摘要
标准遗传算法存在收敛速度慢、过早成熟等缺点。借鉴生物免疫系统中抗体注射免疫的理论,提出了一种基于抗体注射的新型免疫遗传算法(AIGA)。该算法在保留标准遗传算法随机全局搜索能力的基础上,引进了生物免疫系统的免疫应答、抗体注射、免疫选择等机制。结合TSP问题,给出了示范抗体的提取和注射方法,并给出了算法收敛性的理论证明。最后,用AIGA算法对100个城市的TSP问题进行了仿真计算,并将其计算过程与标准遗传算法进行了对比,结果表明该算法能有效地改善遗传算法的不成熟收敛缺陷,使收敛的速度有较大的提高。
The novel genetic algorithm has disadvantages such as slow astringency,precocity.A new Immune Genetic Algorithm based on Antibody（AIGA） is proposed with analogies to the theory of antibody injection immunity.Based on the search ability of novel genetic algorithm,immunity response,antibody injection and immunity selection of biological immune systems are introduced into AIGA.The method of antibody distilling and injecting for TSP is given,and the convergence of AIGA is approved theoretically.A simulation test of 100-city TSP is done with AIGA,and its computational process is compared with that of novel genetic algorithms.The results show that AIG can alleviate the disadvantage of precocity and improve the speed of astringency evidently.
出处
《计算机工程与应用》
CSCD
北大核心
2009年第36期29-31,34,共4页
Computer Engineering and Applications
基金
国家自然科学基金No.60575037
No.60502043
河南省自然科学基金No.082400440260~~
关键词
遗传算法
免疫算法
抗体注射
收敛性
旅行商问题(TSP)
genetic algorithm immune algorithm antibody injection convergence Traveling Salesman Problem（TSP）
