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:
- 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
- Sudoku | Backtracking-7
- Rat in a Maze | Backtracking-2
- Backtracking | Introduction
- Hamiltonian Cycle | Backtracking-6
- Magnet Puzzle | Backtracking-9
- Backtracking to find all subsets
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