动态免疫优化算法及其在背包问题中的应用  

Dynamic Immune Optimization Algorithm and Its Application in Knapsack Problem

作　　者：钱淑渠[1] 武慧虹[1] 涂歆[2] 

机构地区：[1]安顺学院数学与计算机科学系,贵州安顺561000 [2]东南大学自动化学院,南京211189

出　　处：《计算机工程》2011年第20期216-218,222,共4页Computer Engineering

基　　金：贵州省自然科学基金资助项目(20090074)

摘　　要：利用人工免疫系统的学习、记忆、识别等功能,提出一种动态免疫优化算法(DIOA),用于解决一类高维动态约束优化问题。其中对可行抗体进行克隆突变操作,非可行抗体按价值密度使用贪婪算法进行修正,环境识别模块借助记忆细胞产生新的环境初始群,从而加快算法收敛速度。利用DIOA求解不同环境下的高维背包问题,结果表明,与同类算法相比,该算法能更快地跟踪最优值,收敛效果更好。This paper proposes a Dynamic Immune Optimization Algorithm（DIOA） based on biological immune system learning,memory and recognition functions to solve a class of high-dimensional dynamic optimization problem with constraints.The feasible antibodies are cloned and mutated,the infeasible antibodies are repaired,by means of the profit-density of antibody,and the new environmental population is generated by using memory cells of similar environment,which accelerates the convergence of algorithm.The algorithm is applied in the high-dimensional knapsack problems are solved in different environments.Experimental results prove that,compared with traditional algorithms,DIOA can track the optimum rapidly and has stronger convergent capability.

关 键 词：动态环境 高维动态约束优化 背包问题 免疫优化 贪婪算法 

分 类 号：TP306.12[自动化与计算机技术—计算机系统结构]