中文摘要：

基于危险理论,探讨求解动态约束单目标函数优化的免疫算法.设计的关键在于依据抗原呈递细胞（APC）被感染或凋亡细胞识别的机理,研究监测环境变化和确定环境等级的环境检测模块,以及探寻以自反应群、效应群和环境记忆群为载体的协同进化方案.该算法具有结构简单灵活、寻优时间可动态调节的优点,可实时监测环境变化.比较性的数值实验结果显示,算法在执行效率、效果之间能达到合理权衡且具有明显优越性,对动态约束优化具有较好应用潜力.

英文摘要：

Based on the danger theory,an immune algorithm for dynamic constrained single-objective function optimization is proposed.The key of the algorithm is to construct two functional modules：environmental detection and co-evolution.Relying upon the Antigen Presenting Cells（APCs） infected by distressed or apoptotic cells,the change of the environment is detected and the environmental level is confirmed.The co-evolving scheme based on self-reactive,effective and environmental memory cells is explored.The proposed approach can online detect the change of the environment with the merits of simplicity,flexibility and dynamic runtime.The experimental results show that the proposed approach performs better than the compared algorithms,it has potential use for dynamic constrained optimization problems while achieves the reasonable balance between effect and efficiency.