Реализация функции в алгоритме локального поиска ⇐ Python

[Anonymous]

Я пытаюсь реализовать алгоритм локального поиска. Я добился некоторого прогресса, но проблема все еще остается. Мне нужны решения, которые бы учитывали ограничения предшественников и преемников, и я не знаю, как это сделать, поскольку я новичок в Python. Не могли бы вы мне помочь?
Спасибо!

Код: Выделить всё

import random

# Example list of starting times and durations
StartingTime = [0,5,10,15,20,10,0]
p = [0,15,20,5,10,7,0]  # Durations of each task
nPrecs = 5;
pred = [1,1,2,3,5]; #predecessors
succ = [3,4,5,4,6]; #successors

# Objective function: minimize makespan (total time to complete all tasks)
def objective_function(start_times, durations):
end_times = [start_times[i] + durations[i] for i in range(len(start_times))]
return max(end_times)  # The makespan is the maximum end time

# Generate neighbors: create new schedules by swapping starting times of tasks
def generate_neighbors(start_times):
neighbors = []
for i in range(len(start_times)):
for j in range(i + 1, len(start_times)):
new_start_times = start_times[:]
new_start_times[i], new_start_times[j] = new_start_times[j], new_start_times[i]
neighbors.append(new_start_times)
return neighbors

# Local search algorithm (hill climbing) with swapping method
def local_search(start_times, durations):
current_solution = start_times
current_value = objective_function(current_solution, durations)

while True:
neighbors = generate_neighbors(current_solution)
next_solution = None
next_value = float('inf')

print(f"Evaluating neighbors of solution: {current_solution} with objective value {current_value}")
for neighbor in neighbors:
value = objective_function(neighbor, durations)
print(f"Neighbor solution: {neighbor} with objective value {value}")
if value < next_value:
next_solution = neighbor
next_value = value

if next_value >= current_value:
break  # No better neighbor found, local optimum reached

current_solution = next_solution
current_value = next_value
print(f"Improved solution: {current_solution} with objective value {current_value}")

return current_solution, current_value

# Generate initial solution by randomly swapping starting times
def generate_initial_solution(start_times):
initial_solution = start_times[:]
random.shuffle(initial_solution)
return initial_solution

# Running the local search algorithm
initial_solution = generate_initial_solution(StartingTime)
print(f"Initial solution: {initial_solution}")

best_solution, best_value = local_search(initial_solution, p)
print(f"Best solution: {best_solution} with objective value {best_value}")

Я пытаюсь реализовать для этого функцию, но она не работает.

Подробнее здесь: https://stackoverflow.com/questions/785 ... -algorithm