摘要
基于克隆选择算法基本原理,提出一种搜索函数最优解问题的自适应克隆选择算法(ACSA).在ACSA中,抗体的克隆数、高频变异率、每代更新数都能在优化过程中自适应调节,而且变异抗体具有免疫记忆功能.通过对ACSA的收敛性分析,并和标准克隆选择算法仿真比较,结果表明ACSA在求解函数最优解问题时具有较强的收敛性和自适应性.
Based on the basic principle of clonal selection algorithm, an adaptive clonal selection algorithm （ACSA） for function optimization is proposed. The clone number of antibody, the high frequency mutation ratios and the renewal number of each generation can regulate automatically in ACSA. Meanwhile, mutation antibodies have the ability of immune memory. The results indicate that the ACSA has stronger convergence and adaptability through the convergence analysis and simulation compared with standard clonal selection algorithm.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2009年第2期202-207,共6页
Pattern Recognition and Artificial Intelligence
基金
国家十一五科技支撑计划项目子课题项目(No.2006BAD10A1410)
国家自然科学基金项目(No.60774096)
国家863计划项目(No.2006AA10Z237)资助
关键词
自适应克隆选择
人工免疫
函数优化
Adaptive Clonal Selection Algorithm, Artificial Immune, Function Optimization
