Dynamic grid-based uniform search for solving constrained multiobjective optimization problems

被引:0
|
作者
Jiawei Yuan
机构
[1] Guangdong University of Technology,
来源
Memetic Computing | 2021年 / 13卷
关键词
Uniform search; Grid search; Dynamic; Multiobjective optimization; Evolutionary algorithm;
D O I
暂无
中图分类号
学科分类号
摘要
When solving constrained multiobjective optimization problems (CMOPs), it is important to uniformly explore the promising regions that are not dominated by feasible solutions, and this can effectively avoid the loss of the Pareto front fragments. To achieve this, we propose a grid-based uniform search (GUS) to guide the current population to search the promising areas uniformly in this paper. Therein, the promising areas are divided into a number of grids, which are then fully explored by the individuals located in them. In the process of reducing the population size, the individuals with the largest constraint violations in the most crowded grids are removed one by one. To balance the local search and the global search, we dynamically reduce the number of divided grids in GUS with the increase of evolutionary iterations. Embedding the dynamic GUS in evolutionary algorithm, we design a new constrained algorithms for CMOPs. Experimental results show that the proposed algorithm performs better than other state-of-the-art constrained evolutionary multiobjective optimization algorithms in dealing with different CMOPs.
引用
收藏
页码:497 / 508
页数:11
相关论文
共 50 条
  • [1] Dynamic grid-based uniform search for solving constrained multiobjective optimization problems
    Yuan, Jiawei
    [J]. MEMETIC COMPUTING, 2021, 13 (04) : 497 - 508
  • [2] Dynamic resizing for grid-based archiving in evolutionary multiobjective optimization
    Rachmawati, L.
    Srinivasan, D.
    [J]. 2007 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-10, PROCEEDINGS, 2007, : 3975 - 3982
  • [3] Grid-based methods for linearly equality constrained optimization problems
    Feng Y.
    Zhang X.
    Liu L.
    [J]. Journal of Applied Mathematics and Computing, 2007, 23 (1-2) : 269 - 279
  • [4] Multiobjective evolutionary algorithms for solving constrained optimization problems
    Sarker, Ruhul
    Ray, Tapabrata
    [J]. INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE FOR MODELLING, CONTROL & AUTOMATION JOINTLY WITH INTERNATIONAL CONFERENCE ON INTELLIGENT AGENTS, WEB TECHNOLOGIES & INTERNET COMMERCE, VOL 2, PROCEEDINGS, 2006, : 197 - +
  • [5] A Dynamic Quality-Based Harmony Search Algorithm for Solving Constrained Engineering Optimization Problems
    Kattan, Ali
    [J]. PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTER SCIENCE AND ENGINEERING (CSE 2013), 2013, 42 : 72 - 75
  • [6] Indicator-Based Evolutionary Algorithm for Solving Constrained Multiobjective Optimization Problems
    Yuan, Jiawei
    Liu, Hai-Lin
    Ong, Yew-Soon
    He, Zhaoshui
    [J]. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2022, 26 (02) : 379 - 391
  • [7] Multiobjective optimization with ∈-constrained method for solving real-parameter constrained optimization problems
    Ji, Jing-Yu
    Yu, Wei-Jie
    Gong, Yue-Jiao
    Zhang, Jun
    [J]. INFORMATION SCIENCES, 2018, 467 : 15 - 34
  • [8] Extension of Zoutendijk method for solving constrained multiobjective optimization problems
    Morovati, Vahid
    Pourkarimi, Latif
    [J]. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2019, 273 (01) : 44 - 57
  • [9] Multiobjective optimization algorithm for solving constrained single objective problems
    Reynoso-Meza, Gilberto
    Blasco, Xavier
    Sanchis, Javier
    Martinez, Miguel
    [J]. 2010 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2010,
  • [10] Dynamic Landscape Analysis for Constrained Multiobjective Optimization Problems
    Alsouly, Hanan
    Kirley, Michael
    Munoz, Mario Andres
    [J]. ADVANCES IN ARTIFICIAL INTELLIGENCE, AI 2023, PT I, 2024, 14471 : 429 - 441