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

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• 作者：Jinwoo Kim^a ; ^{jwkim@iris.snu.ac.kr}  ; [Author Vitae] ; Minyoung Kim^b ; ^{mkim@csl.sri.com}  ; [Author Vitae] ; Mark-Oliver Stehr^b ; ^{stehr@csl.sri.com}  ; [Author Vitae] ; Hyunok Oh^c ; ^{hoh@hanyang.ac.kr}  ; [Author Vitae] ; Soonhoi Ha^a ; ^{sha@snu.ac.kr}  ; [Author Vitae]
• 关键词：Parallel and distributed optimization framework ; Meta-heuristic ; Knowledge sharing ; Design space exploration
• 刊名：Journal of Parallel and Distributed Computing
• 出版年：2012
• 期刊代码：188_07437315
• 类别：et
• 出版时间：April, 2012
• 卷：72
• 期：4
• 页码：564-578
• 文件大小：1654 K

摘要

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.