| | Data: an instance of the DCMST problem using degree . |
| | Result: a feasible solution and its objective function value. |
| | If () then |
| | Execute Algorithm 2 using parameters |
| | Save feasible solution found and its objective function value |
| | Else |
| | Classical random local search strategy: |
| | Generate an initial random tuple of vertices. Let denote this set of vertices and its complement, |
| | Execute Algorithm 2 using parameters |
| | Save the initial feasible solution found and its objective function value |
| , |
| , , |
| | While () do |
| |
| | For to do |
| | Interchange randomly an element of with an element of |
| , execute Algorithm 2 using parameters |
| | If (a better solution is obtained) then |
| | Save the new solution and set , |
| , , , , |
| | Else |
| , , |
| | If () then |
| |
| | If () then |
| |
| | Else |
| |
| | Return best feasible solution obtained and its objective function value |
