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This paper investigates the use of clonal selection principles based on our immune system for optimization applications in electromagnetics. This concept is based on our immune system's ability to respond to an antigen and produce a pool of anti-body secreting cells. In addition to the common implementations of this algorithm where the a-nity maturation and cloning principles of clonal selection principles are used, we utilize memory and the cross-over concepts that are common to other bio-inspired methods. The performance of the algorithm is investigated for well known mathematical test functions and its potential is demonstrated in the context of the design of a radar absorbing material and a planar phased array antenna with speciflc radiation and null characteristics. Classical optimization techniques typically require an initial estimate reasonably close to the flnal result in order to avoid stagnation at a local optimum point. They also tend to be computationally intensive as they require analytical calculations such as derivatives. Nature provides heuristic optimization methods that rely on the techniques devised by difierent species over thousands of years for survival and can be utilized in engineering applications. Some of these optimizations are utilized for survival by difierent species. These approaches tend to be agent based as they can simultaneously sample the optimization space for a certain number of randomly inspired possibilities, with each iteration adding more intelligence to the heuristic search steps involved. Some examples of nature inspired optimization techniques are the genetic algorithm (GA) (1), particle swarm optimization (PSO) (2), ant colony optimization (ACO) (3), and the artiflcial immune system (AIS) (4), which is the main focus of this paper.

APPLICATION OF ARTIFICIAL IMMUNE SYSTEM ALGORITHM TO ELECTROMAGNETICS PROBLEMS