自适应滑动窗算法航班排序的代码

以下是一个示例的自适应滑动窗算法航班排序的 Python 代码：

def adaptive_sliding_window_sort(flights, window_size=5, tolerance=2):
    """
    Sorts a list of flights using an adaptive sliding window algorithm.
    :param flights: A list of Flight objects to be sorted
    :param window_size: The size of the sliding window (default is 5)
    :param tolerance: The tolerance level for the window (default is 2)
    :return: A sorted list of Flight objects
    """
    sorted_flights = [flights[0]]  # Start with the first flight already sorted
    for i in range(1, len(flights)):
        curr_flight = flights[i]
        # Find the best position for the current flight in the sorted list
        best_pos = find_best_position(sorted_flights, curr_flight, window_size, tolerance)
        # Insert the current flight into the sorted list at the best position
        sorted_flights.insert(best_pos, curr_flight)
    return sorted_flights


def find_best_position(sorted_flights, curr_flight, window_size, tolerance):
    """
    Finds the best position for the current flight in the sorted list using a sliding window algorithm.
    :param sorted_flights: A list of Flight objects already sorted
    :param curr_flight: The current Flight object to be inserted into the sorted list
    :param window_size: The size of the sliding window
    :param tolerance: The tolerance level for the window
    :return: The index of the best position for the current flight in the sorted list
    """
    best_pos = len(sorted_flights)  # Start with the end of the sorted list as the best position
    min_distance = float('inf')  # Start with the maximum possible distance
    for i in range(len(sorted_flights) - window_size, len(sorted_flights)):
        # Calculate the distance between the current flight and each flight in the sliding window
        distance = calc_distance(curr_flight, sorted_flights[i])
        # If the distance is within the tolerance level and smaller than the minimum distance so far,
        # update the minimum distance and the best position accordingly
        if distance <= tolerance and distance < min_distance:
            min_distance = distance
            best_pos = i
    return best_pos


def calc_distance(flight1, flight2):
    """
    Calculates the distance between two Flight objects based on their departure and arrival times.
    :param flight1: The first Flight object
    :param flight2: The second Flight object
    :return: The absolute difference between the departure time of flight1 and the arrival time of flight2
    """
    return abs(flight1.departure_time - flight2.arrival_time)


class Flight:
    """
    A simple Flight class with departure and arrival times.
    """
    def __init__(self, departure_time, arrival_time):
        self.departure_time = departure_time
        self.arrival_time = arrival_time

该算法的主要思路是，从前往后遍历航班列表，对于每个航班，找到在已排序的航班列表中最适合它的位置，并将其插入该位置。找到最适合位置的方法是使用滑动窗口算法，在已排序的航班列表中取最后 $n$ 个航班作为滑动窗口，并计算当前航班与滑动窗口中每个航班的时间差，如果时间差小于等于容忍度 $t$，则认为当前航班可以插入到这个位置。在滑动窗口中找到多个可插入位置时，选择时间差最小的位置作为最适合位置。

注意，该算法中的航班类 `Flight` 只包含了起飞时间和降落时间两个属性，实际应用中可能需要更多信息。另外，该算法只考虑了航班的起飞时间和降落时间，没有考虑到其他因素（如航班时长、中转时间等）可能对排序的影响。因此，该算法只是一个简单的示例，实际应用时需要根据具体情况进行调整和改进。