An Evolutionary Multi- and Many-Objective Optimization Algorithm based on ISDE+ and Region Decomposition

被引:4
|
作者
Lin, Zixian [1 ]
Liu, Hailin [1 ]
Gu, Fangqing [1 ]
机构
[1] Guangdong Univ Technol, Sch Appl Math, Guangzhou, Guangdong, Peoples R China
基金
中国国家自然科学基金;
关键词
Multi-objective optimization; Many-objective optimization; Evolutionary algorithm; Decomposition;
D O I
10.1109/CIS2018.2018.00015
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
In this paper, we propose an evolutionary multi and many-objective optimization algorithm combining I-SDE(+) and region decomposition. It decomposes the objective space into a number of sub-regions by a set of direction vectors and independently calculates the indicator I-SDE(+) by using the corresponding direction vector in each subregion. Thus, the convergence direction of each sub-region is relatively adjusted. In this way, the proposed algorithm can adapt to various of Pareto Front shapes. The inferior individuals are eliminated according to the value of I-SDE(+) of each individual one by one. In the experiments, we compare the proposed algorithm with four evolutionary multi- and many-objective optimization algorithms on WFG series with different number of objectives. The result shows that the proposed algorithm promotes diversity and convergence.
引用
收藏
页码:30 / 34
页数:5
相关论文
共 50 条
  • [1] A new adaptive decomposition-based evolutionary algorithm for multi- and many-objective optimization
    Bao, Chunteng
    Gao, Diju
    Gu, Wei
    Xu, Lihong
    Goodman, Erik D.
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2023, 213
  • [2] A Decomposition-Based Evolutionary Algorithm with Adaptive Weight Vectors for Multi- and Many-objective Optimization
    Peng, Guang
    Wolter, Katinka
    [J]. APPLICATIONS OF EVOLUTIONARY COMPUTATION, EVOAPPLICATIONS 2020, 2020, 12104 : 149 - 164
  • [3] An Evolutionary Many-Objective Optimization Algorithm based on IGD Indicator and Region Decomposition
    Feng, Shuifeng
    Wen, Jiechang
    [J]. 2019 15TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND SECURITY (CIS 2019), 2019, : 206 - 210
  • [4] A decomposition-based evolutionary algorithm for scalable multi/many-objective optimization
    Chen, Jiaxin
    Ding, Jinliang
    Tan, Kay Chen
    Chen, Qingda
    [J]. MEMETIC COMPUTING, 2021, 13 (03) : 413 - 432
  • [5] A decomposition-based evolutionary algorithm for scalable multi/many-objective optimization
    Jiaxin Chen
    Jinliang Ding
    Kay Chen Tan
    Qingda Chen
    [J]. Memetic Computing, 2021, 13 : 413 - 432
  • [6] An Evolutionary Many-Objective Optimization Algorithm Based on Dominance and Decomposition
    Li, Ke
    Deb, Kalyanmoy
    Zhang, Qingfu
    Kwong, Sam
    [J]. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2015, 19 (05) : 694 - 716
  • [7] ISDE+-An Indicator for Multi and Many-Objective Optimization
    Pamulapati, Trinadh
    Mallipeddi, Rammohan
    Suganthan, Ponnuthurai Nagaratnam
    [J]. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2019, 23 (02) : 346 - 352
  • [8] Reformulating preferences into constraints for evolutionary multi- and many-objective optimization
    Hou, Zhanglu
    He, Cheng
    Cheng, Ran
    [J]. INFORMATION SCIENCES, 2020, 541 : 1 - 15
  • [9] Gap Finding and Validation in Evolutionary Multi- and Many-Objective Optimization
    Valledor Pellicer, Pablo
    Iglesias Escudero, Miguel
    Fernandez Alzueta, Silvino
    Deb, Kalyanmoy
    [J]. GECCO'20: PROCEEDINGS OF THE 2020 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, 2020, : 578 - 586
  • [10] A region division based decomposition approach for evolutionary many-objective optimization
    Liu, Ruochen
    Liu, Jin
    Zhou, Runan
    Lian, Cheng
    Bian, Renyu
    [J]. KNOWLEDGE-BASED SYSTEMS, 2020, 194