基于等级变异的克隆选择算法  

Clonal Selection Algorithm Based on Grade Variation

作　　者：宋丹[1,2] 赖旭芝[1] 吴敏[1] 

机构地区：[1]中南大学信息科学与工程学院,长沙410083 [2]湖南财政经济学院信息管理系,长沙410205

出　　处：《模式识别与人工智能》2011年第3期438-443,共6页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.60874042);高等学校博士学科点专项科研基金新教师基金项目(No.20090162120068)资助

摘　　要：提出一种克隆选择算法——基于等级变异的克隆选择算法.为提高进化中变异的有效性,算法将变异尺度分成若干等级,低等级变异有利于跳出局部最优解,实现全局寻优;高等级变异有利于局部的高精度寻优.此外,算法在进化过程中记忆父抗体的变异尺度等级等信息,并制定有效的变异策略运用这些信息以指导后续进化过程.采用标准函数测试并与其它优化算法进行对比.实验结果表明,该算法具有收敛速度快、全局搜索能力强、精度高和鲁棒性好的优点.A clonal selection algorithm is proposed, called ctonal selection algorithm based on grade variation （CSABGV）. To improve the effectiveness of the variation, the variability scale is divided into several levels： low-grade variant is conducive to jump out of local optimal solution and to achieve global optimization while high-grade variation is in favor of local optimization. In addition, the algorithm remembers and uses the information of variation grade of the parent antibody, and develops effective mutation strategy to guide mutation of antibodies. The performance of the proposed algorithm is compared with five benchmark functions and other optimization algorithms. Experimental results show that CSABGV has the characteristics of rapid convergence, powerful global search capability, high precision and good robustness.

关 键 词：免疫记忆 克隆选择 等级变异 全局优化 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]