Multiobjective evolutionary algorithms for solving constrained optimization problems

被引：0
|
作者：
Sarker, Ruhul [1 ]
Ray, Tapabrata [2 ]
机构：
[1] Univ New South Wales, Australian Def Force Acad, Sch Informat Technol & Elect Engn, Canberra, ACT 2600, Australia
[2] Univ New South Wales, Australian Def Force Acad, Sch Aerosp Civil & Mech Engn, Canberra, ACT 2600, Australia
来源：
INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE FOR MODELLING, CONTROL & AUTOMATION JOINTLY WITH INTERNATIONAL CONFERENCE ON INTELLIGENT AGENTS, WEB TECHNOLOGIES & INTERNET COMMERCE, VOL 2, PROCEEDINGS | 2006年
关键词：
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, we compare two multi-objective evolutionary algorithms by solving bi-objective linear and nonlinear constrained optimization problems. The problems considered are three instances of a realistic crop planning problem. The multiobjective algorithms compared are a well-known multi-objective evolutionary algorithm NSGAII and our own algorithm MCA. We discuss the solutions obtained and analyse the sensitivity of variables for multiobjective solutions. From our analysis, it can be concluded that there is still room for improvement in the performance of the evolutionary optimization algorithms for some of these optimization problems.
引用
收藏
页码：197 / +
页数：2
相关论文
共 50 条
  • [31] A runtime analysis of evolutionary algorithms for constrained optimization problems
    Zhou, Yuren
    He, Jun
    IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2007, 11 (05) : 608 - 619
  • [32] Multiobjective Imperialist Competitive Algorithm for Solving Nonlinear Constrained Optimization Problems
    Chun-an LIU
    Huamin JIA
    JournalofSystemsScienceandInformation, 2019, 7 (06) : 532 - 549
  • [33] SUBGRADIENT ALGORITHM FOR SOLVING CONSTRAINED MULTIOBJECTIVE OPTIMIZATION PROBLEMS IN HILBERT SPACES
    Wang, W. E. N. T. I. N. G.
    An, A. I. M. I. N.
    JOURNAL OF NONLINEAR AND CONVEX ANALYSIS, 2023, 24 (05) : 991 - 1003
  • [34] A Survey on Evolutionary Constrained Multiobjective Optimization
    Liang, Jing
    Ban, Xuanxuan
    Yu, Kunjie
    Qu, Boyang
    Qiao, Kangjia
    Yue, Caitong
    Chen, Ke
    Tan, Kay Chen
    IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2023, 27 (02) : 201 - 221
  • [35] An Artificial Evolutionary Approach for Solving the Nonlinear Constrained Optimization Problems
    Hsieh, Y. -C.
    You, P. -S.
    APPLIED SCIENCE AND PRECISION ENGINEERING INNOVATION, PTS 1 AND 2, 2014, 479-480 : 861 - +
  • [36] A hybrid membrane evolutionary algorithm for solving constrained optimization problems
    Xiao Jianhua
    Huang Yufang
    Cheng Zhen
    He Juanjuan
    Niu Yunyun
    OPTIK, 2014, 125 (02): : 897 - 902
  • [37] Solving multiobjective, multiconstraint knapsack problems using mathematical programming and evolutionary algorithms
    Florios, Kostas
    Mavrotas, George
    Diakoulaki, Danae
    EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2010, 203 (01) : 14 - 21
  • [38] Solving Multicommodity Capacitated Network Design Problems Using Multiobjective Evolutionary Algorithms
    Kleeman, Mark P.
    Seibert, Benjamin A.
    Lamont, Gary B.
    Hopkinson, Kenneth M.
    Graham, Scott R.
    IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2012, 16 (04) : 449 - 471
  • [39] A general framework of surrogate-assisted evolutionary algorithms for solving computationally expensive constrained optimization problems
    Yang, Zan
    Qiu, Haobo
    Gao, Liang
    Xu, Danyang
    Liu, Yuanhao
    INFORMATION SCIENCES, 2023, 619 : 491 - 508
  • [40] MOBBO: A Multiobjective Brown Bear Optimization Algorithm for Solving Constrained Structural Optimization Problems
    Mehta, Pranav
    Kumar, Sumit
    Tejani, Ghanshyam G.
    khishe, Mohammad
    JOURNAL OF OPTIMIZATION, 2024, 2024
12345