免疫进化算法

Immune Evolutionary Algorithm

研究现有进化算法的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.该新算法作为一种全局优化算法,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.在寻优过程中,该新算法还把确定性的变化和随机性的搜索有效地结合在一起,提高了收敛速度.通过马尔可夫链的分析,证明它是全局收敛的.测试表明,免疫进化算法不仅参数设置简单,而且可以提高收敛速度.

Based on the study of the existing evolutionary algorithm (EA) and the immune principle of creatures, an immune evolutionary algorithm (IEA) is proposed. It is a global optimization method, in which offsprings are produced by the best individual of parents population and the elitist replaces population to converge. In the process of optimization, the new algorithm increases its convergence speed, combining certainty with randomicity effectively. A global convergence is proved by analysis of Markov chain. The test results show that IEA not only requires simple parameters but also can improve the convergence.