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

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法处理问题的约束条件,提出了一种用改进差分进化算法求解非线性约束优化问题的新方法.结合差分进化算法两种不同变异方式的特点,引入模拟退火策略,使算法在搜索的初始阶段有较强的全局搜索能力,而在后阶段有较强的局部搜索能力,以提高算法的全局收敛性和收敛速率.用几个典型Benchmarks函数进行了测试,实验结果表明,该方法全局搜索能力强,鲁棒性好,精度高,收敛速度快,是一种求解非线性约束优化问题的有效方法.

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.