基于AIS的服务匹配模型

针对在当前服务发现系统的服务匹配过程中存在的系统自学习能力差的缺点，借鉴人工免疫系统中细胞变异、演化和二次响应等基本原理，通过模拟抗体-抗原识别机制来解决实际匹配问题，提出一种基于人工免疫系统的服务匹配模型。理论分析与仿真实验结果表明，该模型不仅在查全率、匹配速度等方面较传统服务发现系统有一定的提高，而且实现了由已知服务请求推测出相似服务请求，进而搜寻到最佳匹配服务的功能，提高了服务匹配过程中系统的适应学习、记忆和动态演化的能力。

AIS-based Service Match Model

With regarded to the problem that the current system’s self-learning ability usually appears weak during service discovery, a novel Artificial Immune System(AIS) based service match model is proposed, which is based on the reference to the principles of cell’s mutation, evaluation and the secondary response abilities, as well as simulation on the antibody-antigen identification mechanism. Theoretical analysis and simulations show that the model can increase the recall ratio and match speed, and realize the function that similar services can be obtained by known services. Furthermore, this service match model is able to improve a system’s self-learning, memory and dynamic evaluation capabilities.