【摘要】 差分进化算法（DE）是一种较新的进化计算技术，具有结构简单、便于编程、求解收敛快速等优点，得到了广泛的关注和应用。为了解决经典DE计算开销大，参数设置与问题本身过于相关等缺陷，提出了一种具有多种自适应机制的改进差分进化算法(MSDE)，它采用了一种自适应变异算子，可根据进化代数实时地调整变异步长，从而提高算法的求解精度，同时采用一种自适应控制参数机制，以加快算法收敛，提高算法求解成功率。通过在MATLAB仿真环境下对著名的基准测试函数分别进行求解，将改进后的算法和已有的多种优化算法进行比较，结果表明，改进的MSDE算法性能明显优于已知的算法，证明自适应是一种有效的改进思路。更多还原

【Abstract】 Differential Evolution(DE) is a novel evolutionary computation technique, which has attracted much attention and wide applications for its simple concept, easy implementation and quick convergence. In order to tackle much overhead, problem-dependent parameters, etc and enhance the precision of classical DE, a Multiple Self-adapting DE(MSDE) algorithm is proposed by using an dynamical mutation operator adjusting the step size with evolution and a self-adapting mechanism to adjust parameters to improve the convergence and robustness. Experiments of solving well-known benchmark functions in MATLAB show the improved approach outperforms existing algorithms, and self-adapting mechanisms are effective improvement ideas.更多还原