30 research outputs found
GPU-accelerated Parallel Solutions to the Quadratic Assignment Problem
The Quadratic Assignment Problem (QAP) is an important combinatorial
optimization problem with applications in many areas including logistics and
manufacturing. QAP is known to be NP-hard, a computationally challenging
problem, which requires the use of sophisticated heuristics in finding
acceptable solutions for most real-world data sets.
In this paper, we present GPU-accelerated implementations of a 2opt and a
tabu search algorithm for solving the QAP. For both algorithms, we extract
parallelism at multiple levels and implement novel code optimization techniques
that fully utilize the GPU hardware. On a series of experiments on the
well-known QAPLIB data sets, our solutions, on average run an
order-of-magnitude faster than previous implementations and deliver up to a
factor of 63 speedup on specific instances. The quality of the solutions
produced by our implementations of 2opt and tabu is within 1.03% and 0.15% of
the best known values. The experimental results also provide key insight into
the performance characteristics of accelerated QAP solvers. In particular, the
results reveal that both algorithmic choice and the shape of the input data
sets are key factors in finding efficient implementations.Comment: 25 pages, 9 figures; parts of this work appeared as short papers in
XSEDE14 and XSEDE15 conferences. This version of the paper is a substantial
extension of previous work with optimizations for newer GPU platforms and
extended experimental result
PGAGrid: A Parallel Genetic Algorithm of Fine-Grained implemented on GPU to find solutions near the optimum to the Quadratic Assignment Problem (QAP)
This work consists in implementing a fine-grained parallel genetic algorithm improved with a greedy 2-opt heuristic to find near-optimal solutions to the Quadratic Assignment Problem (QAP). The proposed algorithm was fully implemented on Graphics Processing Units (GPUs). A two-dimensional GPU grid of size 8x8 defines the population of the genetic algorithm (set of permutations of the QAP), and each GPU block consists of n GPU threads, where n is the size of the QAP. Each GPU block was used to represent the chromosome of a single individual, and each GPU thread represents a gene of such chromosome. The proposed algorithm was tested on a subset of the standard QAPLIB data set. Results show that this implementation is able to find good solutions for large QAP instances in few parallel iterations of the evolutionary process.Resumen: Este trabajo consiste en implementar un algoritmo genético paralelo de grano fino mejorado con una heurÃstica 2-opt voraz para encontrar soluciones cercanas al óptimo al problema de Asignación Cuadrática (QAP). El algoritmo propuesto fue completamente implementado sobre Unidades de Procesamiento Gráfico (GPUs). Una retÃcula GPU bidimensional de tamaño 8×8 define la población del algoritmo genético (conjunto de permutaciones del QAP) y cada bloque GPU consiste de n hilos GPU donde n es el tamaño del QAP. Cada bloque GPU fue utilizado para representar el cromosoma de un solo individuo y cada hilo GPU representa un gen de tal cromosoma. El algoritmo propuesto fue comprobado sobre un subconjunto de problemas de la librerÃa estándar QAPLIB. Los resultados muestran que esta implementación es capaz de encontrar buenas soluciones para grandes instancias del QAP en pocas iteraciones del proceso evolutivo.Doctorad
Recent Advances on GPU Computing in Operations Research
Abstract-In the last decade, Graphics Processing Units (GPUs) have gained an increasing popularity as accelerators for High Performance Computing (HPC) applications. Recent GPUs are not only powerful graphics engines but also highly threaded parallel computing processors that can achieve sustainable speedup as compared with CPUs. In this context, researchers try to exploit the capability of this architecture to solve difficult problems in many domains in science and engineering. In this article, we present recent advances on GPU Computing in Operations Research. We focus in particular on Integer Programming and Linear Programming
Recent Advances on GPU Computing in Operations Research
In the last decade, Graphics Processing Units (GPUs) have gained an increasing popularity as accelerators for High Performance Computing (HPC) applications. Recent GPUs are not only powerful graphics engines but also highly threaded parallel computing processors that can achieve sustainable speedup as compared with CPUs. In this context, researchers try to exploit the capability of this architecture to solve difficult problems in many domains in science and engineering. In this article, we present recent advances on GPU Computing in Operations Research. We focus in particular on Integer Programming and Linear Programming
Parallel hybrid chicken swarm optimization for solving the quadratic assignment problem
In this research, we intend to suggest a new method based on a parallel hybrid chicken swarm optimization (PHCSO) by integrating the constructive procedure of GRASP and an effective modified version of Tabu search. In this vein, the goal of this adaptation is straightforward about the fact of preventing the stagnation of the research. Furthermore, the proposed contribution looks at providing an optimal trade-off between the two key components of bio-inspired metaheuristics: local intensification and global diversification, which affect the efficiency of our proposed algorithm and the choice of the dependent parameters. Moreover, the pragmatic results of exhaustive experiments were promising while applying our algorithm on diverse QAPLIB instances . Finally, we briefly highlight perspectives for further research
An improved Ant Colony System for the Sequential Ordering Problem
It is not rare that the performance of one metaheuristic algorithm can be
improved by incorporating ideas taken from another. In this article we present
how Simulated Annealing (SA) can be used to improve the efficiency of the Ant
Colony System (ACS) and Enhanced ACS when solving the Sequential Ordering
Problem (SOP). Moreover, we show how the very same ideas can be applied to
improve the convergence of a dedicated local search, i.e. the SOP-3-exchange
algorithm. A statistical analysis of the proposed algorithms both in terms of
finding suitable parameter values and the quality of the generated solutions is
presented based on a series of computational experiments conducted on SOP
instances from the well-known TSPLIB and SOPLIB2006 repositories. The proposed
ACS-SA and EACS-SA algorithms often generate solutions of better quality than
the ACS and EACS, respectively. Moreover, the EACS-SA algorithm combined with
the proposed SOP-3-exchange-SA local search was able to find 10 new best
solutions for the SOP instances from the SOPLIB2006 repository, thus improving
the state-of-the-art results as known from the literature. Overall, the best
known or improved solutions were found in 41 out of 48 cases.Comment: 30 pages, 8 tables, 11 figure