A Parallel Computing Framework for Finding the Supported Solutions to a Biobjective Network Optimization Problem
Abstract
Abstract Solving multi‐objective combinatorial optimization problems can quickly become computationally challenging when applied to large networks generated from big data. Present‐day first‐rate Mixed Integer Programming (MIP) solvers have parallelism built‐in to take advantage of multicore architectures; but specialized network optimization algorithms that can often solve graph problems more efficiently than a general MIP solver are typically programmed serially. Thus, these network algorithm implementations do not take full advantage of modern multicore computer capabilities. Many parallel computing languages include a fork/join framework into their concurrency packages that allow for simple conversion of recursive serial algorithms to operate in parallel without having to use complicated low‐level message‐passing interfaces. Fork/join is particularly well suited to divide and conquer algorithms such as Non‐Inferior Set Estimation (NISE) method for computing the supported Pareto front of a multi‐objective optimization problem. This work develops a simple general parallel NISE (pNISE) implementation using the Java 7 concurrency tools, and then offers results from a specific implementation of solving a bi‐objective shortest path problem. The results indicate that this method is capable of sizeable speed‐ups on both small‐scale personal computers as well as large‐scale shared memory supercomputers. Finally, other network problems suitable to this method are discussed, including multi‐objectivemore »
- Authors:
-
- Department of Geography University of California Santa Barbara California USA
- Publication Date:
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1401385
- Resource Type:
- Publisher's Accepted Manuscript
- Journal Name:
- Journal of Multi-Criteria Decision Analysis
- Additional Journal Information:
- Journal Name: Journal of Multi-Criteria Decision Analysis Journal Volume: 22 Journal Issue: 5-6; Journal ID: ISSN 1057-9214
- Publisher:
- Wiley Blackwell (John Wiley & Sons)
- Country of Publication:
- Country unknown/Code not available
- Language:
- English
Citation Formats
Medrano, Fernando Antonio, and Church, Richard Lee. A Parallel Computing Framework for Finding the Supported Solutions to a Biobjective Network Optimization Problem. Country unknown/Code not available: N. p., 2015.
Web. doi:10.1002/mcda.1541.
Medrano, Fernando Antonio, & Church, Richard Lee. A Parallel Computing Framework for Finding the Supported Solutions to a Biobjective Network Optimization Problem. Country unknown/Code not available. https://doi.org/10.1002/mcda.1541
Medrano, Fernando Antonio, and Church, Richard Lee. Tue .
"A Parallel Computing Framework for Finding the Supported Solutions to a Biobjective Network Optimization Problem". Country unknown/Code not available. https://doi.org/10.1002/mcda.1541.
@article{osti_1401385,
title = {A Parallel Computing Framework for Finding the Supported Solutions to a Biobjective Network Optimization Problem},
author = {Medrano, Fernando Antonio and Church, Richard Lee},
abstractNote = {Abstract Solving multi‐objective combinatorial optimization problems can quickly become computationally challenging when applied to large networks generated from big data. Present‐day first‐rate Mixed Integer Programming (MIP) solvers have parallelism built‐in to take advantage of multicore architectures; but specialized network optimization algorithms that can often solve graph problems more efficiently than a general MIP solver are typically programmed serially. Thus, these network algorithm implementations do not take full advantage of modern multicore computer capabilities. Many parallel computing languages include a fork/join framework into their concurrency packages that allow for simple conversion of recursive serial algorithms to operate in parallel without having to use complicated low‐level message‐passing interfaces. Fork/join is particularly well suited to divide and conquer algorithms such as Non‐Inferior Set Estimation (NISE) method for computing the supported Pareto front of a multi‐objective optimization problem. This work develops a simple general parallel NISE (pNISE) implementation using the Java 7 concurrency tools, and then offers results from a specific implementation of solving a bi‐objective shortest path problem. The results indicate that this method is capable of sizeable speed‐ups on both small‐scale personal computers as well as large‐scale shared memory supercomputers. Finally, other network problems suitable to this method are discussed, including multi‐objective variants of the minimum spanning tree problem, transportation problem, assignment problem, maximum flow problem, and the minimum‐cost flow problem. Copyright © 2015 John Wiley & Sons, Ltd.},
doi = {10.1002/mcda.1541},
journal = {Journal of Multi-Criteria Decision Analysis},
number = 5-6,
volume = 22,
place = {Country unknown/Code not available},
year = {Tue Apr 07 00:00:00 EDT 2015},
month = {Tue Apr 07 00:00:00 EDT 2015}
}
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