矢量矩浓度的免疫算法在函数优化中的应用

A Immune Algorithm Based on the Vector Distance Applied to Function Optimization

针对传统免疫算法收敛速度较慢的缺点,介绍了一种改进策略——基于矢量矩浓度的免疫算法(Vector Distance based Immune Algorithm,VDIA)。该算法采用了基于矢量矩表示的抗体浓度、期望繁殖率以及免疫记忆策略。选取2个测试函数进行仿真实验,结果表明改进算法收敛速度快,收敛概率高,特别是在高维时的优越性突出。

To improve the convergence speed of immune algorithm, a modified immune algorithm based on the vector distance （VDIA） is developed, which utilizes antibody density based on the vector distance, expected reproductive rate as well as immune memory strategy. Simulation results demonstrate that the modified algorithm can not only converge fast but also obtain a rather large convergence probability, espesilly in higher dimensional space.