Abstract:
This paper proposes a new Chaos Differential Evolution(CDE) algorithm. It uses Differential Evolution(DE) algorithm to find the best individual each generation, then chaos based local search is executed nearby the best individual. A scaling factor is introduced to enhance the searching ability of CDE. Experimental results on six benchmark functions show the error function value is 10-14, both the ability of finding optimal solution and convergence speed using CDE are better than using DE.
Key words:
Differential Evolution(DE),
chaos,
local search
摘要: 提出一种新的混沌差分进化(CDE)算法,在每一代中通过差分进化(DE)算法找到最佳个体,在最佳个体附近用混沌方法进行局部搜索,通过引入调节因子加强其搜索能力。6个基本测试函数的优化结果表明,当误差函数精度为10-14时,与DE相比,CDE的寻优能力更强、收敛速度较快。
关键词:
差分进化,
混沌,
局部搜索
CLC Number:
TAN Yue; TAN Guan-zheng; TU Li. Novel Chaos Differential Evolution Algorithm[J]. Computer Engineering, 2009, 35(11): 216-217,.
谭 跃;谭冠政;涂 立. 一种新的混沌差分进化算法[J]. 计算机工程, 2009, 35(11): 216-217,.