{"title":"优化计算预算分配,为多目标仿真优化问题选择最优子集","authors":"","doi":"10.1016/j.automatica.2024.111829","DOIUrl":null,"url":null,"abstract":"<div><p>This study aims to develop an efficient budget allocation procedure for the problem of selecting an optimal subset of designs from a finite number of alternative designs in stochastic environments. The optimal subset might contain more alternative designs beyond the Pareto optimal ones. In this study, we adopt the Pareto rank to measure the performance of each design and define the optimal subset. Our objective is to minimize the probability that the optimal subset is falsely selected within a fixed limited simulation budget. We propose an upper bound of the probability of false selection and derive an asymptotically optimal simulation budget allocation rule based on the large deviation theory. We also provide some useful insights into how the simulation budget can be allocated to identify the optimal subset. The proposed budget allocation algorithm is compared with existing methods through numerical experiments, and the results show the efficiency of our proposed algorithm.</p></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":null,"pages":null},"PeriodicalIF":4.8000,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimal computing budget allocation for selecting the optimal subset of multi-objective simulation optimization problems\",\"authors\":\"\",\"doi\":\"10.1016/j.automatica.2024.111829\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This study aims to develop an efficient budget allocation procedure for the problem of selecting an optimal subset of designs from a finite number of alternative designs in stochastic environments. The optimal subset might contain more alternative designs beyond the Pareto optimal ones. In this study, we adopt the Pareto rank to measure the performance of each design and define the optimal subset. Our objective is to minimize the probability that the optimal subset is falsely selected within a fixed limited simulation budget. We propose an upper bound of the probability of false selection and derive an asymptotically optimal simulation budget allocation rule based on the large deviation theory. We also provide some useful insights into how the simulation budget can be allocated to identify the optimal subset. The proposed budget allocation algorithm is compared with existing methods through numerical experiments, and the results show the efficiency of our proposed algorithm.</p></div>\",\"PeriodicalId\":55413,\"journal\":{\"name\":\"Automatica\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.8000,\"publicationDate\":\"2024-08-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Automatica\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0005109824003236\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Automatica","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0005109824003236","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Optimal computing budget allocation for selecting the optimal subset of multi-objective simulation optimization problems
This study aims to develop an efficient budget allocation procedure for the problem of selecting an optimal subset of designs from a finite number of alternative designs in stochastic environments. The optimal subset might contain more alternative designs beyond the Pareto optimal ones. In this study, we adopt the Pareto rank to measure the performance of each design and define the optimal subset. Our objective is to minimize the probability that the optimal subset is falsely selected within a fixed limited simulation budget. We propose an upper bound of the probability of false selection and derive an asymptotically optimal simulation budget allocation rule based on the large deviation theory. We also provide some useful insights into how the simulation budget can be allocated to identify the optimal subset. The proposed budget allocation algorithm is compared with existing methods through numerical experiments, and the results show the efficiency of our proposed algorithm.
期刊介绍:
Automatica is a leading archival publication in the field of systems and control. The field encompasses today a broad set of areas and topics, and is thriving not only within itself but also in terms of its impact on other fields, such as communications, computers, biology, energy and economics. Since its inception in 1963, Automatica has kept abreast with the evolution of the field over the years, and has emerged as a leading publication driving the trends in the field.
After being founded in 1963, Automatica became a journal of the International Federation of Automatic Control (IFAC) in 1969. It features a characteristic blend of theoretical and applied papers of archival, lasting value, reporting cutting edge research results by authors across the globe. It features articles in distinct categories, including regular, brief and survey papers, technical communiqués, correspondence items, as well as reviews on published books of interest to the readership. It occasionally publishes special issues on emerging new topics or established mature topics of interest to a broad audience.
Automatica solicits original high-quality contributions in all the categories listed above, and in all areas of systems and control interpreted in a broad sense and evolving constantly. They may be submitted directly to a subject editor or to the Editor-in-Chief if not sure about the subject area. Editorial procedures in place assure careful, fair, and prompt handling of all submitted articles. Accepted papers appear in the journal in the shortest time feasible given production time constraints.