A Parallel Tabu Search for the Unconstrained Binary Quadratic Programming problem

Although several sequential heuristics have been proposed for dealing with the Unconstrained Binary Quadratic Programming (UBQP), very little effort has been made for designing parallel algorithms for the UBQP. This paper propose a novel decentralized parallel search algorithm, called Parallel Elite...

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Bibliographic Details
Published in:	2017 IEEE Congress on Evolutionary Computation (CEC) pp. 557 - 564
Main Authors:	Jialong Shi, Qingfu Zhang, Derbel, Bilel, Liefooghe, Arnaud
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.06.2017
Subjects:	Algorithm design and analysis Heuristic algorithms Memetics Parallel algorithms Quadratic programming Sociology Statistics
Online Access:	Get full text
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Summary:	Although several sequential heuristics have been proposed for dealing with the Unconstrained Binary Quadratic Programming (UBQP), very little effort has been made for designing parallel algorithms for the UBQP. This paper propose a novel decentralized parallel search algorithm, called Parallel Elite Biased Tabu Search (PEBTS). It is based on D 2 TS, a state-of-the-art sequential UBQP metaheuristic. The key strategies in the PEBTS algorithm include: (i) a lazy distributed cooperation procedure to maintain diversity among different search processes and (ii) finely tuned bit-flip operators which can help the search escape local optima efficiently. Our experiments on the Tianhe-2 supercomputer with up to 24 computing cores show the accuracy of the efficiency of PEBTS compared with a straightforward parallel algorithm running multiple independent and non-cooperating D 2 TS processes.
DOI:	10.1109/CEC.2017.7969360