摘要
为了提高免疫算法的搜索能力,根据生物免疫机制及生物进化的周期性,设计了一种周期变化变异算子。为了避免仅仅以亲和度作为免疫选择评价标准,低亲和度抗体过度抑制,提出了将抗体浓度引入到亲和度中作为评价指标,设计了一种改进的免疫选择算子。基于马尔科夫链,分析了改进免疫算法的收敛性。为了测试该算法的有效性,将算法应用于函数优化问题中。仿真结果表明,改进的免疫算法具有更高的搜索速度和精度。
In order to improve the search ability of the immune algorithm,a periodically varying mutation operator is designed based on immune mechanism and periodical evolution of organism.If only the affinity is taken as an immune selection evaluation criterion,low affinity antibodies will be overly inhibited.An improved immune selection operator is proposed by introducing the antibody concentration to the affinity as the evaluating index.The convergence of the improved immune algorithm is analyzed based on the Markov chain.In order to test the effectiveness of the algorithm,it is applied to solve the function optimization problems.Simulation results show that the improved algorithm has a higher search speed and accuracy.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2011年第2期464-467,共4页
Systems Engineering and Electronics
基金
国家自然科学基金(60774032)
广西教育厅科研项目(201010LX242)
广东省自然科学基金博士启动项目(9451064101002853)
广西自然科学基金(2010GXNSFA013126)资助课题
关键词
免疫算法
函数优化
变异算子
选择算子
周期变化
immune algorithm
function optimization
mutation operator
selection operator
periodically varying
