基于邻域-克隆选择学习算法的分馏装置负荷优化

Optimization of distillation resources based on neighborhood-clonal selection learning algorithm

在免疫克隆选择和人工免疫网络算法基础上,采用了Agent的思想,提出了一种邻域-克隆选择学习全局优化算法(N-Clonalg)。不同于其他人工免疫算法,N-Clonalg定义了网格化的邻域操作环境,其主要搜索算子有N-克隆选择、N-竞争和自学习算子,能有机结合全局与局部搜索,多峰测试函数表明能较好地克服克隆选择算法(Clonalg)的早熟及人工免疫网络算法(Opt-aiNet)收敛速度慢问题。分馏装置负荷优化实例应用表明,算法具有较好的最优解搜索性能,能较好地实现化工中的寻优问题。

A neighbourhood-clonal selection learning algorithm（N-Clonalg）is proposed in this paper,which is combined with the idea of biological immune clonal selection system and multi-agent technology.Different from other artificial immune algorithms,N-Clonalg is based on the grid environment,and contains three main search operators,i.e.,N-clonal selection,N-competition and self-learning operators.Combining global and local searching operations,N-Clonalg overcomes the phenomena of precocious and slow convergence,and can better achieve the global optimal solutions effectively in the individual space,which is proved in multi-modal benchmarks.Distillation resources optimization shows that it has better search performance.