Note: Execute software for MATLAB software
(2) Based on the interval number DEMATEL method to verify the causal
relationships among the four variables in the theoretical framework
preliminarily, the differential evolution algorithm is further adopted.
Based on the collected sample of 132 teams and 520 team members, this
study searches for outstanding individual samples globally, so that the
excellent sample which will enter the next generation of population can
be used as empirical sample of SEM based on Bayesian estimation.
The main purpose of the differential evolution algorithm is to find out
the optimal solution vector, which can search for the excellent
individual sample with good fault tolerance and strong learning ability
as a whole, so that the better individuals with strong learning ability
can enter the next generation group, and thus play the optimization and
global search capability (Liyanan etc., 2016; Guohaixiang etc., 2014;
Deb, 2000). Firstly, based on the differential evolution algorithm, this
study uses MATLAB and Stata software to set the execution parameters (as
shown in Table 4), and searches for excellent individual sample with
good fault tolerance and strong learning ability by the whole, so that
the better individuals with strong learning ability can enter the next
generation group(Liyanan etc., 2016; Guohaixiang etc., 2014; Deb, 2000).
This ensures that the excellent sample which will enter the next
generation of population can be used as empirical sample of SEM based on
Bayesian estimation exert to optimization and global search function,
and explore the causal relationships among the four variables. Secondly,
this study reveals the mechanism among the simmelian ties, high
performance work practice system, and knowledge fermenting. Finally, the
transmission mechanism of the simmelian ties on the knowledge spiral
with the high performance work practice system and knowledge fermenting
as the conduction variables are revealed.
The relevant principles, modeling steps, and processes of the
differential evolution algorithm are as follows(Liyanan etc., 2016;
Guohaixiang etc., 2014; Deb, 2000):
The differential evolution algorithm includes four basic operations of
initializing population, variation, cross, and selection. The modeling
steps and processes of the differential evolution algorithm are as
follows(Jinweijian,Huhanhui, 2011; Gaopeiran,Luxinyuan, 2014;Jiangqian
etc., 2016):