Directional Pareto Front and Its Estimation to Encourage Multi-Objective Decision-Making

被引:2
作者
Takagi, Tomoaki [1 ]
Takadama, Keiki [1 ]
Sato, Hiroyuki [1 ]
机构
[1] Univ Electrocommun, Grad Sch Informat & Engn, Chofu, Tokyo 1828585, Japan
关键词
Estimation; Optimization; Pareto optimization; Decision making; Response surface methodology; Linear programming; Multi-objective optimization; multi-objective decision-making; evolutionary algorithm; response surface methodology; EVOLUTIONARY ALGORITHM; INTERPOLATION; CONVERGENCE; PERFORMANCE; DIVERSITY;
D O I
10.1109/ACCESS.2023.3250238
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This work introduces the following concepts of directional and estimated directional Pareto front to encourage multi-objective decision making, especially when the Pareto front exists in limited regions in the objective space. The general output of multi-objective optimization is a set of non-dominated solutions to approximate the Pareto front. When the Pareto front exists in limited regions, few solutions are obtained and presented to the decision maker. The limited output representing the objective value trade-off is a barrier to multi-objective decision making. The directional Pareto front introduced in this study is a superset of the Pareto front and supplements the objective value trade-off between the Pareto fronts. The estimated directional Pareto front is a response surface that represents the directional Pareto front using a limited number of points, which are objective vectors of the obtained solutions. The experimental results show that the directional Pareto front and the estimated front provide an objective value trade-off even in areas where the Pareto front does not exist and enhances the explanation of the objective space of the target problem.
引用
收藏
页码:20619 / 20634
页数:16
相关论文
共 43 条
  • [1] Batista LS, 2011, LECT NOTES COMPUT SC, V6576, P76, DOI 10.1007/978-3-642-19893-9_6
  • [2] Bhattacharjee Kalyan Shankar, 2017, AI 2017: Advances in Artificial Intelligence. 30th Australasian Joint Conference. Proceedings: LNAI 10400, P93, DOI 10.1007/978-3-319-63004-5_8
  • [3] An approach to generate comprehensive piecewise linear interpolation of pareto outcomes to aid decision making
    Bhattacharjee, Kalyan Shankar
    Singh, Hemant Kumar
    Ray, Tapabrata
    [J]. JOURNAL OF GLOBAL OPTIMIZATION, 2017, 68 (01) : 71 - 93
  • [4] Bowman VJ., 1975, Lect. Notes Econ. Math. Syst, V130, P76, DOI [DOI 10.1007/978-3-642-87563-25, DOI 10.1007/978-3-642-87563-2_5]
  • [5] Chankong V., 1983, MULTIOBJECTIVE DECIS
  • [6] A benchmark test suite for evolutionary many-objective optimization
    Cheng, Ran
    Li, Miqing
    Tian, Ye
    Zhang, Xingyi
    Yang, Shengxiang
    Jin, Yaochu
    Yao, Xin
    [J]. COMPLEX & INTELLIGENT SYSTEMS, 2017, 3 (01) : 67 - 81
  • [7] A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization
    Cheng, Ran
    Jin, Yaochu
    Olhofer, Markus
    Sendhoff, Bernhard
    [J]. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2016, 20 (05) : 773 - 791
  • [8] Coello CCA., 2007, EVOLUTIONARY ALGORIT, DOI [DOI 10.1007/978-0-387-36797-2, 10.1007/978-0-387-36797-2]
  • [9] Normal-boundary intersection: A new method for generating the Pareto surface in nonlinear multicriteria optimization problems
    Das, I
    Dennis, JE
    [J]. SIAM JOURNAL ON OPTIMIZATION, 1998, 8 (03) : 631 - 657
  • [10] Deb K, 2002, IEEE C EVOL COMPUTAT, P825, DOI 10.1109/CEC.2002.1007032