Hierarchical model parallel memetic algorithm in heterogeneous computing environment
Abstract
Distributed computing environments offer vast amounts of computational power for use in parallel memetic algorithms. However, they consist of heterogeneous computing nodes, in terms of computational power, operating platform, network connectivity and latency. The behavior of parallel memetic algorithms in such environment is poorly understood: the vast majority of current parallel MAs assumes homogeneous environment. To deal with the heterogeneity of the computing resources, a hierarchical model PMA (hPMA-DLS) is proposed to provide the speed-up regardless of the heterogeneity in the distributed environment while preserving the standard behavior of the PMA. The empirical study on several large scale quadratic assignment problems (QAPs) shows that hPMA-DLS can enhance the efficiency of the island model PMA-DLS [22] search without deterioration in the solution quality.
Authors: Jing Tang, M.H. Lim, Yew-Soon Ong, L. Q. Song
Published in: IEEE Congress on Evolutionary Computation (CEC) (2007)