An adaptive k-opt method for solving traveling salesman problem

摘要

This paper presents a new heuristic solution to the traveling salesman problem (TSP). Inspired by an existing technique that employs the task swap mechanism to solve the multi-agent task allocation, we exploit the adaptive k-swap based searching process and take into account the newly introduced subtour constraint, and propose a new variant of k-opt method for incrementally improving suboptimal but feasible TSP tours. Different from existing k-opt methods, a unique feature of the proposed method is that the parameter k is adjusted adaptively as the tour improvement proceeds. We show that by combining with existing TSP approximation techniques, the hybrid approaches can further improve the solution quality with negligible extra running time.