Hybridizing grey wolf optimization with differential evolution for global optimization and test sche

• 作 者: Aijun Zhu Chuanpei Xu Zhi Li J
• 机 构: SchoolofMechano-ElectronicEngineering,SchoolofElectronicEngineeringandAutomation,GuangxiKeyLaborator
• 出 处: 系统工程与电子技术：英文版
• 发表日期: 2015年2期
• 关键词: meta-heuristic global optimization NP HARD problem. meta-heuristic global opti
• 基金项目: 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 Resear

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 evo- lution （HGWO