分布式人工蜂群免疫算法求解函数优化问题

为了克服人工蜂群算法由于开发能力较弱而导致收敛速度慢、搜索精度不高等缺点，结合子蜂群思想和免疫克隆选择算法，提出一种基于分布式精英进化模型的人工蜂群免疫算法。首先对外层子蜂群进行启发式快速人工蜂群操作以提高收敛速度；然后对内层精英蜂群进行免疫克隆选择操作，进一步提高了算法的收敛精度和全局搜索能力。仿真结果表明了该算法在求解函数优化问题上的有效性和优越性。

Distributed artificial bee colony immune algorithm for the problems of function optimization

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.