116 lines
4.8 KiB
Python
116 lines
4.8 KiB
Python
#!/usr/bin/env python
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from datetime import datetime, timedelta
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import math
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import numpy as np
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import random
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import bisect
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import itertools
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from aman.sys.aco.Configuration import Configuration
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from aman.sys.aco.RunwayManager import RunwayManager
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from aman.sys.aco.Node import Node
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# This class implements a single ant of the following paper:
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# https://sci-hub.mksa.top/10.1109/cec.2019.8790135
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class Ant:
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def __init__(self, pheromoneTable : np.array, configuration : Configuration, nodes):
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self.Configuration = configuration
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self.Nodes = nodes
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self.RunwayManager = RunwayManager(self.Configuration)
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self.InboundSelected = [ False ] * len(self.Nodes)
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self.InboundScore = np.zeros([ len(self.Nodes), 1 ])
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self.PheromoneMatrix = pheromoneTable
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self.SequenceDelay = timedelta(seconds = 0)
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self.Sequence = None
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# Implements function (5)
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def heuristicInformation(self, current : int):
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_, eta, _ = self.RunwayManager.selectArrivalRunway(self.Nodes[current], self.Configuration.EarliestArrivalTime)
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inboundDelay = eta - self.Nodes[current].Inbound.InitialArrivalTime
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# calculate the fraction with a mix of the unused runway time and the delay of single aircrafts
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heuristic = inboundDelay.total_seconds() / 60.0
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heuristic = (1.0 / (heuristic or 1)) ** self.Configuration.Beta
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return heuristic
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# Implements functions (3), (6)
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def selectNextLandingIndex(self):
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q = float(random.randint(0, 100)) / 100
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weights = []
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if q <= self.Configuration.PseudoRandomSelectionRate:
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for i in range(0, len(self.InboundSelected)):
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if False == self.InboundSelected[i]:
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weights.append(self.heuristicInformation(i))
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else:
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# roulette selection strategy
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pheromoneScale = 0.0
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for i in range(0, len(self.InboundSelected)):
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if False == self.InboundSelected[i]:
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pheromoneScale += self.heuristicInformation(i)
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for i in range(0, len(self.InboundSelected)):
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if False == self.InboundSelected[i]:
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weights.append(self.heuristicInformation(i) / (pheromoneScale or 1))
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total = sum(weights)
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cumdist = list(itertools.accumulate(weights)) + [total]
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candidateIndex = bisect.bisect(cumdist, random.random() * total)
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for i in range(0, len(self.InboundSelected)):
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if False == self.InboundSelected[i]:
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if 0 == candidateIndex:
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return i
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else:
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candidateIndex -= 1
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return None
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def associateInbound(self, node : Node, earliestArrivalTime : datetime):
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# prepare the statistics
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rwy, eta, _ = self.RunwayManager.selectArrivalRunway(node, self.Configuration.EarliestArrivalTime)
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eta = max(earliestArrivalTime, eta)
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node.Inbound.PlannedRunway = rwy
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node.Inbound.PlannedStar = node.ArrivalCandidates[rwy.Name].Star
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node.Inbound.PlannedArrivalTime = eta
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node.Inbound.PlannedArrivalRoute = node.ArrivalCandidates[rwy.Name].ArrivalRoute
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node.Inbound.InitialArrivalTime = node.ArrivalCandidates[rwy.Name].InitialArrivalTime
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self.RunwayManager.RunwayInbounds[rwy.Name] = node
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delay = node.Inbound.PlannedArrivalTime - node.Inbound.InitialArrivalTime
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if 0.0 < delay.total_seconds():
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return delay, rwy
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else:
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return timedelta(seconds = 0), rwy
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def findSolution(self, first : int):
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self.Sequence = []
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# select the first inbound
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self.InboundSelected[first] = True
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delay, _ = self.associateInbound(self.Nodes[first], self.Configuration.EarliestArrivalTime)
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self.Sequence.append(first)
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self.SequenceDelay += delay
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while 1:
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index = self.selectNextLandingIndex()
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if None == index:
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break
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self.InboundSelected[index] = True
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delay, _ = self.associateInbound(self.Nodes[index], self.Configuration.EarliestArrivalTime)
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self.SequenceDelay += delay
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self.Sequence.append(index)
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# update the local pheromone
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update = (1.0 - self.Configuration.propagationRatio) * self.PheromoneMatrix[self.Sequence[-2], self.Sequence[-1]]
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update += self.Configuration.propagationRatio * self.Configuration.ThetaZero
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self.PheromoneMatrix[self.Sequence[-2], self.Sequence[-1]] = max(self.Configuration.ThetaZero, update)
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# validate that nothing went wrong
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if len(self.Sequence) != len(self.Nodes):
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self.SequenceDelay = None
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self.Sequence = None
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