Application of ant colony algorithm in parameters optimization of fuzzy Petri nets
蚁群算法在模糊Petri网参数优化中的应用研究

How to determine the various parameters of fuzzy production rules is significant to the built up of fuzzy Petri net (FPN), which has not been solved yet. Maximum-minimum ant system (MMAS) of ant colony algorithm (ACA) was originally introduced into the procedure of exploring parameters of FPN. An optimization algorithm based on the techniques of multithreading was proposed. Realization of the algorithm did not depend on experiential data and no strict requirements for primary input were needed. Simulation shows that the trained parameters gained from above MMAS are highly accurate and the resultant FPN model possesses strong generalizing and self-adjusting capabilities.