文献类型：期刊文献

英文题名：A framework for high-dimensional robust evolutionary multi-objective optimization

作者：Du, Wei[1]; Tong, Le[2]; Tang, Yang[1]

机构：[1] Key Laboratory of Advanced Control, Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, China; [2] College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai, China

年份：2018

起止页码：1791

外文期刊名：GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion

收录：EI(收录号：20183405711329)

语种：英文

外文关键词：Multiobjective optimization - Pareto principle - Decision making

摘要：This paper proposes a framework for solving high-dimensional robust multi-objective optimization problems. A decision variable classification-based framework is developed to search for robust Pareto-optimal solutions. The decision variables are classified as highly and weakly robustness-related variables based on their contributions to the robustness of candidate solutions. In the case study, an order scheduling problem in the apparel industry is investigated via the proposed framework. The experimental results reveal that the performance of robust evolutionary optimization can be greatly improved via analyzing the properties of decision variables and then decomposing the high-dimensional robust multiobjective optimization problem. ? 2018 Association for Computing Machinery.