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

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.This work was supported by the BK21 project, Acceleration Research sponsored by KOSEF research program (R17-2007-086-01001-0), and Basic Science Research Program through theNational Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2010-0005982). The ICT at Seoul National University provided research facilities for this study. Additional support from National Science Foundation Grant 0932397 (A Logical Framework for Self-Optimizing Networked Cyber–Physical Systems) is gratefully acknowledged. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of NSF