A survey of multi-objective optimization methods and their applications for nuclear scientists and engineers

被引:40
|
作者
Stewart, Ryan H. [1 ]
Palmer, Todd S. [1 ]
DuPont, Bryony [1 ]
机构
[1] Oregon State Univ, 1500 SW Jefferson St, Corvallis, OR 97331 USA
关键词
Multi-objective problem; Optimization; Nuclear engineering; INSPIRED EVOLUTIONARY ALGORITHM; FUEL LOADING PATTERN; OBJECTIVE REDUCTION; GENETIC ALGORITHMS; TABU SEARCH; SUPPORT; DESIGN;
D O I
10.1016/j.pnucene.2021.103830
中图分类号
TL [原子能技术]; O571 [原子核物理学];
学科分类号
0827 ; 082701 ;
摘要
Problems in nuclear engineering-such as reactor core design-involve a multitude of design variables including fuel or assembly configurations; all of which require careful consideration when constrained by a set of objectives such as fuel temperature or assembly power density. Reactor design is one facet of nuclear engineering, where many nuclear engineers often face large multi-objective problems to solve. These types of problems can be solved by relying upon experts to aid in reducing the design space required for multi-objective optimization, however, computational optimization algorithms have been used to generate optimal solutions with reproducibility and quantitative evidence for designs. We present a review of multi-objective optimization literature including an introduction to optimization theory, commonly used multi-objective optimization algorithms, and current applications in nuclear science and engineering. From this review, researchers will glean an understanding of multi-objective optimization algorithms that are currently available, and gain a fundamental understanding of how to apply these techniques to a wide variety of problems in the fields of nuclear science and engineering.
引用
收藏
页数:16
相关论文
共 50 条
  • [1] Survey of multi-objective optimization methods for engineering
    R.T. Marler
    J.S. Arora
    Structural and Multidisciplinary Optimization, 2004, 26 : 369 - 395
  • [2] Survey of multi-objective optimization methods for engineering
    Marler, RT
    Arora, JS
    STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, 2004, 26 (06) : 369 - 395
  • [3] Multi-objective optimization techniques: a survey of the state-of-the-art and applications Multi-objective optimization techniques
    Saini, Naveen
    Saha, Sriparna
    EUROPEAN PHYSICAL JOURNAL-SPECIAL TOPICS, 2021, 230 (10): : 2319 - 2335
  • [4] A review of multi-objective optimization: Methods and its applications
    Gunantara, Nyoman
    COGENT ENGINEERING, 2018, 5 (01): : 1 - 16
  • [5] Survey of multi-objective particle swarm optimization algorithms and their applications
    Ye Q.
    Wang W.
    Wang Z.
    Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science), 2024, 58 (06): : 1107 - 1120+1232
  • [6] A Survey on Dynamic Multi-Objective Optimization
    Liu R.-C.
    Li J.-X.
    Liu J.
    Jiao L.-C.
    Jisuanji Xuebao/Chinese Journal of Computers, 2020, 43 (07): : 1246 - 1278
  • [7] Stochastic multi-objective optimization: a survey on non-scalarizing methods
    Gutjahr, Walter J.
    Pichler, Alois
    ANNALS OF OPERATIONS RESEARCH, 2016, 236 (02) : 475 - 499
  • [8] Stochastic multi-objective optimization: a survey on non-scalarizing methods
    Walter J. Gutjahr
    Alois Pichler
    Annals of Operations Research, 2016, 236 : 475 - 499
  • [9] A Comprehensive Survey on Multi-objective Evolutionary Optimization in Power System Applications
    Pindoriya, N. M.
    Singh, S. N.
    Lee, Kwang Y.
    IEEE POWER AND ENERGY SOCIETY GENERAL MEETING 2010, 2010,
  • [10] A comprehensive survey on NSGA-II for multi-objective optimization and applications
    Ma, Haiping
    Zhang, Yajing
    Sun, Shengyi
    Liu, Ting
    Shan, Yu
    ARTIFICIAL INTELLIGENCE REVIEW, 2023, 56 (12) : 15217 - 15270