The impact of Quality Indicators on the rating of Multi-objective Evolutionary Algorithms

被引:30
作者
Ravber, Miha [1 ]
Mernik, Marjan [1 ]
Crepinkek, Matej [1 ]
机构
[1] Univ Maribor, Fac Elect Engn & Comp Sci, Maribor, Slovenia
关键词
Multi-objective optimization; Evolutionary Algorithms; Quality Indicator; Performance assessment; Chess rating; BEE COLONY ALGORITHM; PERFORMANCE ASSESSMENT; OPTIMIZATION; DIVERSITY; SYSTEM;
D O I
10.1016/j.asoc.2017.01.038
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Evaluating and comparing multi-objective optimizers is an important issue. But, when doing a comparison, it has to be noted that the results can be influenced highly by the selected Quality Indicator. Therefore, the impact of individual Quality Indicators on the ranking of Multi-objective Optimizers in the proposed method must be analyzed beforehand. In this paper the comparison of several different Quality Indicators with a method called Chess Rating System for Evolutionary Algorithms (CRS4EAs) was conducted in order to get a better insight on their characteristics and how they affect the ranking of Multi-objective Evolutionary Algorithms (MOEAs). Although it is expected that Quality Indicators with the same optimization goals would yield a similar ranking of MOEAs, it has been shown that results can be contradictory and significantly different. Consequently, revealing that claims about the superiority of one MOEA over another can be misleading. (c) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码:265 / 275
页数:11
相关论文
共 50 条
  • [31] Runtime Analyses of Multi-Objective Evolutionary Algorithms in the Presence of Noise
    Dinot, Matthieu
    Doerr, Benjamin
    Hennebelle, Ulysse
    Will, Sebastian
    PROCEEDINGS OF THE THIRTY-SECOND INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, IJCAI 2023, 2023, : 5549 - 5557
  • [32] A Systematic Review of Multi-Objective Evolutionary Algorithms Optimization Frameworks
    Patrausanu, Andrei
    Florea, Adrian
    Neghina, Mihai
    Dicoiu, Alina
    Chis, Radu
    PROCESSES, 2024, 12 (05)
  • [33] Decoding the Architectural Genome: Multi-Objective Evolutionary Algorithms in Design
    Makki, Mohammad
    Navarro-Mateu, Diego
    Showkatbakhsh, Milad
    TECHNOLOGY-ARCHITECTURE + DESIGN, 2022, 6 (01) : 68 - 79
  • [34] Evolutionary Algorithms for Multi-Objective Optimization: Performance Assessments and Comparisons
    K.C. Tan
    T.H. Lee
    E.F. Khor
    Artificial Intelligence Review, 2002, 17 (4) : 251 - 290
  • [35] Improving multi-objective evolutionary algorithms using Grammatical Evolution
    Rodriguez, Amin V. Bernabe
    Alejo-Cerezo, Braulio I.
    Coello, Carlos A. Coello
    SWARM AND EVOLUTIONARY COMPUTATION, 2024, 84
  • [36] An Adjustable Diversity Metric for Multimodal Multi-objective Evolutionary Algorithms
    Zhang, Weiwei
    Fan, Yan
    Zhang, Ningjun
    Zhang, Weizheng
    ADVANCED DATA MINING AND APPLICATIONS, ADMA 2021, PT I, 2022, 13087 : 382 - 392
  • [38] Analysis of Evolutionary Algorithms using Multi-Objective Parameter Tuning
    Ugolotti, Roberto
    Cagnoni, Stefano
    GECCO'14: PROCEEDINGS OF THE 2014 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, 2014, : 1343 - 1350
  • [39] Study The Effect of High Dimensional Objective Functions on Multi-Objective Evolutionary Algorithms
    Safi, Hayder H.
    Ucan, Osman N.
    Bayat, Oguz
    ICEMIS'18: PROCEEDINGS OF THE FOURTH INTERNATIONAL CONFERENCE ON ENGINEERING AND MIS, 2018,
  • [40] On the preferences of quality indicators for multi-objective search algorithms in search-based software engineering
    Wu, Jiahui
    Arcaini, Paolo
    Yue, Tao
    Ali, Shaukat
    Zhang, Huihui
    EMPIRICAL SOFTWARE ENGINEERING, 2022, 27 (06)