Title
A metaheuristic framework for stochastic combinatorial optimization problems based on GPGPU with a case study on the probabilistic traveling salesman problem with deadlines
Abstract
In this work we propose a general metaheuristic framework for solving stochastic combinatorial optimization problems based on general-purpose computing on graphics processing units (GPGPU). This framework is applied to the probabilistic traveling salesman problem with deadlines (PTSPD) as a case study. Computational studies reveal significant improvements over state-of-the-art methods for the PTSPD. Additionally, our results reveal the huge potential of the proposed framework and sampling-based methods for stochastic combinatorial optimization problems.
Year
DOI
Venue
2013
10.1016/j.jpdc.2012.05.004
J. Parallel Distrib. Comput.
Keywords
Field
DocType
general metaheuristic framework,sampling-based method,proposed framework,stochastic combinatorial optimization problem,salesman problem,huge potential,significant improvement,general-purpose computing,case study,computational study,monte carlo sampling,gpgpu
Graphics,Stochastic optimization,Mathematical optimization,Computer science,CUDA,Combinatorial optimization,Cross-entropy method,Theoretical computer science,General-purpose computing on graphics processing units,Optimization problem,Metaheuristic
Journal
Volume
Issue
ISSN
73
1
0743-7315
Citations 
PageRank 
References 
3
0.39
23
Authors
3
Name
Order
Citations
PageRank
Dennis Weyland11088.43
Roberto Montemanni264344.25
Luca Maria Gambardella37926726.40