Application of an immune algorithm to multi-optimization problems

Abstract

An immune system has powerful abilities such as memory, recognition and learning to respond to invading antigens, and is expected to be applicable to many engineering applications. This paper proposes an optimization algorithm imitating the immune system to solve the multi-optimization problem partly using a genetic algorithm. The proposed algorithm is shown to be effective for searching for a set of solutions, but not local solutions, through illustrative examples of multimodal functions such as Shubert function.