Survey of quality measures for multi-objective optimization: Construction of complementary set of multi-objective quality measures

被引:51
|
作者
Laszczyk, Maciej [1 ]
Myszkowski, Pawel B. [1 ]
机构
[1] Wroclaw Univ Sci & Technol, Fac Comp Sci & Management, Ul Ignacego Lukasiewicza 5, PL-50371 Wroclaw, Poland
关键词
Survey; Multi-objective optimization; Quality measures; MS-RCPSP; Benchmark; EVOLUTIONARY ALGORITHMS; OBJECTIVE OPTIMIZATION; DIVERSITY; METRICS;
D O I
10.1016/j.swevo.2019.04.001
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In recent years interest in multiobjective optimization has flourished. Many Quality Measures (QM) have been developed to allow comparison of results gained by many methods. Unfortunately significant amount of various QMs along with the lack of imposed taxonomy have caused vagueness in the naming conventions. Hence a cohesive taxonomy is proposed that allows for classification of both existing and future QMs. This paper additionally provides thorough description of recently used QMs while attempting to unify the nomenclature. Advantages and disadvantages are shown along with the various features of the measures in given problem - as an example Multi-Skill Resource Constrained Project Scheduling Problem is given. Finally, a complementary set of QMs is proposed that can create a meaningful comparison of obtained multiobjective solutions to a multiobjective problem. Supplementary measures are proposed for specialized applications and open issues in the field are identified.
引用
收藏
页码:109 / 133
页数:25
相关论文
共 50 条
  • [1] Performance Measures for Dynamic Multi-Objective Optimization
    Camara, Mario
    Ortega, Julio
    de Toro, Francisco
    BIO-INSPIRED SYSTEMS: COMPUTATIONAL AND AMBIENT INTELLIGENCE, PT 1, 2009, 5517 : 760 - +
  • [2] Multi-Objective Quality Diversity Optimization
    Pierrot, Thomas
    Richard, Guillaume
    Beguir, Karim
    Cully, Antoine
    PROCEEDINGS OF THE 2022 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO'22), 2022, : 139 - 147
  • [3] On Dynamic Multi-Objective Optimization, Classification and Performance Measures
    Tantar, Emilia
    Tantar, Alexandru-Adrian
    Bouvry, Pascal
    2011 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2011, : 2759 - 2766
  • [4] A note on multi-objective information measures
    Padet, C
    Ramer, A
    Yager, R
    ADVANCES IN INTELLIGENT COMPUTING - IPMU '94, 1995, 945 : 70 - 76
  • [5] Multi-objective optimization of air quality monitoring
    Dimosthenis A. Sarigiannis
    Michaela Saisana
    Environmental Monitoring and Assessment, 2008, 136 : 87 - 99
  • [6] Multi-objective optimization of air quality monitoring
    Sarigiannis, Dimosthenis A.
    Saisana, Michaela
    ENVIRONMENTAL MONITORING AND ASSESSMENT, 2008, 136 (1-3) : 87 - 99
  • [7] Multi-objective Optimization for Part Quality in Stereolithography
    Roysarkar, K. P.
    Banerjee, P. S.
    Sinha, A.
    Banerjee, M. K.
    CIE: 2009 INTERNATIONAL CONFERENCE ON COMPUTERS AND INDUSTRIAL ENGINEERING, VOLS 1-3, 2009, : 617 - 623
  • [8] Research on Performance Measures of Multi-objective Optimization Evolutionary Algorithmsa
    Zhang Lili
    Zeng Wenhua
    2008 3RD INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEM AND KNOWLEDGE ENGINEERING, VOLS 1 AND 2, 2008, : 502 - +
  • [9] Scalable benchmarks and performance measures for dynamic multi-objective optimization
    Sun, Baiqing
    Zhang, Changsheng
    Zhao, Haitong
    Yu, Zhang
    APPLIED SOFT COMPUTING, 2024, 159
  • [10] A Survey on Dynamic Multi-Objective Optimization
    Liu R.-C.
    Li J.-X.
    Liu J.
    Jiao L.-C.
    Jisuanji Xuebao/Chinese Journal of Computers, 2020, 43 (07): : 1246 - 1278