三种混沌免疫优化组合算法性能之比较研究

The Comparative Study of Performance of Three Types of Chaos Immune Optimization Combination Algorithms

利用混沌迭代的遍历性和内在随机性,提出三种混沌免疫优化组合算法,以弥补免疫进化算法收敛性能差的缺陷。这三种算法均综合了免疫进化算法和混沌优化算法各自的空间搜索优势,分别把混沌变量加载于免疫算法的总种群、遗传操作种群和记忆库种群的变量群体中,利用混沌搜索的特点对这些群体进行微小扰动并逐步调整扰动幅度。对三种算法的性能进行了实验比较,结果表明算法一具有更好的收敛性能和搜索效率。

On the basis of the ergodicity and internal randomicity of the chaos iteration, three types of novel Chaos Immune Optimization Combinational Algorithms (CIOCA) were presented to solve the problem of poor convergence of the Immune Evolutionary Algorithm (IEA). All the three algorithms combined the advantage of spatial search of both IEA and Chaos Optimization Algorithm (COA). In these three algorithms, chaos variable was respectively loaded in the variable group of global colony, inheritance colony and memory colony of immune algorithm, and then a tiny disturbance was added to these colonies and the disturbance amplitude was adjusted step by step by virtue of the property of chaos searching. The performances of the three algorithms were compared and the experimental results showed that the first algorithm takes advantage in convergence property and searching efficiency.