混沌变异算子的改进遗传算法及其应用

Improved genetic algorithms based on chaotic mutation operation and its application

为解决标准遗传算法(SGA)收敛缓慢等缺点,提出一种混沌变异算子的改进遗传算法,进化过程中,为防止局部早熟收敛,对较优个体的变异操作中引入一个混沌变异算子,并把混沌运动的遍历范围"放大"到优化变量的取值范围,通过一代代地不断进化,收敛到一个最适合环境的个体上,求得问题的最优解;建立精英个体序列库,防止最优解的丢失。采用实际算例进行仿真试验,仿真结果证明了该算法的有效性。

In order to change the slow convergence of simple genetic algorithms, an improved genetic algorithms based on chaotic mutation operation （named as CGA） was presented. In the evolution process, to avoid local premature convergence, CGA introduced a chaotic mutation operator in the mutation of the optimum individual, and enlarged the scope of chaotic campaign to the range of variables optimization, through the evolution from generation to generation, converged to a individual, which is the most suitable environment, to find the optimal solution; the best individual sequences is to avoid missing the best value. At last the examples were emtdated, and the data make it clear that the CGA is super to SGA.