免疫进化算法及其在暴雨强度公式参数优化中的应用

在研究现有进化算法优越性与存在不足的基础上，受生物免疫原理的启发，提出了一种新的算法－免疫进化算法，免疫进化算法的核心在于充分利用最优个体的信息，在整个进化过程中，以父代最优个体为基础来产生子代群体，并以最优个体的收敛来代替群体的收敛，此外，算法的随机搜索是在确定方式的指导下完成的。相比于现有的进化算法，免疫进化算法提高了收敛速度，有效地克服了不成熟收敛，理论证明该算法是全局收敛的。最后，用免疫进化算法对暴雨强度公式参数进行了优化，并将其计算结果与传统方法和加速遗传算法的计算结果作了比较，结果表明，免疫进化算法的拟合效果最好。

Immune evolutionary algorithm and its application to parameters optimization in storm intensity formula

Based on study of the existing evolutionary algorithms' property and enlightened by the immune principle of creature, a novel evolutionary algorithm-IEA is proposed. The core of IEA is to make full use of the information of the elitist. IEA makes the offspring population through the elitist of the parent population, and it pursues the convergence of the elitist in stead of that of population. Besides that, certain ways are adopted to guide the random search dynamically in the evolutionary process. Compared with the existing evolutionary algorithms, IEA greatly increases convergent speed and overcomes premature convergence, and furthermore, it is theoretically proved to be overall convergent. Finally, parameters optimization in storm intensity formula is done with IEA, and its results are compared with those of traditional method and accelerated genetic algorithm. Tests show that IEA has the best fitting results.