High-level hybridization of heuristics and metaheuristics to solve symmetric TSP: a comparative study

TL;DR

This study evaluates high-level hybridizations of eight metaheuristics with heuristics for solving the symmetric TSP, comparing their effectiveness without parameter tuning across problems with 50 to 280 cities.

Contribution

It provides a comparative analysis of various hybrid metaheuristic-heuristic combinations for symmetric TSP, highlighting the impact of different search mechanisms.

Findings

Certain hybrid combinations outperform existing solutions.

Metaheuristic search patterns significantly influence solution quality.

Parameter tuning is unnecessary due to inherent search pattern diversity.

Abstract

The Travelling Salesman Problem - TSP is one of the most explored problems in the scientific literature to solve real problems regarding the economy, transportation, and logistics, to cite a few cases. Adapting TSP to solve different problems has originated several variants of the optimization problem with more complex objectives and different restrictions. Metaheuristics have been used to solve the problem in polynomial time. Several studies have tried hybridising metaheuristics with specialised heuristics to improve the quality of the solutions. However, we have found no study to evaluate whether the searching mechanism of a particular metaheuristic is more adequate for exploring hybridization. This paper focuses on the solution of the classical TSP using high-level hybridisations, experimenting with eight metaheuristics and heuristics derived from k-OPT, SISR, and segment intersection search, resulting in twenty-four combinations. Some combinations allow more than one set of searching parameters. Problems with 50 to 280 cities are solved. Parameter tuning of the metaheuristics is not carried out, exploiting the different searching patterns of the eight metaheuristics instead. The solutions' quality is compared to those presented in the literature.