Multi-modal Function Optimization Based on Artificial Immune Network and Chaos

摘要

After the immune network algorithms of multi-modal function optimization have developed, their performance can be improved by stochastic chaos map. In chaos attractor equations the variables are steadily approached stable points. A novel algorithm of immune network combined chaos is presented. The solutions searched and optimized can be accelerated using this method. According to opt-aiNet improved, parameters sensitivity can be bated. At last, some functions are tested. Through multi-peak illustrated and results optimized, the approach is verified with high generalized, efficiency and precision.