一种改进的混沌优化算法

为了克服遗传算法的早熟现象以及混沌优化的搜索时间过长的缺点,将遗传算法、混沌优化和变尺度方法相结合,提出了一种改进的混沌优化算法.该算法利用混沌的随机性、遍历性和规律性来避免陷入局部极小值,从而也克服了遗传算法中的早熟现象,同时引入了变尺度方法提高该算法的搜索速度.本文还给出了算法的收敛性分析.对典型测试函数的仿真结果表明此算法优于变尺度混沌优化和遗传算法.

An improved chaotic optimization algorithm

To overcome premature convergence of genetic algorithm(GA) and long search time of chaotic optimization,an improved chaotic optimization algorithm(ICOA) is proposed by combining GA,chaotic optimization and mutative scale method.This algorithm uses chaotic characteristics-randomness,ergodicity and regularity to avoid trapping around local optima.It can overcome premature convergence of GA.At the same time,mutative scale method is introduced into the algorithm to improve search speed.The convergence analysis of algorithm is also given.Finally,the proposed algorithm is applied to solve some complex benchmark functions,and the simulations show the proposed algorithm can provide better performance than mutative scale chaotic algorithm and GA.