Parameter Optimizing for Fuzzy Petri Net Based on Improved Artificial Immune Algorism

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Parameter Optimizing for Fuzzy Petri Net Based on Improved Artificial Immune Algorism

更新时间：2026-09-04

- Parameter Optimizing for Fuzzy Petri Net Based on Improved Artificial Immune Algorism
- Vol. 30, Issue 9, Pages: 102-105(2013)
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- Published：2013
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基于改进人工免疫算法的模糊Petri网参数优化[J]. 微电子学与计算机, 2013, 30(9): 102-105.

Parameter Optimizing for Fuzzy Petri Net Based on Improved Artificial Immune Algorism[J]. 2013, 30(9): 102-105.
基于改进人工免疫算法的模糊Petri网参数优化[J]. 微电子学与计算机, 2013, 30(9): 102-105. DOI：

Parameter Optimizing for Fuzzy Petri Net Based on Improved Artificial Immune Algorism[J]. 2013, 30(9): 102-105. DOI：

摘要

提出了一种基于改进人工免疫算法的模糊Petri网参数优化算法。首先，对模糊Petri网和产生式规则进行了定义和描述，然后，设计了抗体编码方式、亲和度评估函数和模拟退火免疫选择算子，以实现对经典人工免疫算法的改进，并定义了基于此改进人工免疫算法对参数进行优化的具体算法。仿真实验表明，文中方法能较为准确地实现参数优化，得到的优化结果与期望值具有较小的均方误差，且与其他方法相比，具有较快的全局收敛速度和较强的全局寻优能力，具有很强的通用性。

Abstract

An algorism based on artificial immune algorism for obtaining the optimum parameters was proposed. Firstly

the fuzzy petri net and generating rules were defined and described

and then the coding method of antibody

Affinity evaluation function and Simulated Annealing immune selection operator are designed to improve the classic artificial immune algorism.The specific algorism based on this improved artificial algorism was defined. The simulation experiment shows the method in this paper can accurately realize the parameters optimizing and has the litter square error

compared with the other methods

our method has the quick global convergence rate

optimizing ability and strong versatility.

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