Constrained optimization differential evolution algorithm usingaugmented Lagrange penalty function
结合增广Lagrange罚函数的约束优化差分进化算法

Using augmented Lagrange penalty function to deal with the constrained conditions, this paper proposed a modified constrained optimization differential evolution algorithm. It converted the general constrained optimization problem into a bound constrained optimization problem. In the process of evolution, divided the initial population into two subpopulations, i. e. elite and general subpopulations, which used different mutation strategies to balance the ability of global and local search respectively. It tested ten classic Benchmarks problems, the experiment results show that the proposed algorithm is an effective way for constrained optimization problems.