Simultaneous use of different scalarizing functions in MOEA/D
Simultaneous use of different scalarizing functions in MOEA/D
复制标题
DOI：
10.1145/1830483.1830577
复制
发表时间：
2010-07
期刊：
影响因子：
--
通讯作者：
H. Ishibuchi;Yuji Sakane;Noritaka Tsukamoto;Y. Nojima
中科院分区：
文献类型：
--
作者：
H. Ishibuchi;Yuji Sakane;Noritaka Tsukamoto;Y. Nojima
在过去的二十年里，使用Pareto优势进行适应度评价一直是进化多目标优化的主流。最近，它已被指出，在一些研究中，Pareto优势为基础的算法并不总是工作得很好的多目标问题与许多目标。基于标度化函数的适应度评价是一种很有前途的替代帕累托优势，特别是在多目标的情况下。典型的基于标量化函数的算法是张和李（2007）的MOEA/D（基于分解的多目标进化算法）。它的高搜索能力已经在各种问题上得到了证明。MOEA/D的一个重要实现问题是选择标量化函数，因为它的搜索能力强烈依赖于这个选择。然而，要为每个多目标问题选择合适的标量化函数并不容易。在本文中，我们提出了同时使用不同类型的标量化函数的想法。例如，加权切比雪夫（Chebyshev）和加权和都用于适应度评估。我们研究两种方法来实现我们的想法。一种是使用多个权向量网格，另一种是在单个网格中交替地为每个权向量分配不同的标量化函数。
The use of Pareto dominance for fitness evaluation has been the mainstream in evolutionary multiobjective optimization for the last two decades. Recently, it has been pointed out in some studies that Pareto dominance-based algorithms do not always work well on multiobjective problems with many objectives. Scalarizing function-based fitness evaluation is a promising alternative to Pareto dominance especially for the case of many objectives. A representative scalarizing function-based algorithm is MOEA/D (multiobjective evolutionary algorithm based on decomposition) of Zhang & Li (2007). Its high search ability has already been shown for various problems. One important implementation issue of MOEA/D is a choice of a scalarizing function because its search ability strongly depends on this choice. It is, however, not easy to choose an appropriate scalarizing function for each multiobjective problem. In this paper, we propose an idea of using different types of scalarizing functions simultaneously. For example, both the weighted Tchebycheff (Chebyshev) and the weighted sum are used for fitness evaluation. We examine two methods for implementing our idea. One is to use multiple grids of weight vectors and the other is to assign a different scalarizing function alternately to each weight vector in a single grid.