利用改进的遗传算法解决全局寻优问题

Using an improved genetic algorithm to solve global optimization problem

寻找函数的全局最优解是一个很常见的工程应用问题,简单遗传算法是解决此类问题的有力工具。但由于简单遗传算法具有中全局收敛能力差和收敛速度慢的缺点。本文基于对遗传算子的优化,提出一种混合分类选择和定向变异的改进遗传算法来解决全局寻优问题。经仿真结果表明,该算法具有较强的全局收敛能力和较快的收敛速度。

It is a very common engineering application problem to search an global optimum solution of a function. Simple genetic algorithm is a powerful tool to solve these problems. But SGA has the shortcomings in overall situation and convergence speed. Based on the optimization for genetic operator, this paper put forwards a kind of improved genetic algorithm combined with classified selection and directed mutation to solve global optimization problem. The simulation experiments show that the improved algorithm has strong overall situation ability and fast convergence speed.