Optimizing Fuzzy Petri Nets by Using an Improved Ant Colony Algorithm
Abstract: It is very important
for constructing a FPN (fuzzy petri net) to accurately find out all parameters
of fuzzy production rules. In this paper, Maximum-Minimum Ant System (MMAS) of
ant colony algorithm(ACA) is originally introduced into the process of
exploring the optimal parameters of a modified FPN.The optimization algoritnm
is based on the techniques of multithreading. Realization of the algorithm do
notdepend on experiential data and requirements for the initial input of the
FPN are not stringent. Simulation experiment shows that the parameters trained
by the above MMAS multithreading algorithm are highly accurate and the FPN
model constructed by these parameters possesses strong generalizing capability and
self-adjusting purpose.
Author: Li Yang, Yue Xiao-bo
Journal Code: jptkomputergg160105
