An adaptive chaos immune optimization algorithm with mutative scale and its application
自适应变尺度混沌免疫优化算法及其应用

By combing the chaos optimization method and the immune algorithm, we propose an adaptive chaos immune optimization algorithm(AMSCIOA) with mutative scale, using one-dimensional iterative chaotic self mapping x = sin(2/x) with infinite collapses within the finite region -1, 1]. In the optimization process, to ensure the high speed and precision some measures are taken, including: 1) the ranges of optimized variables are reduced continuously by the adaptive mutative scale method, and the searching precision is enhanced accordingly; 2) the maximal number of repetitions is regarded as a controlled index. The simulation results for three testing functions validate the high speed and precision of the AMSCIOA with mutative scale. The simulation of the intrusion detection system for detecting the intrusions to mobile Ad Hoc networks show that this algorithm lowers the dependence of training samples, reduces the noise influence on the performance, provides a high detection rate, and produces a small time-delay caused by computation.