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Hybridizing grey wolf optimization with differential evolution for global optimization and test scheduling for 3D stacked SoC

查看全文 作　　者：Aijun [1]Zhu;Chuanpei [2,3]Xu;Zhi [1,2,3,4]Li;Jun [2]Wu;Zhenbing [2]Liu 高影响力作者 机构地区：[1]School of Mechano-Electronic Engineering, Xidian University;[2]School of Electronic Engineering and Automation, Guilin University of Electronic Technology;[3]Guangxi Key Laboratory of Automatic Detecting Technology and Instruments;[4]Guilin University of Aerospace Technology高影响力机构 出　　处：《Journal of Systems Engineering and Electronics》索引2015年第26卷第2期,共12页高影响力期刊 基　　金：supported by the National Natural Science Foundation of China(60766001;61105004);the Guangxi Key Laboratory of Automatic Detecting Technology and Instruments(YQ14110);the Program for Innovative Research Team of Guilin University of Electronic Technology(IRTGUET) 摘　　要：A new meta-heuristic method is proposed to enhance current meta-heuristic methods for global optimization and test scheduling for three-dimensional(3D) stacked system-on-chip(SoC) by hybridizing grey wolf optimization with differential evolution(HGWO). Because basic grey wolf optimization(GWO) is easy to fall into stagnation when it carries out the operation of attacking prey, and differential evolution(DE) is integrated into GWO to update the previous best position of grey wolf Alpha, Beta and Delta, in order to force GWO to jump out of the stagnation with DE's strong searching ability. The proposed algorithm can accelerate the convergence speed of GWO and improve its performance.Twenty-three well-known benchmark functions and an NP hard problem of test scheduling for 3D SoC are employed to verify the performance of the proposed algorithm. Experimental results show the superior performance of the proposed algorithm for exploiting the optimum and it has advantages in terms of exploration. 关 键 词：meta-heuristic, global optimization, NP HARD problem.
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