Abstract

The traveling salesman problem (TSP) has been widely studied for the classical closed-loop variant. However, very little attention has been paid to the open-loop variant. Most of the existing studies also focus merely on presenting the overall optimization results (gap) or focus on processing time, but do not reveal much about which operators are more efficient to achieve the result. In this paper, we present two new operators (link swap and 3–permute) and study their efficiency against existing operators, both analytically and experimentally. Results show that while 2-opt and relocate contribute equally in the closed-loop case, the situation changes dramatically in the open-loop case where the new operator, link swap, dominates the search; it contributes by 50% to all improvements, while 2-opt and relocate have a 25% share each. The results are also generalized to tabu search and simulated annealing.

Highlights

  • The traveling salesman problem (TSP) aims to find the shortest tour for a salesperson to visit N number of cities

  • The gap is the length of the optimum path divided by the length of the tour found by the local search (0% indicates optimum)

  • 2-opt and relocate both have about a 50% share of all improvements in the closed-loop case (TSPLIB), they are inferior to the new link swap operator in case of open-loop test sets (O–Mopsi and Dots)

Read more

Summary

Introduction

The traveling salesman problem (TSP) aims to find the shortest tour for a salesperson to visit N number of cities. Finding the optimum order by minimizing the tour length corresponds to solving open-loop TSP [5], because the players are not required to return to the start position. The optimum tour is still needed for reference when analyzing the performance of the players This is is usually made asisa still post-game but can happen during real-time of play. Along with the exact solver, a faster heuristic algorithm is needed to is fast enough to produce the optimum tour. Along with the exact solver, a faster heuristic algorithm is needed to quickly, inBesides spite of the danger TSP of occasionally resulting in a sub-optimal solution. The O–Mopsi dataset contains real-world TSP instances and the Dots dataset is a computer-generated random dataset for an experimental computer game.

Local Search
Relocate
Two-Optimization
Three-Node
Link Swap
The Uniqueness of the Operators
Performance of a Single Operator
Initial Solution
Search Strategy
Results With A Single Operator
Combining the Operators
16. Typical
Processing Time
Parameter Values
The Productivity of the Operators
Stochastic Variants
Discussion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call