Interactive Evolutionary Multiobjective Optimization with Modular Physical User Interface

被引:1
作者
Mazumdar, Atanu [1 ]
Otayagich, Stefan [1 ]
Miettinen, Kaisa [1 ]
机构
[1] Univ Jyvaskyla, Fac Informat Technol, POB 35 Agora, FI-40014 Jyvaskyla, Finland
来源
PROCEEDINGS OF THE 2022 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION, GECCO 2022 | 2022年
关键词
preference information; multicriteria decision making; decision support; decomposition-based MOEA; human machine interface; tactile interface;
D O I
10.1145/3520304.3534008
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Incorporating the preferences of a domain expert, a decision-maker (DM), in solving multiobjective optimization problems increased in popularity in recent years. The DM can choose to use different types of preferences depending on his/her comfort, requirements, or the problem being solved. Most papers, where preference-based and interactive algorithms have been proposed, do not pay attention to the user interfaces and input devices. If they do, they use character or graphics-based preference input methods. We propose the option of using a physical or tactile input device that gives the DM a better sense of control over providing his/her preferences. However, off the shelf hardware devices are not tailored to solve multiobjective optimization problems and provide many controls that may increase the cognitive load on the DM. In this paper, we propose a fully modular physical user interface to input preference information for solving multiobjective optimization problems. The modularity allows to arrange each input module in various ways depending on the algorithm, DM's requirements, or the problem being solved. The device can be used with any computer and uses web-based visualizations. We demonstrate the potential of the physical interface by solving a real-world problem with an interactive decomposition-based multiobjective evolutionary algorithm.
引用
收藏
页码:1835 / 1843
页数:9
相关论文
共 27 条
[1]   Assessing the Performance of Interactive Multiobjective Optimization Methods: A Survey [J].
Afsar, Bekir ;
Miettinen, Kaisa ;
Ruiz, Francisco .
ACM COMPUTING SURVEYS, 2021, 54 (04)
[2]   Brain-Computer Evolutionary Multiobjective Optimization: A Genetic Algorithm Adapting to the Decision Maker [J].
Battiti, Roberto ;
Passerini, Andrea .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2010, 14 (05) :671-687
[3]  
Beaufort F., 2016, ACCESS USB DEVICES W
[4]   Mouse, Tactile, and Tangible Input for 3D Manipulation [J].
Besancon, Lonni ;
Issartel, Paul ;
Ammi, Mehdi ;
Isenberg, Tobias .
PROCEEDINGS OF THE 2017 ACM SIGCHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'17), 2017, :4727-4740
[5]   A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization [J].
Cheng, Ran ;
Jin, Yaochu ;
Olhofer, Markus ;
Sendhoff, Bernhard .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2016, 20 (05) :773-791
[6]   An Interactive Simple Indicator-Based Evolutionary Algorithm (I-SIBEA) for Multiobjective Optimization Problems [J].
Chugh, Tinkle ;
Sindhya, Karthik ;
Hakanen, Jussi ;
Miettinen, Kaisa .
EVOLUTIONARY MULTI-CRITERION OPTIMIZATION, PT I, 2015, 9018 :277-291
[7]  
Deb K, 2005, LECT NOTES COMPUT SC, V3776, P690
[8]   New advancements, challenges and opportunities of multi-storey modular buildings - A state-of-the-art review [J].
Ferdous, Wahid ;
Bai, Yu ;
Tuan Duc Ngo ;
Manalo, Allan ;
Mendis, Priyan .
ENGINEERING STRUCTURES, 2019, 183 :883-893
[9]  
Gong MG, 2011, GECCO-2011: PROCEEDINGS OF THE 13TH ANNUAL GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, P721
[10]   Modular industrial robots as the tool of process automation in robotized manufacturing cells [J].
Gwiazda, A. ;
Banas, W. ;
Sekala, A. ;
Foit, K. ;
Hryniewicz, P. ;
Kost, G. .
MODERN TECHNOLOGIES IN INDUSTRIAL ENGINEERING (MODTECH2015), 2015, 95