Calculating the Complete Pareto Front for a Special Class of Continuous Multi-Objective Optimization Problems

被引:0
|
作者
Hu, Xiao-Bing [1 ,2 ]
Wang, Ming [1 ]
Hu, Xiao-Bing [1 ,2 ]
Leeson, Mark S. [2 ]
机构
[1] Beijing Normal Univ, State Key Lab Earth Surface Proc & Resource Ecol, Beijing 100875, Peoples R China
[2] Univ Warwick, Sch Engn, Coventry CV4 7AL, W Midlands, England
关键词
Continuous Problem; Local Optima; Pareto Front; Multi-Objective Optimization; Evolutionary algorithm; NORMAL CONSTRAINT METHOD; GENETIC ALGORITHMS; OPTIMA;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Existing methods for multi-objective optimization usually provide only an approximation of a Pareto front, and there is little theoretical guarantee of finding the real Pareto front. This paper is concerned with the possibility of fully determining the true Pareto front for those continuous multi-objective optimization problems for which there are a finite number of local optima in terms of each single objective function and there is an effective method to find all such local optima. To this end, some generalized theoretical conditions are firstly given to guarantee a complete cover of the actual Pareto front for both discrete and continuous problems. Then based on such conditions, an effective search procedure inspired by the rising sea level phenomenon is proposed particularly for continuous problems of the concerned class. Even for general continuous problems to which not all local optima are available, the new method may still work well to approximate the true Pareto front. The good practicability of the proposed method is especially underpinned by multi-optima evolutionary algorithms. The advantages of the proposed method in terms of both solution quality and computational efficiency are illustrated by the simulation results.
引用
收藏
页码:290 / 297
页数:8
相关论文
共 50 条
  • [21] Two efficient algorithms for constructing almost even approximations of the Pareto front in multi-objective optimization problems
    Dolatnezhadsomarin, Azam
    Khorram, Esmaile
    ENGINEERING OPTIMIZATION, 2019, 51 (04) : 567 - 589
  • [22] Numerical algorithms for generating an almost even approximation of the Pareto front in nonlinear multi-objective optimization problems
    Dolatnezhadsomarin, Azam
    Khorram, Esmaile
    Yousefikhoshbakht, Majid
    APPLIED SOFT COMPUTING, 2024, 165
  • [23] Jacobian Matrix Singularity Based Pareto Front Identification for Multi-Objective Problems
    Brown, Brandon
    Singh, Tarunraj
    Rai, Rahul
    2013 AMERICAN CONTROL CONFERENCE (ACC), 2013, : 3979 - 3984
  • [24] A novel Pareto-based multi-objective vibration damping optimization algorithm to solve multi-objective optimization problems
    Hajipour, V.
    Mehdizadeh, E.
    Tavakkoli-Moghaddam, R.
    SCIENTIA IRANICA, 2014, 21 (06) : 2368 - 2378
  • [25] A novel Pareto-based multi-objective vibration damping optimization algorithm to solve multi-objective optimization problems
    Hajipour, V. (v.hajipour@basu.ac.ir), 1600, Sharif University of Technology (21):
  • [26] Multi-objective evolutionary optimization based on online perceiving Pareto front characteristics
    Feng, Wenqing
    Gong, Dunwei
    Yu, Zekuan
    INFORMATION SCIENCES, 2021, 581 (581) : 912 - 931
  • [27] Multi-objective optimization techniques to design the Pareto front of organic dielectric polymers
    Mannodi-Kanakkithodi, Arun
    Pilania, Ghanshyam
    Ramprasad, Rampi
    Lookman, Turab
    Gubernatis, James E.
    COMPUTATIONAL MATERIALS SCIENCE, 2016, 125 : 92 - 99
  • [28] Adaptive multi-objective particle swarm optimization based on virtual Pareto front
    Li, Yuxuan
    Zhang, Yu
    Hu, Wang
    INFORMATION SCIENCES, 2023, 625 : 206 - 236
  • [29] Generating Pareto front of multi-objective optimization using artificial immune algorithm
    Tan Guangxing
    Mao Zongyuan
    PROCEEDINGS OF THE 24TH CHINESE CONTROL CONFERENCE, VOLS 1 AND 2, 2005, : 1334 - 1338
  • [30] Using a Family of Curves to Approximate the Pareto Front of a Multi-Objective Optimization Problem
    Martinez, Saul Zapotecas
    Sosa Hernandez, Victor A.
    Aguirre, Hernan
    Tanaka, Kiyoshi
    Coello Coello, Carlos A.
    PARALLEL PROBLEM SOLVING FROM NATURE - PPSN XIII, 2014, 8672 : 682 - 691