A Tabu Search Matheuristic for the Generalized Quadratic Assignment Problem

Peter Greistorfer, Rostislav Stanek, Vittorio Maniezzo

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in Konferenzband

Abstract

This work treats the so-called Generalized Quadratic Assignment Problem. Solution methods are based on heuristic and partially LP-optimizing ideas. Base constructive results stem from a simple 1-pass heuristic and a tree-based branch-and-bound type approach. Then we use a combination of Tabu Search and Linear Programming for the improving phase. Hence, the overall approach constitutes a type of mat- and metaheuristic algorithm. We evaluate the different algorithmic designs and report computational results for a number of data sets, instances from literature as well as own ones. The overall algorithmic performance gives rise to the assumption that the existing framework is promising and worth to be examined in greater detail.
OriginalspracheEnglisch
TitelMetaheuristics
Untertitel14th International Conference, MIC 2022, Syracuse, Italy, July 11–14, 2022, Proceedings
Herausgeber (Verlag)Springer Cham
Seiten544-553
Seitenumfang10
Band13838
Auflage1
ISBN (elektronisch)1611-3349
ISBN (Print)0302-9743
PublikationsstatusVeröffentlicht - 23 Feb. 2023

Publikationsreihe

NameLecture Notes in Computer Science
Herausgeber (Verlag)Springer Cham
Band13838
ISSN (Print)0302-9743
ISSN (elektronisch)1611-3349

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