3-opt

In optimization, 3-opt is a simple local search algorithm for solving the travelling salesman problem and related network optimization problems[1].

3-opt analysis involves deleting 3 connections (or edges) in a network (or tour), to create 3 sub-tours. Then the 7 different ways of reconnecting the network are analysed to find the optimum one. This process is then repeated for a different set of 3 connections, until all possible combinations have been tried in a network. A single execution of 3-opt has a time complexity of [2]. Iterated 3-opt has a higher time complexity.

This is the mechanism by which the 3-opt swap manipulates a given route:

def reverse_segment_if_better(tour, i, j, k):
    "If reversing tour[i:j] would make the tour shorter, then do it."
    # Given tour [...A-B...C-D...E-F...]
    A, B, C, D, E, F = tour[i-1], tour[i], tour[j-1], tour[j], tour[k-1], tour[k % len(tour)]
    d0 = distance(A,B) + distance(C,D) + distance(E,F)
    d1 = distance(A,C) + distance(B,D) + distance(E,F)
    d2 = distance(A,B) + distance(C,E) + distance(D,F)
    d3 = distance(A,D) + distance(E,B) + distance(C,F)
    d4 = distance(F,B) + distance(C,D) + distance(E,A)

    if d0 > d1:
      tour[i:j] = reversed(tour[i:j])
      return -d0 + d1
    elif d0 > d2:
      tour[j:k] = reversed(tour[j:k])
      return -d0 + d2
    elif d0 > d4:
      tour[i:k] = reversed(tour[i:k])
      return -d0 + d4
    elif d0 > d3:
      tmp = tour[j:k], tour[i:j]
      tour[i:k] = tmp
      return -d0 + d3
    return 0

The principle is pretty simple. You compute, the original distance and you compute the cost of each modification. If you find a better cost, apply the modification and return (relative cost). This is the complete 3-opt swap making use of the above mechanism:

def three_opt(tour):
    "Iterative improvement based on 3 exchange."
    delta = 0
    for (a,b,c) in all_segments(len(tour)):
        delta += reverse_segment_if_better(tour, a, b, c)

    if delta < 0:
        return three_opt(tour)
    return tour

def all_segments(N):
    "Generate all segments combinations"
    return [(i, j, k)
        for i in range(N)
        for j in range(i+2, N)
        for k in range(j+2, N+(i>0))]

For the given tour, you generate all segments combinations and for each combinations, you try to improve the tour by reversing segments. While you find a better result, you restart the process.

See also

References

  1. ↑ Munim, Ziaul Haque; Haralambides, Hercules (2018). "Competition and cooperation for intermodal container transhipment: A network optimization approach". Research in Transportation Business & Management. 26: 87–99. doi:10.1016/j.rtbm.2018.03.004.
  2. ↑ Blazinskas, Andrius; Misevicius, Alfonsas (2011). "Combining 2-OPT, 3-OPT and 4-OPT with K-SWAP-KICK perturbations for the traveling salesman problem" (PDF).
  • F. BOCK (1965). An algorithm for solving traveling-salesman and related network optimization problems. unpublished manuscript associated with talk presented at the 14th ORSA National Meeting.
  • S. LIN (1965). Computer solutions of the traveling salesman problem. Bell Syst. Tech. J. 44, 2245-2269. Available as PDF
  • S. LIN AND B. W. KERNIGHAN (1973). An Effective Heuristic Algorithm for the Traveling-Salesman Problem. Operations Res. 21, 498-516. Available as PDF
  • Local Search Heuristics. (n.d.) Retrieved June 16, 2008, from http://www.tmsk.uitm.edu.my/~naimah/csc751/slides/LS.pdf%5Bpermanent+dead+link%5D
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