Artificial Immune Optimization and Its Application in Industrial Electronics

Abstract

As we know that natural immune systems are complex and enormous self-defence systems with the distinguished capabilities of learning, memory, and adaptation. Artificial Immune System (AIS), based on the natural immune systems, can be considered as an emerging kind of biologically inspired computational intelligence methods, which have attracted considerable research interest from different communities over the past decade. Artificial Immune Optimization (AIO) methods are an important partner of the AIS. They have been successfully applied to handle numerous challenging optimization problems with superior performances over classical approaches.

In this Master's thesis, the essential natural immune principles of circulatory, regulatory, and memory mechanisms are first introduced. Next, we present a few typical AIS models and algorithms. In addition, the recent advances of the AIO methods with their applications are discussed. We also demonstrate the application of the clonal selection algorithm in nonlinear function optimization and LC passive power filter optimal design. Computer simulations are made to verify its optimization effectiveness.