采用差分进化算法优化模糊Petri网参数

J4 ›› 2014, Vol. 36 ›› Issue (06): 1095-1100.

采用差分进化算法优化模糊Petri网参数

张弛1，乐晓波1，周恺卿2，莫礼平3

（1.长沙理工大学计算机与通信工程学院，湖南长沙 410114;
2.马来西亚理工大学计算机科学与信息系统系，柔佛州新山市马来西亚 81310；
3.吉首大学信息科学与工程学院，湖南吉首 416000）

收稿日期:2012-10-29 修回日期:2013-03-20 出版日期:2014-06-25 发布日期:2014-06-25
基金资助:
国家自然科学基金资助项目(61170199)；湖南省自然科学基金资助项目(08JJ3124)

Optimizing parameters of fuzzy Petri net
using differential evolution algorithm

ZHANG Chi1，YUE Xiaobo1，ZHOU Kaiqing2,MO Liping3

(1.Department of Computer&Communication Engineering,Changsha University of Science and Technology,Changsha 410114,China;
2.Faculty of Computing,Univeristi Teknologi Malaysia,UTM Skudai, Johor 81310,Malaysia;
3.College of Information Science & Engineering,Jishou University,Jishou 416000,China)

Received:2012-10-29 Revised:2013-03-20 Online:2014-06-25 Published:2014-06-25

摘要/Abstract

摘要：

如何有效地确定模糊Petri网（FPN）的各项参数、摆脱自学习能力差的缺点，一直是悬而未决的问题。针对此问题，将差分进化算法首次引入到FPN参数优化中，根据FPN的实际特征，提出了一种改进的差分进化算法。算法采用混沌策略产生初始种群，融合自适应变异因子及早熟惩罚策略提高种群多样性，同时保证很强的收敛性与全局性。仿真实验表明，将改进的差分进化算法与传统算法相比较，收敛到理想参数值的速度提高了5倍。

关键词: 模糊Petri网, 模糊推理, 改进的差分进化算法, 早熟惩罚

Abstract:

It is significant and being unsolved yet for building a Fuzzy Petri Net (FPN) so as to get rid of the shortcomings of poor selflearning ability. To address this problem, differential evolution algorithm is originally introduced into the procedure of exploring parameters of FPN. According to the actual characteristics of FPN, an improved differential evolution algorithm is proposed. The algorithm utilizes the chaotic strategy to generate initial population and integrates selfadaptive factors with precocious punishment strategies as a result of enhancing the diversity of population, while ensuring being strong convergent and global. Simulation experiment shows that the trained parameters gained from the proposed algorithm are 5 times accurate than any other traditional algorithms.

Key words: fuzzy Petri net(FPN);fuzzy reasoning;improved differential evolution algorithm;precocious punishment

张弛1，乐晓波1，周恺卿2，莫礼平3. 采用差分进化算法优化模糊Petri网参数[J]. J4, 2014, 36(06): 1095-1100.

ZHANG Chi1，YUE Xiaobo1，ZHOU Kaiqing2,MO Liping3. Optimizing parameters of fuzzy Petri net
using differential evolution algorithm [J]. J4, 2014, 36(06): 1095-1100.

参考文献

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附中文参考文献：
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