一种改进的遗传算法在函数优化中的应用

Application of a modified genetic algorithm in function optimization

针对传统遗传算法在处理多峰值函数优化存在的"早熟"问题,以及在后期搜索效率低的问题,在对目前常见的几种种群早熟程度评价指标进行分析的此基础上,提出了一种新的种群"早熟"程度评价指标,并据此提出了一种改进的自适应遗传算法;最后将改进的遗传算法用于函数优化;实验表明:改进后的遗传算法有效地解决了过早收敛、局部搜索能力差和全局收敛速度慢等问题。

In this paper, an improved adaptive genetic algorithm is presented in order to resolve the problem that traditional GA is prone to premature and is inefficient in application of the standard genetic algorithm to the Multi- modal Function optimization problems in the final stage. On the basis of evalution of several common premature index for the population, a new premature index is put forward. Then an improved adaptive genetic algorithm is presented. The simulation shows our new method has faster evolution speed and robustness,and reaches a general optimal solution.