摘要
为了更有效的求解旅行商问题(TSP),利用遗传算法与免疫算法各自的特点以及二者的共性提出了一种新的优化方法———免疫遗传算法,在本算法中采用抗体浓度调节机制并引入能量函数来求解TSP问题。给出了求解TSP问题的抗体、抗原、抗体浓度以及能量函数的数学表示,描述了该算法求解TSP的具体实现过程。仿真实验结果表明该方法在解决同类问题时比传统人工神经网络、遗传算法以及单一免疫算法取得了更短路径和更快的收敛。
Using the characteristics of the genetic algorithm and the immune algorithm, an immune-genetic algorithm was presented for solving TSP （traveling salesman problem） more effectively. The energy function and adjusting mechanism of antibody concentration were introduced into this algorithm . The mathematical formulas of antibody , antigen, antibody concentration and energy function for solving TSP were established. The procedure of solving TSP was described. The experimental results showed that this algorithm procure has the shorter mute and faster convergence than the other algorithms for the same TSP, including traditional artifical neural network , genetic algorithm and simplex immune algorithm.
出处
《四川大学学报（工程科学版）》
EI
CAS
CSCD
北大核心
2006年第1期86-91,共6页
Journal of Sichuan University (Engineering Science Edition)
基金
国家自然科学基金(60373110)
教育部博士点基金(20030610003)
教育部新世纪优秀人才计划(NCET-04-0870)
四川大学科技创新基金资助项目(2004CF10)
关键词
免疫-遗传
能量函数
抗体浓度
TSP
immune-genetic
energy function
antibody concentration
TSP
