论文标题
全局多目标优化的模因程序
A Memetic Procedure for Global Multi-Objective Optimization
论文作者
论文摘要
在本文中，我们考虑了一个盒子上的多目标优化问题。这个问题非常相关，文献中已经提出了几种计算方法。它们大致分为两个主要类别：进化方法，这些方法通常非常擅长探索可行区域并在非凸案例中检索良好的解决方案，而下降方法则在有效近似良好质量的解决方案方面表现出色。在本文中，首先，我们通过数值实验确认这些方法的优点和缺点。然后，我们提出了一种结合两者的良好特征的新方法。我们称之为非主导分类模因算法（NSMA）的算法除了享受有趣的理论属性外，在我们对几种广泛使用的测试功能上进行的所有数值测试中都表现出色。
In this paper we consider multi-objective optimization problems over a box. The problem is very relevant and several computational approaches have been proposed in the literature. They broadly fall into two main classes: evolutionary methods, which are usually very good at exploring the feasible region and retrieving good solutions even in the nonconvex case, and descent methods, which excel in efficiently approximating good quality solutions. In this paper, first we confirm, through numerical experiments, the advantages and disadvantages of these approaches. Then we propose a new method which combines the good features of both. The resulting algorithm, which we call Non-dominated Sorting Memetic Algorithm (NSMA), besides enjoying interesting theoretical properties, excels in all of the numerical tests we performed on several, widely employed, test functions.
