中文摘要：

借鉴禁忌搜索的思想改进了人工免疫网络算法（aiNet），提出一种禁忌人工免疫网络算法（TS—aiNet）。在算法中引入禁忌表，禁忌在网络迭代中亲和力不再增加的细胞，通过特赦准则赦免一些被禁忌的优良状态；增加记忆表，保存成熟的记忆细胞；重新定义高斯变异方式，保证多样化的搜索。利用Markov链分析了该算法的全局收敛性，通过对典型系统的仿真实验分析了该算法的性能，并与克隆选择算法和opt-aiNet算法进行了比较，最终将改进的算法运用到红外与可见光图像配准中，像素级配准精度可以达到0．5像素。实验结果表明，该算法在多模态搜索空间中具有更好的全局收敛性、稳定性和发现极值点能力，能够克服早熟现象，提高图像配准的速度和精度，是一种有效的全局优化方法。

英文摘要：

The paper proposes the tabu search artificial immune algorithm （TS-aiNet） based on the aiNet model and the tabu search algorithm. It introduces a tabu list that tabooes the cells whose affinity do not increase any more in the network. In some phrase the tabooed excellent cells are released according to the aspiration criteria. For saving mature memory cells a memory table is added to the network. Moreover, it redefines the expression of the Gauss mutation for diversity seeking, and uses the Markov chain to prove the global convergence. The performance optimization analysis of the proposed algorithm was carried out with typical system experiments, and it was compared with the CLONALG and the opt-aiNet algorithm. Finally the TS-aiNet algorithm was applied to the image registration for visible and infrared images, and the matching accuracy of 0.5 pixels was achieved. Both the theoretical analysis and the simulation results show that the pre- sented approach has preferable global convergence ability in multi-modal search space, and it can avoid prematurity effec- tively. It has better performance in improving accuracy and speed of image registration, and is an ett~cient global optimization algorithm.