摘要
为了克服基本遗传算法存在的缺点和不足,将免疫系统中抗体多样性的维持机制引入遗传算法,同时兼顾个体多样性和提高种群中个体适应度的水平,提出了基于相似性矢量距为选择概率的免疫遗传算法,并给出了此类概率选择的一般表示形式.为了防止基于相似性矢量距为选择概率的免疫遗传算法在优化过程中出现退化现象,通过在算法中引入免疫疫苗的方式,对该算法进一步加以改进.从每一代保优抗体中提取有效信息,进而得到一种新的疫苗提取方法.基于所提出的改进免疫遗传算法,提出了改进的编码方案.对20个城市的TSP问题进行研究,通过不同参数的比较,得出了算法中相关参数的取值范围.比较了6种算法的收敛速度,进一步证实了所提出算法具有良好的收敛性.
To overcome the shortage of the basic genetic algorithm, the mechanism of antibodies＇ diversity in the immune system is introduced into the genetic algorithm. In the case of keeping individual diversity and improving the level of adaptability of the individual diversity in the population, the immune genetic algorithm based on selection probability of similarity and vector distance is proposed. Meanwhile the general expressing form of the kind of selection probability is given. Secondly, the immune vaccine is introduced into immune genetic algorithm on selection probability of similarity and vector distance to prevent the algorithm degenerative during the process of optimization. The immune genetic algorithm is applied to the 20-city traveling salesman problem, and advanced coding strategy is proposed. Comparing the algorithm with other six algorithms, the results show that the convergent speed of the algorithm is faster than others.
出处
《控制与决策》
EI
CSCD
北大核心
2005年第10期1185-1188,共4页
Control and Decision
基金
黑龙江省骨干教师基金项目(1053G002)
关键词
免疫遗传算法
相似性
矢量距
免疫疫苗
TSP
Immune genetic algorithm
Similarity
Vector distance
Immune vaccine
TSP
