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A parallel and distributed meta-heuristic framework based on partially ordered knowledge sharing

Title
A parallel and distributed meta-heuristic framework based on partially ordered knowledge sharing
Author
오현옥
Keywords
Parallel and distributed optimization framework; Meta-heuristic; Knowledge sharing; Design space exploration
Issue Date
2012-01
Publisher
Elsevier Science B.V., Amsterdam
Citation
Journal of Parallel and Distributed Computing, Vol.72, No.4 [2012], p564-578
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.
URI
http://www.sciencedirect.com/science/article/pii/S0743731512000184?via%3Dihubhttp://hdl.handle.net/20.500.11754/67767
ISSN
0743-7315
DOI
10.1016/j.jpdc.2012.01.007
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > INFORMATION SYSTEMS(정보시스템학과) > Articles
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