Robustness of Multi-objective Optimal Solutions to Physical Deterioration through Active Control

被引:0
|
作者
Avigad, Gideon
Eisenstadt, Erella
机构
来源
关键词
Evolutionary multi-objective; Physical deterioration;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper, we suggest a novel problem within the context of multi objective optimization. It concerns the control of solutions' performances in multi objective spaces. The motivation for controlling these performances comes from an inspiration to improve the robustness of solutions to physical deterioration. When deterioration occurs, the solution performances degrade. In order to prevent extended degradation and loss of robustness, an active control is implemented. Naturally, in order to enable such a control, the solution (product) should have tunable parameters that would serve as the controlled variables. Optimizing the solution for such a problem means that the tunable parameters should be found and their manipulation determined. Here the optimal solutions and the controller are designed using multi and single objective evolutionary algorithms. The paper is concluded with a discussion on the high potential of the approach for research and real life applications.
引用
收藏
页码:394 / 403
页数:10
相关论文
共 50 条
  • [21] Sensitivity Analysis of Multi-objective Optimal Control Problems
    Toan, N. T.
    Thuy, L. Q.
    APPLIED MATHEMATICS AND OPTIMIZATION, 2021, 84 (03): : 3517 - 3545
  • [22] Multi-objective Optimal Design of Hybrid Active Power Filter
    Jiang, You-hua
    Chang, Jian
    Tian, Shu-jin
    INTERNATIONAL CONFERENCE ON ADVANCED MANUFACTURE TECHNOLOGY AND INDUSTRIAL APPLICATION, AMTIA 2016, 2016, : 127 - 131
  • [23] Multi-objective Optimal Design of μ-controller for Active Magnetic Bearing
    Li Y.
    Zhu C.
    Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering, 2023, 43 (13): : 5192 - 5202
  • [24] Multi-objective optimal design for hybrid active power filter
    He, Na
    Hu, An
    Gao, Qiang
    Yang, Hua
    Dianji yu Kongzhi Xuebao/Electric Machines and Control, 2010, 14 (11): : 49 - 57
  • [25] Understanding Clusters of Optimal Solutions in Multi-Objective Decision Problems
    Veerappa, Varsha
    Letier, Emmanuel
    2011 19TH IEEE INTERNATIONAL REQUIREMENTS ENGINEERING CONFERENCE (RE), 2011, : 89 - 98
  • [26] Multi-objective Optimal Placement of PMU in Active Distribution Network
    Wang S.
    Yan Z.
    Kong X.
    Guo R.
    Xu X.
    Dianwang Jishu/Power System Technology, 2019, 43 (03): : 833 - 840
  • [27] PARETO OPTIMAL SOLUTIONS FOR MULTI-OBJECTIVE GENERALIZED ASSIGNMENT PROBLEM
    Prakash, S.
    Sharma, M. K.
    Singh, A.
    SOUTH AFRICAN JOURNAL OF INDUSTRIAL ENGINEERING, 2010, 21 (02): : 91 - 100
  • [28] Optimal solutions to multi-objective robust fault detection problems
    Liu, Nike
    Zhou, Kemin
    PROCEEDINGS OF THE 46TH IEEE CONFERENCE ON DECISION AND CONTROL, VOLS 1-14, 2007, : 715 - 722
  • [29] Assessing the robustness of solutions to a multi-objective model of an energy management system aggregator
    Carreiro, Andreia M.
    Antunes, Carlos Henggeler
    Jorge, Humberto
    PROCEEDINGS OF 2016 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI), 2016,
  • [30] Multi-objective infinite horizon optimal control problems: characterization of the Pareto fronts and Pareto solutions
    Chorobura, Ana Paula
    COMPUTATIONAL & APPLIED MATHEMATICS, 2021, 40 (08):