摘要
遗传算法是一种导向随机搜索算法,具有较强的全局搜索能力。为克服遗传算法盲目搜索、收敛速度慢的缺点,文章提出了免疫遗传混合算法。利用求解问题特征对遗传算法的种群进行免疫接种,以提高搜索速度。为检验混合算法的效率,给出了经典TSP问题的混合算法。实验结果表明,混合算法具有收敛速度快、搜索精度高、稳健性强的特点。
Genetic algorithm (GA) is an algorithm used to find approximate solutions to difficult-to-solve problems through application of the principles of evolutionary biology to computer science, it has the ability of doing a global searching quickly and stochastically. But it has many problems, such as searching blindly and converging slowly. A novel algorithm that combines GA with immune algorithm is proposed, which inoculate populations generated by GA to improve searching speed according to the speciality of a certain problem. In order to evaluate the performance, an algorithm to solve TSP problem is designed and implemented. The Simulated results show that the near global optimal solution can be easily and quickly obtained by this method.
出处
《微电子学与计算机》
CSCD
北大核心
2005年第6期221-224,共4页
Microelectronics & Computer
基金
国家863高技术研究发展计划(2003AA001048)
关键词
遗传算法
免疫算法
TSP
Genetic algorithm, Immune algorithm, TSP
