针对多目标优化的混和差分演化算法

作为一种简单而有效的新兴计算技术，差分演化算法（DE）已受到学术界和工程界的广泛关注，并且已经在多峰函数优化，数据过滤，多目标优化等十九个大方向上取得了许多成功应用。为此，对围绕差分演化算法的相关背景，原理、特点、改进等方面进行简单介绍．Hookeand Jeeves方法是一种经典的局部搜索算法，将其与差分演化算法结合来求解多目标优化问题，提高了解的收敛质量，因而从整体上提高了算法的性能，并且测试结果也说明了该算法的可行性。

Research in Differential Evolution

As a novel evolutionary computing technique, differential evolution （DE） is simple and effective,which has already been applied succesively to many areas such as multimodal function optimization,neural network learning, digital filter design,multi_objective optimization and so on.A simple introduce on DE is presented in aspect of mechanism,feature and improvements. Hooke and Jeeves direct search method is a classical one, which is combined with DE, can improve DE s＂ convergence performance in the whole search process, and the test shows thismethod is feasible.