# Travelling Salesman Problem implementation using BackTracking

** Travelling Salesman Problem (TSP):** Given a set of cities and distance between every pair of cities, the problem is to find the shortest possible route that visits every city exactly once and returns back to the starting point.

Note the difference between **Hamiltonian Cycle** and TSP. The Hamiltoninan cycle problem is to find if there exist a tour that visits every city exactly once. Here we know that Hamiltonian Tour exists (because the graph is complete) and in fact many such tours exist, the problem is to find a minimum weight Hamiltonian Cycle.

For example, consider the graph shown in the figure. A TSP tour in the graph is 1 -> 2 -> 4 -> 3 -> 1. The cost of the tour is 10 + 25 + 30 + 15 which is 80.

The problem is a famous NP hard problem. There is no polynomial time know solution for this problem.

Output of Given Graph:

Minimum weight Hamiltonian Cycle : 10 + 20 + 30 + 15 = 80

**Approach:** In this post, implementation of simple solution is discussed.

- Consider city 1 (let say 0th node) as the starting and ending point. Since route is cyclic, we can consider any point as starting point.
- Start traversing from the source to its adjacent nodes in dfs manner.
- Calculate cost of every traversal and keep track of minimum cost and keep on updating the value of minimum cost stored value.
- Return the permutation with minimum cost.

Below is the implementation of the above approach:

## C++

`// C++ implementation of the approach ` `#include <bits/stdc++.h> ` `using` `namespace` `std; ` `#define V 4 ` ` ` `// Function to find the minimum weight Hamiltonian Cycle ` `void` `tsp(` `int` `graph[][V], vector<` `bool` `>& v, ` `int` `currPos, ` ` ` `int` `n, ` `int` `count, ` `int` `cost, ` `int` `& ans) ` `{ ` ` ` ` ` `// If last node is reached and it has a link ` ` ` `// to the starting node i.e the source then ` ` ` `// keep the minimum value out of the total cost ` ` ` `// of traversal and "ans" ` ` ` `// Finally return to check for more possible values ` ` ` `if` `(count == n && graph[currPos][0]) { ` ` ` `ans = min(ans, cost + graph[currPos][0]); ` ` ` `return` `; ` ` ` `} ` ` ` ` ` `// BACKTRACKING STEP ` ` ` `// Loop to traverse the adjacency list ` ` ` `// of currPos node and increasing the count ` ` ` `// by 1 and cost by graph[currPos][i] value ` ` ` `for` `(` `int` `i = 0; i < n; i++) { ` ` ` `if` `(!v[i] && graph[currPos][i]) { ` ` ` ` ` `// Mark as visited ` ` ` `v[i] = ` `true` `; ` ` ` `tsp(graph, v, i, n, count + 1, ` ` ` `cost + graph[currPos][i], ans); ` ` ` ` ` `// Mark ith node as unvisited ` ` ` `v[i] = ` `false` `; ` ` ` `} ` ` ` `} ` `}; ` ` ` `// Driver code ` `int` `main() ` `{ ` ` ` `// n is the number of nodes i.e. V ` ` ` `int` `n = 4; ` ` ` ` ` `int` `graph[][V] = { ` ` ` `{ 0, 10, 15, 20 }, ` ` ` `{ 10, 0, 35, 25 }, ` ` ` `{ 15, 35, 0, 30 }, ` ` ` `{ 20, 25, 30, 0 } ` ` ` `}; ` ` ` ` ` `// Boolean array to check if a node ` ` ` `// has been visited or not ` ` ` `vector<` `bool` `> v(n); ` ` ` `for` `(` `int` `i = 0; i < n; i++) ` ` ` `v[i] = ` `false` `; ` ` ` ` ` `// Mark 0th node as visited ` ` ` `v[0] = ` `true` `; ` ` ` `int` `ans = INT_MAX; ` ` ` ` ` `// Find the minimum weight Hamiltonian Cycle ` ` ` `tsp(graph, v, 0, n, 1, 0, ans); ` ` ` ` ` `// ans is the minimum weight Hamiltonian Cycle ` ` ` `cout << ans; ` ` ` ` ` `return` `0; ` `} ` |

*chevron_right*

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## Python3

# Python3 implementation of the approach

V = 4

answer = []

# Function to find the minimum weight

# Hamiltonian Cycle

def tsp(graph, v, currPos, n, count, cost):

# If last node is reached and it has

# a link to the starting node i.e

# the source then keep the minimum

# value out of the total cost of

# traversal and “ans”

# Finally return to check for

# more possible values

if (count == n and graph[currPos][0]):

answer.append(cost + graph[currPos][0])

return

# BACKTRACKING STEP

# Loop to traverse the adjacency list

# of currPos node and increasing the count

# by 1 and cost by graph[currPos][i] value

for i in range(n):

if (v[i] == False and graph[currPos][i]):

# Mark as visited

v[i] = True

tsp(graph, v, i, n, count + 1,

cost + graph[currPos][i])

# Mark ith node as unvisited

v[i] = False

# Driver code

# n is the number of nodes i.e. V

if __name__ == ‘__main__’:

n = 4

graph= [[ 0, 10, 15, 20 ],

[ 10, 0, 35, 25 ],

[ 15, 35, 0, 30 ],

[ 20, 25, 30, 0 ]]

# Boolean array to check if a node

# has been visited or not

v = [False for i in range(n)]

# Mark 0th node as visited

v[0] = True

# Find the minimum weight Hamiltonian Cycle

tsp(graph, v, 0, n, 1, 0)

# ans is the minimum weight Hamiltonian Cycle

print(min(answer))

# This code is contributed by mohit kumar

**Output:**

80

## Recommended Posts:

- Travelling Salesman Problem | Set 2 (Approximate using MST)
- Travelling Salesman Problem | Set 1 (Naive and Dynamic Programming)
- Traveling Salesman Problem (TSP) Implementation
- m Coloring Problem | Backtracking-5
- N Queen Problem | Backtracking-3
- The Knight's tour problem | Backtracking-1
- Word Break Problem using Backtracking
- Exact Cover Problem and Algorithm X | Set 2 (Implementation with DLX)
- Subset Sum | Backtracking-4
- Rat in a Maze | Backtracking-2
- Sudoku | Backtracking-7
- Backtracking | Introduction
- Hamiltonian Cycle | Backtracking-6
- Magnet Puzzle | Backtracking-9
- Rat in a Maze | Backtracking using Stack

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