Abstract: An adaptive differential evolution algorithm combined with chaotic search（CADE） is presented.It adjusts the cross operator adaptively according to the computation process in order to preserve the diversity of population at the initial generation as well as to improve the global convergence ability.Chaotic search which behaves well in local search is adopted to enhance the precision of solution and the probability of obtaining global optimal solution.Several typical benchmark functions are tested and experimental results show that the presented algorithm has remarkable global convergence ability，and it can avoid premature convergence effectively.

Key words: differential evolution algorithm, adaptive, chaotic search, global optimization

摘要： 提出一种基于混沌搜索的自适应差分进化算法（CADE），该算法在计算过程中自适应地调整交叉率，在搜索初期保持种群多样性的同时增强算法的全局收敛性。具有较强局部遍历搜索性能的混沌搜索的引入使得算法具有较好的求解精度，增加搜索到全局最优解的概率。对几种典型的测试函数对CADE进行了测试，实验结果表明，该算法能有效地避免早熟收敛，具有良好的全局收敛性。

关键词: 差分进化算法, 自适应, 混沌搜索, 全局优化