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并行人工免疫系统的塔式主从模型

发表时间:2011-04-30  浏览量:1400  下载量:446
全部作者: 戚玉涛,刘芳
作者单位: 西安电子科技大学计算机学院
摘 要: 提出并行人工免疫系统的塔式主从模型(tower-like master-slave model,TMSM)及并行免疫记忆克隆选择算法(parallel immune memory clonal selection algorithm, PIMCSA)。TMSM是粗粒度的两层并行人工免疫模型,该设计体现了分布式的免疫响应和免疫记忆机制。PIMCSA用疫苗的迁移代替了抗体的迁移,兼顾了种群多样性的保持和算法的收敛速度。对函数优化问题和旅行商问题(travelling saleman problem, TSP)的仿真结果表明,PIMCSA无论在求解精度还是在运行时间上都有很好的表现。
关 键 词: 模式识别;并行人工免疫系统; 克隆选择; 函数优化; 旅行商问题
Title: Tower-like master-slave model for parallel artificial immune system
Author: QI Yutao, LIU Fang
Organization: School of Computer Science and Technology, Xidian University
Abstract: This paper presents a tower-like master-slave model (TMSM) for parallel artificial immune systems. Based on TMSM, the parallel immune memory clonal selection algorithm (PIMCSA) is also proposed. TMSM is a two level coarse-grained parallel artificial immune model with distributed immune response and distributed immune memory. In PIMCSA, vaccines are extracted and migrated between populations rather than antibodies as has been done in parallel genetic algorithms, it is a good balance between the diversity maintenance of populations and the convergent speed of the algorithm. Experimental results on the function optimization and travelling travelling saleman problems (TSP) show that PIMCSA achieves good performance in terms of both solution quality and computation time.
Key words: pattern recognition; parallel artificial immune system; clone selection; function optimization; travelling saleman problem
发表期数: 2011年4月第8期
引用格式: 戚玉涛,刘芳. 并行人工免疫系统的塔式主从模型[J]. 中国科技论文在线精品论文,2011,4(8):683-690.
 
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