摘要
为提高人工免疫算法求解旅行商问题的效率,构造了一种基于多子种群免疫进化的两层框架模型。在此模型的基础上提出了分层局部最优免疫优势克隆选择算法(HLOICSA)。通过对多个子种群进行低层免疫操作——局部最优免疫优势、克隆选择、基于信息熵的抗体多样性改善和高层遗传操作——选择、交叉、变异,增强优秀抗体实现亲和力成熟的机会,提高抗体群分布的多样性,在深度搜索和广度寻优之间取得了平衡。针对TSP的实验结果表明,该算法具有可靠的全局收敛性及较快的收敛速度。
In order to solve traveling salesman problem more efficiently using artificial immune algorithm, a two-floor model based on multiple sub-populations immune evolution as well as hierarchical local optimization immunodominance clonal selection algorithm（HLOICSA） was put forward. To quickly obtain the global optimum, multiple sub-populations were operated by bottom floor immune operators：local optimization immunodominance, elonal selection, antibody diversity amelioration based on locus information entropy, multiple sub-populations were also operated by top floor genetic operators：selection, crossover, mutation. Through those operators, diversity of antibody sub-population distribution and excellent antibody affinity maturation was enhanced, the balance between in the depth and breadth of the search-optimizing was acquired. Experimental results indicate that the algorithm has a remarkable quality of the global convergence reliability and convergence velocity.
出处
《计算机科学》
CSCD
北大核心
2010年第6期256-260,264,共6页
Computer Science
基金
国家自然科学基金重点项目(60634020)
国家自然科学基金项目(60874096)资助
关键词
人工免疫算法
旅行商问题
分层
局部最优免疫优势
克隆选择
Artificial immune algorithm, Traveling salesman problem, Hierarchical, Local optimization immunodominance, Clonal selection
