Tabu Search with two approaches to parallel flowshop evaluation on CUDA platform

Authors:
Michał Czapiński;Stuart Barnes
Affiliations:
-;-
Venue:
Journal of Parallel and Distributed Computing
Year:
2011

Citing 6
Cited 3

An iterated local search algorithm for the permutation flowshop problem with total flowtime criterion

Computers and Operations Research
An estimation of distribution algorithm for minimizing the total flowtime in permutation flowshop scheduling problems

Computers and Operations Research
Solving the flow shop problem by parallel programming

Journal of Parallel and Distributed Computing
Two ant-colony algorithms for minimizing total flowtime in permutation flowshops

Computers and Industrial Engineering - Special issue: Selected papers from the 30th international conference on computers; industrial engineering
Parallel scatter search algorithm for the flow shop sequencing problem

PPAM'07 Proceedings of the 7th international conference on Parallel processing and applied mathematics
Parallel Simulated Annealing with Genetic Enhancement for flowshop problem with Csum

Computers and Industrial Engineering

A metaheuristic framework for stochastic combinatorial optimization problems based on GPGPU with a case study on the probabilistic traveling salesman problem with deadlines

Journal of Parallel and Distributed Computing
Parallelization strategies for hybrid metaheuristics using a single GPU and multi-core resources

PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
An effective Parallel Multistart Tabu Search for Quadratic Assignment Problem on CUDA platform

Journal of Parallel and Distributed Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

The introduction of NVidia's powerful Tesla GPU hardware and Compute Unified Device Architecture (CUDA) platform enable many-core parallel programming. As a result, existing algorithms implemented on a GPU can run many times faster than on modern CPUs. Relatively little research has been done so far on GPU implementations of discrete optimisation algorithms. In this paper, two approaches to parallel GPU evaluation of the Permutation Flowshop Scheduling Problem, with makespan and total flowtime criteria, are proposed. These methods can be employed in most population-based algorithms, e.g. genetic algorithms, Ant Colony Optimisation, Particle Swarm Optimisation, and Tabu Search. Extensive computational experiments, on Tabu Search for Flowshop with both criteria, followed by statistical analysis, confirm great computational capabilities of GPU hardware. A GPU implementation of Tabu Search runs up to 89 times faster than its CPU counterpart.