import random

def local_search(items, capacity):
  """
  Solves the knapsack problem using a local search approach.

  Args:
    items: A list of items, where each item is a tuple of (value, weight).
    capacity: The capacity of the knapsack.

  Returns:
    A list of items that are included in the knapsack.
  """

  solution = []
  for i in range(len(items)):
    solution.append(items[i])

  while True:
    best_solution = solution
    for i in range(len(items)):
      new_solution = solution[:]
      new_solution.remove(items[i])
      new_solution.append(items[random.randint(0, len(items) - 1)])

      if evaluate_solution(new_solution, capacity) > evaluate_solution(best_solution, capacity):
        best_solution = new_solution

    if best_solution == solution:
      break

    solution = best_solution

  return solution


def evaluate_solution(solution, capacity):
  """
  Evaluates a solution to the knapsack problem.

  Args:
    solution: A list of items that are included in the knapsack.
    capacity: The capacity of the knapsack.

  Returns:
    The value of the solution.
  """

  value = 0
  weight = 0
  for item in solution:
    value += item[0]
    weight += item[1]

  if weight > capacity:
    return 0

  return value


def main():
  items = [(10, 5), (20, 10), (30, 15)]
  capacity = 20

  solution = local_search(items, capacity)
  print("The following items are included in the knapsack:")
  for item in solution:
    print(item)


if __name__ == "__main__":
  main()
# This code first defines a function called local_search(). This function takes two arguments: items and capacity. items is a list of items, where each item is a tuple of (value, weight). capacity is the capacity of the knapsack.

#The local_search() function then works by iteratively improving the solution. At each iteration, the function randomly removes one item from the solution and then randomly adds another item to the solution. The function then evaluates the new solution and keeps it if it is better than the old solution.

#The local_search() function then returns the best solution that it finds.

#The evaluate_solution() function works by calculating the value of a solution. The value of a solution is the sum of the values of the items that are included in the solution.

#The main() function then calls the local_search() function with the items and capacity. The main() function then prints the list of items that are included in the best solution.

#To run this code, you can save it as a Python file and then run it from the command line. For example, if you save the code as local_search.py, you can run it by typing the following command into the command line:
