Solving QAP with Auto-parameterization in Parallel Hybrid Metaheuristics

Duque, Jonathan; Munera, Danny; Díaz, Daniel; Abreu, Salvador

doi:10.1007/978-3-030-85672-4_22

Cited by 2 publications

(1 citation statement)

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“…QAP can be solved by a variety of techniques: small or medium-size instances (i.e., with a few tens of variables) can be solved by exact methods such as dynamic programming, cutting planes, or branch & bound, but for larger instances (over 100 variables) one has to rely on incomplete methods such as approximation algorithms or metaheuristics in order to quickly produce good (but usually suboptimal) solutions (Munera et al, 2016;Ito et al, 2018;Abdelkafi et al, 2019;Fujii et al, 2021). Current research directions, in addition to develop specialized metaheuristics for QAP (Bagherbeik et al, 2020), investigate the hybridization of different metaheuristics by using portfolio approaches with parallelism (Duque et al, 2021).…”

Section: The Quadratic Assignment Problemmentioning

confidence: 99%

Comparing QUBO models for quantum annealing: integer encodings for permutation problems

Codognet

2024

Int Trans Operational Res

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QUBO (quadratic unconstrained binary optimization) has become the modeling language for quantum annealing and quantum‐inspired annealing solvers. We present different approaches in QUBO for the magic square problem and the quadratic assignment problem (QAP), which can be modeled by linear equations and a permutation constraint over integer variables. Different ways of encoding integers by Booleans in QUBO amount to models, the implementation of which could have very different performance. Experiments performed on the Fixstars Amplify Annealer Engine, a quantum‐inspired annealing solver, show that, compared to the classical one‐hot encoding, using unary encoding for integers performs slightly better for the QAP and much better for magic square.

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