Paper Info
Title
A Generic Solver Based On Functional Parallelism For Solving Combinatorial Optimization Problems
Abstract
This paper proposes a new class of parallel branch-and-bound (B&B) schemes. The main idea of the scheme is to focus on the functional parallelism instead of conventional data parallelism, and to support such a heterogeneous and irregular parallelism by using a collection of autonomous agents distributed over the network. After examining several implementation issues, we describe a detail of the prototype system implemented over eight PC's connected by a network. The result of experiments conducted over the prototype system indicates that the proposed parallel processing scheme significantly improves the performance of the underlying B&B scheme by adaptively switching exploring policies adopted by each agent participating to the problem solving.
Year
DOI
Venue
2006
10.1093/ietisy/e89-d.6.1940
IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
Keywords
Field
DocType
combinatorial optimization problem, autonomous agents, parallel branch-and-bound, winner determination problem
Instruction-level parallelism,Autonomous agent,Implicit parallelism,Task parallelism,Computer science,Software agent,Theoretical computer science,Combinatorial optimization,Data parallelism,Solver
Journal
Volume
Issue
ISSN
E89D
6
1745-1361
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Shigeaki Tagashira19428.39
Masaya Mito2193.01
Satoshi Fujita34618.99