TY - JOUR AV - none Y1 - 2013/// KW - cluster; distribution; Finite -difference time domains (FDTD); hybrid; Hybrid genetic algorithms; Optimization procedures; optimized; Stagewise optimizations KW - Finite difference time domain method; Optimization; Personal computers KW - Genetic algorithms A1 - Zakaria, N. A1 - Pal, A.J. A1 - Shah, S.N.M. SP - 2303 VL - 9 N1 - cited By 4 TI - Stagewise optimization of distributed clustered finite difference time domain (FDTD) using genetic algorithm SN - 13494198 IS - 6 JF - International Journal of Innovative Computing, Information and Control EP - 2326 UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84880069910&partnerID=40&md5=2934c6d7b08141aba5295b259d994966 N2 - In this paper, we explore the use of hybrid genetic algorithm for optimized clustering and distribution of Finite Difference Time Domain (FDTD) computation over a large number of desktop computers and servers. Given a large number of computers, we first attempt to compute an optimal set of clusters. The clustering takes into consideration similarity of machine capability plus the interconnection speed. It considers as well the predicted availability pattern for each computer. Then, for each cluster, we optimize the distribution of FDTD workload over its computers. Hence, the overall optimization procedure optimizes clustered distributed FDTD. We show in this paper how pure as well as hybrid genetic algorithm can effectively be used to perform the optimization. © 2013 ICIC International. ID - scholars3551 ER -