Immune Cloning Optimization Algorithm Based on Antibody Similarity Screening and Steady-State Adjustment

Abstract

In order to further improve the population diversity of the immune cloning algorithm when optimizing high-dimensional objects, and to improve the algorithm's global optimization ability and search efficiency, an immune cloning optimization algorithm based on antibody similarity screening and steady-state adjustment (ICASA) is proposed. By screening, that is, removing highly similar antibodies in the antibody population, the probability of the algorithm searching for the optimal solution is improved, and the repeated solution of similar antibodies is avoided. The antibody population is adjusted based on themedian property, and is injected with a high-quality vaccine realized by the median, which makes the antibody population evenly diffuse in the solution space to generate global antibody solutions. Finally, the convergence of the algorithm is proved by Markov chain theory. The test results of six groups of high-dimensional functions show that, compared with genetic algorithm (GA), immune cloning algorithm (ICA) and immune genetic algorithm (IGA), the proposed algorithm achieves 100% optimization, and the minimum convergence algebra, average convergence algebra and iterative algebra standard deviation are reduced by an average of 13.3%, 5.3%, and 29.3%, respectively, which verifies the algorithm's strong optimization ability, fast convergence and good stability.