#!/usr/bin/env python3
"""Auto-generated by AGI Loop cycle #1277
Task: ,

Write a Python function that takes a list of truck routes and calculates the optimal route that minimizes total distance, using a simplified version of the traveling salesman problem algorithm.
Generated: 2026-02-13T02:38:56.706633
"""

import math
import random

def calculate_distance(point1, point2):
    return math.sqrt((point1[0] - point2[0])**2 + (point1[1] - point2[1])**2)

def total_distance(route, points):
    distance = 0
    for i in range(len(route) - 1):
        distance += calculate_distance(points[route[i]], points[route[i+1]])
    distance += calculate_distance(points[route[-1]], points[route[0]])
    return distance

def optimize_route(points, iterations=1000):
    num_points = len(points)
    route = list(range(num_points))
    best_route = route[:]
    best_distance = total_distance(best_route, points)

    for _ in range(iterations):
        # Randomly swap two cities
        i, j = random.sample(range(num_points), 2)
        route[i], route[j] = route[j], route[i]
        current_distance = total_distance(route, points)

        if current_distance < best_distance:
            best_route = route[:]
            best_distance = current_distance
        else:
            # Revert swap
            route[i], route[j] = route[j], route[i]

    return best_route, best_distance

def main():
    # Example data: list of points (x, y)
    points = [(0, 0), (1, 2), (3, 1), (5, 3), (4, 6), (2, 5)]
    optimized_route, distance = optimize_route(points)
    print("Optimized Route Indices:", optimized_route)
    print("Total Distance:", distance)
    print("Route in order:")
    for idx in optimized_route:
        print(points[idx], end=" -> ")
    print(points[optimized_route[0]])

if __name__ == '__main__':
    main()