解高维复杂函数优化问题的混合差分进化算法

鉴于传统方法用于高维复杂函数优化很容易陷入局部极小，为此提出了一类通用、易实现、具有全局优化特性的混合优化算法（CHADE算法）．该算法将混沌优化的随机性与差分进化算法（DE算法）相结合，利用混沌扰动算子增强算法的局部搜索能力；同时，随着搜索过程的进行随机地调整缩放因子和差分进化模式．多个典型高维复杂函数的数值仿真结果表明：CHADE算法寻优效率高、收敛速度快，尤其是具有避免局部极小的能力，其优化性能优于单一的DE算法．

Hybrid Differential Evolution for Complex Functions with High-dimension

Traditional optimization methods are easy to be trapped in local minima for complex functions with high-dimension.This paper proposes an effective hybrid optimization algorithm called chaos-based hybrid adaptive differential evolution(CHADE).The hybrid algorithm is general,easily implemented and is of globally parallel optimization property.The main principle of CHADE is that chaos perturbation of outstanding individuals adjusted little by little gradually to avoid the search being trapped in local minima,simultaneously,the scale factor and differential strategy adjusted randomly generation by generation.Numerical simulation results on complex functions with high-dimension show that CHADE is efficient and outstanding with strong ability to avoid being trapping in local minima and its performances are superior to DE.