A Hybrid Artificial Immune Network with Swarm Learning

摘要：

The artificial immune system is a new approach inspired from abundant mechanisms of biological immune system. It includes such basic operations as clone, mutation, and selection, even crossover. It is widely applied to function optimization, abnormal detection, pattern recognition, computer security, machine learning, control engineering, etc. However, the evolutionary process of the current artificial immune system depends on only two factors. One is the fitness between antibody and antigen, and the other is the concentration of antibody population. As a global searching method, particle swarm optimization includes an important social learning mechanism that enables it to fast approximate the global optimum. This paper proposed a hybrid artificial immune network for optimization with swarm learning and elite-keeping. Simulation results indicated this hybrid method has lower time complexity and fast convergence, and is an effective optimization tool.

作者: Jian Fu Zhonghua Li Hong-Zhou Tan

作者单位: Hi-Tech Office Guangzhou Science and Technology Bureau Guangzhou, Guangdong Province, China, 510032 Dept.of Electronics and Communication Engineering Sun Yat-Sen University Guangzhou, Guangdong Provin

会议类型: 国际会议

会议名称: 2007年通信、电路与系统国际会议(2007 International Conference on Communications,Circuits and Systems Proceedings)

会议地点: 日本福冈

会议语种:英文

在线出版日期: 2007-07-11（万方平台首次上网日期，不代表论文的发表时间）