Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2023

Abstract

The traveling salesman problem (TSP) is the most well-known problem in combinatorial optimization which hasbeen studied for many decades. This paper focuses on dealing with one of the most difficult TSP variants named thequadratic traveling salesman problem (QTSP) that has numerous planning applications in robotics and bioinformatics.The goal of QTSP is similar to TSP which finds a cycle visiting all nodes exactly once with minimum total costs. However, the costs in QTSP are associated with three vertices traversed in succession (instead of two like in TSP). This leadsto a quadratic objective function that is much harder to solve.To efficiently solve the problem, we propose a hybrid geneticalgorithm including a local search procedure for intensification and a new mutation operator for diversification. The local search is composed of a restricted double-bridge move(a variant of 4-Opt); and we show the neighborhood can beevaluated in O(n2), the same complexity as for the classicalTSP. The mutation phase is inspired by a ruin-and-recreatescheme. Experimental results conducted on benchmark instances show that our method significantly outperforms state-of-the-art algorithms in terms of solution quality. Out of the800 instances tested, it finds 437 new best-known solutions.

Keywords

Benchmarking, Combinatorial optimization, Genetic algorithms, Local search (optimization), Robot programming

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems

Research Areas

Data Science and Engineering; Information Systems and Management; Intelligent Systems and Optimization

Publication

33rd International Conference on Automated Planning and Scheduling (ICAPS-2023), Prague, Czech Republic, July 2023

Identifier

10.1609/icaps.v33i1.27212

Publisher

Association for the Advancement of Artificial Intelligence

City or Country

Prague, Czech Republic

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