基于解空间定向优化的改进免疫算法研究及应用

Research and Application of Improved Immune Algorithms Based on Solution Space Oriented Optimization

为了降低免疫算法应用过程中搜索目标函数的响应耗时,提出了一种基于解空间定向优化的改进免疫算法。相比于传统的免疫算法,增加了药物辅助环节能够使算法搜索目标函数的范围大大减小,搜索过程中按照目标函数的最终要求,规定搜索方向同时缩小搜索范围。能够为算法搜索目标函数最值降低数据量。将该优化算法应用于多峰函数的寻找最小值的问题,仿真实验证明了算法的可行性和更优的实时性。

In order to reduce the response time of searching objective function in the application of immune algorithm,an improved immune algorithms based on solution space oriented optimization is proposed in this paper.Compared with the traditional immune algorithm,the range of searching objective function for the algorithm can be greatly reduced by adding drug-assisted links.In the process of searching,according to the final requirements of the objective function,the search direction can be specified and the search range can be reduced at the same time.It can reduce the amount of data for searching the maximum value of the objective function for the algorithm.At the end of this paper,the optimization algorithm is applied to the problem of finding the minimum value of multimodal function.The simulation results show that the algorithm is feasible and real-time.