For the problems of poor convergence, low searching precision and ease of premature convergence due to the weak exploitation of the artificial bee colony(ABC) algorithm, combining with the subpopulation and immune clonal selection(ICS) algorithm, a distributed quick artificial bee colony immune(DQABCI) algorithm based on the parallel distributed elitist(PDE) model is proposed. Firstly, the method ameliorates the diversity of subpopulation and enhances the global convergence through the out layer heuristic quick ABC operation of several subpopulations. Then, the convergence precision and global searching capability are improved by the inner layer ICS operation of elitist colony. Experimental results show the effectiveness and feasibility of the DQABCI algorithm for solving function optimization problems.