Study on chaos immune network algorithm for multimodal function optimization
面向多模态函数优化的混沌免疫网络算法研究

Multimodal function optimization problem has important applications in the fields of engineering,which requires finding the global optimum and as many as possible local optima.Artificial immune network solving multimodal function optimization may generate premature convergence phenomena.Its searching precision can't satisfy us yet.So improved chaos immune network algorithm was presented in this paper.Termination conditions as well as relevant measures were improved to avoid possibly generating premature convergence,and chaos variable was used to simulate proliferation mode of immune cells to enhance searching precision.Some classical functions were applied to test the performance of the algorithm.The simulation results illustrate that this algorithm can fast optimize antibodies and take advantage in searching ability and searching precision.