Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

7-2022

Abstract

There are two major challenges to solving constrained optimization problems using a QuadraticUnconstrained Binary Optimization or QUBO solver (QS). First, we need to tune both the underlyingproblem parameters and the algorithm parameters. Second, the solution returned from a QSmight not be feasible. While it is common to use automated tuners such as SMAC and Hyperopt totune the algorithm parameters, the initial search ranges input for the auto tuner affect the performanceof the QS. In this paper, we propose a framework that resembles the Algorithm Selection(AS) framework to tune algorithm parameters for an annealing-based QS. To cope with constraints,we focus on permutation-based combinatorial optimization problems, since computing the projectionto the feasible space for this class of problems can be done efficiently; and for simplicity, the numberof problem parameters can be reduced to one and we fix it. Methodologically, we train a recommendationsystem to to learn good annealing problem parameter ranges. During testing, we search forgood hyperparameter values using a recommendation system approach. To illustrate our approach experimentally, we use the Fujitsu Digital Annealer as our QUBO solver and Optuna as the auto tuner tosolve the Traveling Salesman Problem.

Discipline

Artificial Intelligence and Robotics | Systems Architecture

Research Areas

Intelligent Systems and Optimization

Publication

NASO 2022 proceedings: Workshop on New Architectures for Search and Optimization a satellite workshop of IJCAI-ECAI 2022, Vienna, Austria, July 24

First Page

22

Last Page

29

City or Country

Vienna. Austria

Additional URL

https://khaverna.synology.me:4857/d/s/pahRcMX4g2tZ7cjXCdn6LkvMAohxGH19/xnK5cZID9sreG0SSNIwclmQTGrjBzemV-QbzA4Xc7sQk

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