改进免疫遗传算法及其应用研究  

Study on Improved Immune Genetic Algorithm and Its Application

作　　者：张建萍[1] 刘希玉[2] 谭业武[1] 

机构地区：[1]滨州学院,山东滨州256603 [2]山东师范大学,山东济南250014

出　　处：《计算机技术与发展》2008年第10期166-169,共4页Computer Technology and Development

基　　金：国家自然科学基金资助项目(6037405);山东省自然科学基金重大项目(Z2004G02);"泰山学者"建设工程专项经费资助项目;山东省滨州学院"青年人才创新工程"科研基金项目(BZXYQNLG200711)

摘　　要：在传统遗传算法基础上提出一种改进遗传算法——免疫遗传算法(IGA),该算法将生物系统免疫思想引入遗传算法中,通过计算抗体之间的亲和度来促进和抑制抗体,既保留了群体中较优抗体又保证了抗体多样性,避免搜索进化的过早收敛,得到全局最优解。文中采用此改进算法来控制无源光中光分支器和光网络单元中位置分配选择,通过浓度因子控制解空间的搜索方向,帮助逃离局部极值。利用Visual C++6.0对改进的免疫遗传算法和传统遗传算法进行比较,证明IGA的有效性和优越性。In this paper, based on an genetic algorithm,an improved effective immune genetic algorithm is presented. In order to overcome premature convergence and find out optimal solution, the immune mechanism of creature is used in IGA and antibodies will be promoted or restrained according to the computation result of affinity between antibodies, which reserves the excellent antibodies as well as guarantees the variability of antibody. In addition, the IGA＇ s local searching ability is improved by combining it with gradient method. The computation results show that both global and local searching abilities of the IGA are improved and premature convergence is overcome availably. The effectiveness and the superiority of IGA is proved on Visual C＋＋6.0 by optimization experiments using another optimization algorithm comparisons to.

关 键 词：无源光网络 遗传算法 免疫算子 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]