主从式免疫克隆选择算法求解任务分配问题

Solving Task Assignment Problem by Master-slave Immune Clone Selection Algorithm

基于生物免疫系统的克隆选择机理,提出一种求解任务分配问题(task assignment problem,TAP)的主从式免疫克隆选择算法(MSICSA).该算法采用一种多种群策略,通过迁入和迁出操作,更新种群之间的信息,保持了群体的多样性.实验结果表明,该算法可有效改善基本免疫克隆选择算法解决大规模优化问题上的不足,具有很好的收敛性和稳定性,能有效解决任务分配问题.

Based on the clonal selection mechanism of biological immune system,a kind of master-slave immune clone selection algorithm（MSICSA） is introduced to deal with task assignment problem（TAP）.The algorithm adopts multi-population strategy.Through the immigration and emigration operation,information among populations is updated and the diversity of population is maintained.Experimental results show that the proposed MSICSA can effectively overcome the shortcomings of the basic immune clonal selection algorithm in solving large-scale optimization problems,has excellent convergence and stability,and can solve the TAP problem effectively.