the end, then select next city as starting point.
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MSTs = []
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for i in range(0, self.N):
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MSTs.append([-1] * self.N)
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for i in range(0, self.N):
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solution = []
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solution.append(i)
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unvisited_list = list(range(0, self.N))
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cur_city = i
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# print("[starting]", i)
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for steps in range(self.N - 1):
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# print(unvisited_list)
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unvisited_list.remove(cur_city)
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if MSTs[cur_city][0] == -1:
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MST = self.getMST(cur_city)
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MSTs[cur_city] = MST
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for j in MSTs[cur_city]:
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if(j in unvisited_list):
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solution.append(j)
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cur_city = j
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break
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# print(solution)
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MST_solutions.append(solution)
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self.fitness_list.append(self.fitness(solution))
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self.best_solution = MST_solutions[ self.fitness_list.index(min(self.fitness_list)) ]
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return self.best_solution, self.fitness_list
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