Differential Evolution for Nonlinear Constrained Optimization Using Non-stationary Multi-stage Assignment Penalty Function
采用非固定多段映射罚函数的非线性约束优化差分进化算法

Using non-stationary multi-stage assignment penalty function to deal with the constrained conditions,a modified differential evolution(MDE) for nonlinear constrained optimization is proposed.In order to improve global convergence and convergence speed of the algorithm,two different mutation scheme of DE were combined,and simulation anneal tactics was adapted,which ensure the algorithm has good global exploring ability at the beginning stage and good local exploring ability at the last stage.Several classic Benchmarks functions were tested,the experiment results show that the MDE has powerful global exploring ability,good robustness,high precision,and fast convergence speed.So it is an effective way for nonlinear constrained optimization problems.