Journal of Systems Engineering and Electronics

• SYSTEMS ENGINEERING • Previous Articles     Next Articles

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

Aijun Zhu1, Chuanpei Xu2,3,*, Zhi Li1,2,3,4, JunWu2, and Zhenbing Liu2   

  1. 1. School of Mechano-Electronic Engineering, Xidian University, Xi’an 710071, China;
    2. School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China;
    3. Guangxi Key Laboratory of Automatic Detecting Technology and Instruments, Guilin 541004, China;
    4. Guilin University of Aerospace Technology, Guilin 541004, China
  • Online:2015-04-21 Published:2010-01-03


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.

Key words: meta-heuristic, global optimization, NP hard problem