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
  • [21] Improving Robustness of Stopping Multi-objective Evolutionary Algorithms by Simultaneously Monitoring Objective and Decision Space
    Mahbub, Md Shahriar
    Wagner, Tobias
    Crema, Luigi
    GECCO'15: PROCEEDINGS OF THE 2015 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, 2015, : 711 - 718
  • [22] Multi-objective evolutionary algorithms for structural optimization
    Coello, CAC
    Pulido, GT
    Aguirre, AH
    COMPUTATIONAL FLUID AND SOLID MECHANICS 2003, VOLS 1 AND 2, PROCEEDINGS, 2003, : 2244 - 2248
  • [23] New model for multi-objective evolutionary algorithms
    Zheng, Bojin
    Li, Yuanxiang
    COMPUTATIONAL SCIENCE - ICCS 2007, PT 4, PROCEEDINGS, 2007, 4490 : 1037 - +
  • [24] Multi-objective pole placement with evolutionary algorithms
    Sanchez, Gustavo
    Villasana, Minaya
    Strefezza, Miguel
    EVOLUTIONARY MULTI-CRITERION OPTIMIZATION, PROCEEDINGS, 2007, 4403 : 417 - +
  • [25] Multi-Objective Security Constrained Unit Commitment via Hybrid Evolutionary Algorithms
    Ali, Aamir
    Shah, Arslan
    Keerio, Muhammad Usman
    Mugheri, Noor Hussain
    Abbas, Ghulam
    Touti, Ezzeddine
    Hatatah, Mohammed
    Yousef, Amr
    Bouzguenda, Mounir
    IEEE ACCESS, 2024, 12 : 6698 - 6718
  • [26] Convergence of multi-objective evolutionary algorithms to a uniformly distributed representation of the Pareto front
    Chen, Yu
    Zou, Xiufen
    Xie, Weicheng
    INFORMATION SCIENCES, 2011, 181 (16) : 3336 - 3355
  • [27] 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
  • [28] A Systematic Review of Multi-Objective Evolutionary Algorithms Optimization Frameworks
    Patrausanu, Andrei
    Florea, Adrian
    Neghina, Mihai
    Dicoiu, Alina
    Chis, Radu
    PROCESSES, 2024, 12 (05)
  • [29] Research on evolutionary multi-objective optimization algorithms
    Gong, Mao-Guo
    Jiao, Li-Cheng
    Yang, Dong-Dong
    Ma, Wen-Ping
    Ruan Jian Xue Bao/Journal of Software, 2009, 20 (02): : 271 - 289
  • [30] On the use of metamodel-assisted, multi-objective evolutionary algorithms
    Karakasis, Marios K.
    Giannakoglou, Kyriakos C.
    ENGINEERING OPTIMIZATION, 2006, 38 (08) : 941 - 957