文献类型：期刊文献

英文题名：Searching for Robustness Intervals in Evolutionary Robust Optimization

作者：Du, Wei[1];Song, Wenjiang[1];Tang, Yang[1];Jin, Yaochu[1,2];Qian, Feng[1]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Smart Mfg Energy Chem Proc, Shanghai 200237, Peoples R China;[2]Univ Surrey, Dept Comp Sci, Guildford GU2 7XH, Surrey, England

年份：2022

卷号：26

期号：1

起止页码：58

外文期刊名：IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION

收录：;EI(收录号：20213310763494);WOS:【SCI-EXPANDED(收录号:WOS:000748370700009)】；

基金：This work was supported in part by the National Natural Science Foundation of China (Basic Science Center Program) under Grant 61988101; in part by International (Regional) Cooperation and Exchange Project under Grant 61720106008; in part by National Natural Science Fund for Distinguished Young Scholars under Grant 61725301; in part by Fundamental Research Funds for the Central Universities under Grant 222202017006; in part by the Natural Science Foundation of Shanghai under Grant 21ZR1416100; in part by the Program of Shanghai Academic Research Leader under Grant 20XD1401300; in part by the Project of the Humanities and Social Sciences on Young Fund of the Ministry of Education in China under Grant 20YJCZH052; and in part by the Programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017.

语种：英文

外文关键词：Robustness; Optimization; Perturbation methods; Uncertainty; Task analysis; Schedules; Monte Carlo methods; Bilevel optimization problem; evolutionary robust optimization; heuristic-based approach; robustness interval

摘要：In many real-world optimization applications, a goal solution (i.e., scenario) is often provided by a user according to his/her experience. Due to the presence of a wide range of uncertainties, one may be interested in identifying the robustness interval of the solution, i.e., the range of the decision variables in which the solution remains robust. This article investigates how to find the robustness intervals of the goal solution in evolutionary robust optimization and formulates this as a bilevel optimization problem. Then, a novel algorithm framework is proposed to solve the bilevel problem: an efficient heuristic-based approach is developed to optimize the upper level task, while a global optimizer is utilized to tackle the lower level task. The proposed heuristic-based approach contains four key components: 1) peak detection; 2) peak allocation; 3) calculation of the next perturbation value; and 4) robustness interval fine-tuning, aiming to enhance the efficiency of searching for the target intervals. Finally, three types of artificial test problems and a practical problem are provided to verify the effectiveness of the proposed algorithm framework. The results show that all the robustness intervals can be successfully found when the goal solution is given by means of the proposed algorithm framework.