Study on Chaos Immune Network Algorithm for Multimodal Function Optimization

Multimodal function optimization problem, which requires finding out the global optimum and the utmost number of local optima, has important applications in the field of engineering. When solving multimodal function optimization problem with artificial immune network algorithm, problems such as premature convergence phenomena and unsatisfying searching precision may arise. Under such circumstances, improved chaos immune network algorithm was put forward in this paper. In the improved algorithm, the stopping criterion was improved and some relevant measures taken to avoid premature convergence; and chaos variable was used to simulate proliferation mode of immune cells to enhance searching precision. Based on simulation tests on some benchmark functions, conclusions were drawn that this algorithm can fast optimize the antibodies, strengthen the searching ability and enhance the searching precision.