Solving QAP with Auto-parameterization in Parallel Hybrid Metaheuristics - Archive ouverte HAL Access content directly
Journal Articles Communications in Computer and Information Science Year : 2021

Solving QAP with Auto-parameterization in Parallel Hybrid Metaheuristics

, , (1) ,
1
Jonathan Duque
Danny Múnera
Daniel Díaz
Salvador Abreu

Abstract

The Quadratic Assignment Problem (QAP) is one of the most challenging combinatorial optimization problems with many reallife applications. Currently, the best solvers are based on hybrid and parallel metaheuristics, which are actually highly complex and parametric methods. Finding the best set of tuning parameters for such methods is a tedious and error-prone task. In this paper, we propose a strategy for auto-parameterization of QAP solvers. We show evidence that autoparameterization can further improve the quality of computed solutions. Our auto-parameterization scheme relieves the user from having to find the right parameters while providing a high quality solution.
Not file

Dates and versions

hal-03947253 , version 1 (19-01-2023)

Identifiers

Cite

Jonathan Duque, Danny Múnera, Daniel Díaz, Salvador Abreu. Solving QAP with Auto-parameterization in Parallel Hybrid Metaheuristics. Communications in Computer and Information Science, 2021, Communications in Computer and Information Science, 1443, pp.294-309. ⟨10.1007/978-3-030-85672-4_22⟩. ⟨hal-03947253⟩
0 View
0 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More