A parallel tabu search for the unconstrained binary quadratic programming problem

Jialong Shi (1) , Qingfu Zhang (1) , Bilel Derbel (2) , Arnaud Liefooghe (2)

1 CUHK - City University of Hong Kong [Hong Kong]
2 DOLPHIN - Parallel Cooperative Multi-criteria Optimization

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 D2TS, 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 D2TS processes.

Domaines: Recherche opérationnelle [math.OC]; Optimisation et contrôle [math.OC]

Jialong Shi, Qingfu Zhang, Bilel Derbel, Arnaud Liefooghe. A parallel tabu search for the unconstrained binary quadratic programming problem. IEEE Congress on Evolutionary Computation (CEC 2017), Jun 2017, Donostia - San Sebastián, Spain. pp.557-564. ⟨hal-01581361⟩