Performance Measurement for Interactive Multi-objective Evolutionary Algorithms

被引：7
|
作者：
Long Nguyen [1 ]
Hung Nguyen Xuan [2 ]
Lam Thu Bui [2 ]
机构：
[1] Natl Def Acad, Dept Informat Technol, Hanoi, Vietnam
[2] Le Quy Don Tech Univ, Fac Informat Technol, Hanoi, Vietnam
来源：
2015 SEVENTH INTERNATIONAL CONFERENCE ON KNOWLEDGE AND SYSTEMS ENGINEERING (KSE) | 2015年
关键词：
D O I：
10.1109/KSE.2015.51
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper suggests to use a different metric for performance of multiple-point interactive evolutionary multi-objective algorithms. We defined a preferred region based on a set of user's reference points. Based on the preferred region, we also define a User based Front (UbF) which is generated from the preferred region. UbF is used in calculation of Generational Distance (GD) and Inverse Generational Distance (IGD). The usage of the metric in experiments indicated meaningful comparisons for interactive multi-objective evolutionary algorithms using multiple reference points.
引用
收藏
页码：302 / 305
页数：4
相关论文
共 50 条
  • [41] Convergence performance comparison of quantum-inspired multi-objective evolutionary algorithms
    Li, Zhiyong
    Rudolph, Guenter
    Li, Kenli
    COMPUTERS & MATHEMATICS WITH APPLICATIONS, 2009, 57 (11-12) : 1843 - 1854
  • [42] Evaluating evolutionary multi-objective optimization algorithms using running performance metrics
    Deb, K
    Jain, S
    RECENT ADVANCES IN SIMULATED EVOLUTION AND LEARNING, 2004, 2 : 307 - 326
  • [43] On the Performance of Master-Slave Parallelization Methods for Multi-Objective Evolutionary Algorithms
    Zavoianu, Alexandru-Ciprian
    Lughofer, Edwin
    Koppelstaetter, Werner
    Weidenholzer, Guenther
    Amrhein, Wolfgang
    Klement, Erich Peter
    ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, PT II, 2013, 7895 : 122 - +
  • [44] Stopping criteria in evolutionary algorithms for multi-objective performance optimization of integrated inductors
    Fernandez, Francisco V.
    Esteban-Muller, J.
    Roca, Elisenda
    Castro-Lopez, Rafael
    2010 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2010,
  • [45] Difficulties in Fair Performance Comparison of Multi-Objective Evolutionary Algorithms [Research Frontier]
    Ishibuchi, Hisao
    Pang, Lie Meng
    Shang, Ke
    IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE, 2022, 17 (01) : 86 - 101
  • [46] Diversity Assessment of Multi-Objective Evolutionary Algorithms: Performance Metric and Benchmark Problems
    Tian, Ye
    Cheng, Ran
    Zhang, Xingyi
    Li, Miqing
    Jin, Yaochu
    IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE, 2019, 14 (03) : 61 - 74
  • [47] IMPROVING THE PERFORMANCE OF MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS USING THE ISLAND PARALLEL MODEL
    Marquez, A.
    Gil, C.
    Banos, R.
    Gomez, J.
    PARALLEL PROCESSING LETTERS, 2007, 17 (02) : 127 - 139
  • [48] InDM2: Interactive Dynamic Multi-Objective Decision Making Using Evolutionary Algorithms
    Nebro, Antonio J.
    Ruiz, Ana B.
    Barba-Gonzalez, Cristobal
    Garcia-Nieto, Jose
    Luque, Mariano
    Aldana-Montes, Jose F.
    SWARM AND EVOLUTIONARY COMPUTATION, 2018, 40 : 184 - 195
  • [49] A combination of geometric buffer technique and ray based interactive methods for multi-objective evolutionary algorithms
    Long Nguyen
    Dinh Nguyen Duc
    PROCEEDINGS OF 2018 10TH INTERNATIONAL CONFERENCE ON KNOWLEDGE AND SYSTEMS ENGINEERING (KSE), 2018, : 141 - 145
  • [50] General framework for localised multi-objective evolutionary algorithms
    Wang, Rui
    Fleming, Peter J.
    Purshouse, Robin C.
    INFORMATION SCIENCES, 2014, 258 : 29 - 53
12345