摘要
生物免疫系统在遭受未知抗原攻击时,能通过基因的重组和变异,找到合适的抗体消灭抗原,并且能保持抗体的多样性。把生物免疫系统的这种特性加入到免疫遗传算法中能解决其迭代后期出现的退化现象。针对注射过疫苗的生物免疫系统能够很快识别抗原这一特性,对传统免疫遗传算法进行改进,提出一种改进的免疫遗传算法(IIGA),并用其求解经典的Benchmark多峰值函数,实验结果表明IIGA能够有效抑制免疫遗传算法的退化现象,并提高算法的收敛速度。
The biological immune system when attacked can always find the right antibodies to destroy the antigen and can maintain the diversity of antibodies. The combination of genetic and immunity in biology has been shown to be an effective approach to solving the degeneration of genetic algorithm in the late iterative optimization. According to the characteristic that the injected vaccine immune system can accomplish quickly identification the antigen, an improved immune genetic algorithm （IIGA） is proposed based on this theory for Benchmark function optimization. The results show that the IIGA can effectively prevent the algorithm degenerative during the process of optimization of the genetic algorithm, and improve the convergent speed of the algorithm.
出处
《电子科技大学学报》
EI
CAS
CSCD
北大核心
2013年第5期769-772,共4页
Journal of University of Electronic Science and Technology of China
基金
河南省科技创新团队(112PCXTD343)
关键词
收敛
遗传算法
免疫疫苗
多峰值函数
最优解
接种
convergence
genetic algorithm
immune vaccine
multi-peak function
optimum solution
vaccinate
