论文标题
多目标优化的内存梯度方法
Memory gradient method for multiobjective optimization
论文作者
论文摘要
在本文中，我们提出了一种新的下降方法，称为多目标存储梯度方法，用于查找多目标优化问题的帕累托关键点。此方法中的主要思想是选择当前下降方向和过去的多步迭代信息作为新的搜索方向，并通过两种类型的策略来获得步骤。事实证明，具有合适参数的开发方向始终满足每次迭代的足够下降条件。基于温和的假设，我们获得了我们方法的全局收敛和收敛速率。进行计算实验以证明所提出的方法的有效性。
In this paper, we propose a new descent method, termed as multiobjective memory gradient method, for finding Pareto critical points of a multiobjective optimization problem. The main thought in this method is to select a combination of the current descent direction and past multi-step iterative information as a new search direction and to obtain a stepsize by virtue of two types of strategies. It is proved that the developed direction with suitable parameters always satisfies the sufficient descent condition at each iteration. Based on mild assumptions, we obtain the global convergence and the rates of convergence for our method. Computational experiments are given to demonstrate the effectiveness of the proposed method.
