Optimization of Artificial Bee Colony Algorithm Based on Immune Regulation

In this paper, aiming at the shortcomings of the Artificial Bee Colony (ABC) algorithm in guiding bees and observing bees in the search mechanism, the author integrated the idea of self-regulation of antibody concentration in the immune algorithm, regarded the optimal solution as antigen, and the search bee as antibody for target search. In this paper, the differences between the optimal solution and the initial solution are compared by setting parameters when initializing the nectar source, so as to enhance the memory of antibodies, avoid falling into the local optimal when maintaining the diversity of the population, accelerate the convergence speed, and increase the global search ability of the algorithm. Simulation results of six classical test functions show that the proposed algorithm has obvious advantages over ABC algorithm in terms of optimization accuracy, convergence, accuracy and stability. We will combine the proposed algorithm with Blockchain techniques in our future work.