Abstract
The Traveling Salesman Problem (TSP) aims at finding the shortest trip for a salesman, who has to visit each of the locations from a given set exactly once, starting and ending at the same location. Here, we consider the Euclidean version of the problem, in which the locations are points in the two-dimensional Euclidean space and the distances are correspondingly Euclidean distances. We propose simple, fast, and easily implementable heuristics that work well, in practice, for large real-life problem instances. The algorithm works on three phases, the constructive, the insertion, and the improvement phases. The first two phases run in time O ( n 2 ) and the number of repetitions in the improvement phase, in practice, is bounded by a small constant. We have tested the practical behavior of our heuristics on the available benchmark problem instances. The approximation provided by our algorithm for the tested benchmark problem instances did not beat best known results. At the same time, comparing the CPU time used by our algorithm with that of the earlier known ones, in about 92% of the cases our algorithm has required less computational time. Our algorithm is also memory efficient: for the largest tested problem instance with 744,710 cities, it has used about 50 MiB, whereas the average memory usage for the remained 217 instances was 1.6 MiB.
Highlights
The Traveling Salesman Problem (TSP) is one of the most studied strongly NP-hard combinatorial optimization problems
We have tested the performance of CII-algorithm for 85 benchmark instances from TSPLIB [16]
Parameter “Error” specifies the approximation factor of algorithm H compared to cost of the best known solution (C(BKS)): C ( Best Known Solution (BKS)) − C ( TH )
Summary
The Traveling Salesman Problem (TSP) is one of the most studied strongly NP-hard combinatorial optimization problems. The cost of a tour is the sum of the distances between each pair of the neighboring cities in that tour. This problem can be described in graph terms. We have an undirected weighted complete graph G = (V, E), where V is the set of n = |V | vertices (cities) and E is the set of the n2 − n edges (i, j) = ( j, i ), i 6= j. There are two basic sets of restrictions that define feasible solution (a tour that has to start and complete at the same vertex and has to contain all the vertices from set V exactly once).
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