A Parallel Iterated Local Search Algorithm on GPUs for Quadratic Assignment Problem

Year 2018, Volume: 4 Issue: 2, 124 - 128, 27.06.2018

Erdener Özçetin Gürkan Öztürk

Abstract

In this study, quadratic assignment
problem, which is a hard combinatorial optimization problem, is examined to
solve by a new approach. To reach the optimal results by using mathematical
programming approaches cannot be possible even for some sorts of small and
middle scaled problems in a reasonable time interval. Huge amounts of data are
being progressed simultaneously by graphics processing units located on
computers’ graphics card. Therefore, a parallel iterated local search algorithm
has been proposed to solve the quadratic assignment problem by using graphics
processing units’ simultaneously progressing property. This parallel algorithm
and the sequential one on central processing units are tested and compared for test
problems in literature. Indeed, it is observed that the parallel algorithm
works averagely 6.31 times faster for Skorin problems and 11.93 times faster for
Taillard problems faster than sequentially one.

Keywords

Quadratic assignment problem (QAP), local search algorithms, graphics processing units (GPU), CUDA

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