一种用于函数优化的免疫算法

An Immune Algorithm Applied to Function Optimization

遗传算法是目前最为广泛使用的可以用于函数优化的寻优方法之一。针对其容易陷入局部极值点等弱点,该文基于生物免疫系统中的学习机理及与其相关的免疫学理论中的克隆选择学说,提出了一种新的用于函数优化的免疫算法。新算法包括选择、克隆扩展、超变异和免疫记忆操作,定义了体现算法学习机制的学习参数和用于保存最优解的免疫记忆集合。提出了根据算法亲合度自适应调节学习参数的方法,以提高算法的全局寻优能力。用不同类型的测试函数进行仿真实验,结果表明该算法是有效的。

Genetic algorithm is one of the most widely used optimization methods applied to function optimization so far. However, it has such weaknesses as easy to get trapped into local optimal. This paper proposes an immune algorithm applied to function optimization based on the learning mechanism in natural immune system and the clonal selection theory in immunology. This algorithm includes selection, clone, hyper-mutation and re-selection operations. It defines a learning parameter to embody the learning mechanism and an immune memory set to keep the optimal results. The learning parameter adjusts adaptively with the affinity to promote the global search ability. Different testing functions are utilized to this method and the simulation results show this algorithm has good performance.