Title
A parallel and distributed meta-heuristic framework based on partially ordered knowledge sharing
Abstract
We propose a new distributed and parallel meta-heuristic framework to address the issues of scalability and robustness in the optimization problem. The proposed framework, named PADO (Parallel And Distributed Optimization framework), can utilize heterogeneous computing and communication resources to achieve scalable speedup while maintaining high solution quality. Specifically, we combine an existing meta-heuristic framework with a loosely coupled distributed island model for scalable parallelization. Based on a mature sequential optimization framework, we implement a population-based meta-heuristic algorithm with an island model for parallelization. The coordination overhead of previous approaches is significantly reduced by using a partially ordered knowledge sharing (POKS) model as an underlying model for distributed computing. The resulting framework can encompass many meta-heuristic algorithms and can solve a wide variety of problems with minimal configuration. We demonstrate the applicability and the performance of the framework with a traveling salesman problem (TSP), multi-objective design space exploration (DSE) problem of an embedded multimedia system, and a drug scheduling problem of cancer chemotherapy.
Year
DOI
Venue
2012
10.1016/j.jpdc.2012.01.007
J. Parallel Distrib. Comput.
Keywords
Field
DocType
knowledge sharing,optimization framework,drug scheduling problem,proposed framework,existing meta-heuristic framework,mature sequential optimization framework,optimization problem,resulting framework,meta-heuristic algorithm,parallel meta-heuristic framework,island model
Population,Computer science,Parallel computing,Symmetric multiprocessor system,Robustness (computer science),Travelling salesman problem,Design space exploration,Optimization problem,Speedup,Distributed computing,Scalability
Journal
Volume
Issue
ISSN
72
4
0743-7315
Citations 
PageRank 
References 
10
0.65
23
Authors
5
Name
Order
Citations
PageRank
Jinwoo Kim11918168.52
Minyoung Kim2564.14
Mark-oliver Stehr337729.62
Hyunok Oh445740.49
Soonhoi Ha51684174.65