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1. 四川大学计算机学院
2. 海南师范大学信息学院
纸质出版日期:2010,
网络出版日期:2009-12-30,
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张瑜,李涛,吴丽华,夏峰.求解TSP问题的抗体克隆优化算法[J].工程科学与技术,2010,42(3):127-131.
Zhang Yu, Li Tao, Wu Lihua, et al. A Novel Antibody Clone Optimization Algorithm for TSP[J]. Advanced Engineering Sciences, 2010,42(3):127-131.
中文摘要: 为解决传统求解TSP问题(Traveling Salesman Problem)的方法所固有的组合爆炸问题,提出了一种新的基于MHC(Major Histocompatibility Complex,主要组织相容性复合体)的抗体克隆优化算法(Antibody Clone Optimization Algorithm inspired by MHC,COAMHC)。该算法应用MHC分子单倍型特性将优秀抗体基因保存为MHC串,并通过疫苗接种遗传至子代以增强其局部搜索能力;应用MHC分子多态性并通过基因突变以及随机引入新抗体基因来提高抗体群多样性,以增强其全局搜索能力。通过TSP问题的仿真实验表明,该算法在收敛速度、和求解精度方面比经典克隆选择算法CLONALG性能更好
Abstract:To address the traditional Traveling Salesman Problems (TSP) with the combinatorial explosion property
a novel MHC-inspired antibody clone optimization algorithm (COAMHC) was proposed by drawing inspiration from the features of Major Histocompatibility Complex (MHC) in the biological immune system. COAMHC preserves elitist antibody genes through the MHC string to improve its local search capability and improves the diversity of antibody population by gene mutation and some new random immigrant antibodies to enhance its global search capability. The experiments of comparing COAMHC with the canonical clone selection algorithm (CLONALG) were carried out for the TSP and results indicated that the performance of COAMHC is better than that of CLONALG. The COAMHC algorithm provides new opportunities for solving previously intractable optimization problems such as TSP.
人工免疫系统TSPMHC抗体克隆算法优化
artificial immune systemTSPMHCantibody clone algorithmoptimization
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