Global Numerical Optimization by Using an Improved Immune Algorithm

An improved immune algorithm, named the Taguchi-immune algorithm (TIA), is proposed to solve global numerical optimization problems with continuous variables based on both the features of a biological immune system and the systematic reasoning ability of the Taguchi method. In the TIA, the clonal proliferation within hypermutation for several antibody diversifications and the recombination by using the Taguchi method for the local search are integrated to improve the capabilities of exploration and exploitation. The systematic reasoning ability of the Taguchi method is executed in the recombination operations to select the better genes to achieve the potential recombination, and consequently enhance the TIA. The proposed TIA is effectively applied to solve 15 benchmark problems of global optimization with 30 or 100 dimensions and very large numbers of local minima. The computational experiments show that the proposed TIA not only can find optimal or close-to-optimal solutions but also can obtain both better and more robust results than the existing improved genetic algorithms reported recently in the literature. Therefore, the TIA can be more robust, statistically sound, and quickly convergent for solving the global numerical optimization problems.