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
  • [41] Calculating Complete and Exact Pareto Front for Multiobjective Optimization: A New Deterministic Approach for Discrete Problems
    Hu, Xiao-Bing
    Wang, Ming
    Di Paolo, Ezequiel
    IEEE TRANSACTIONS ON CYBERNETICS, 2013, 43 (03) : 1088 - 1101
  • [42] Improving SVM for Learning Multi-Class Domains with Pareto Multi-Objective Optimization
    Zhang, Xiaolong
    Qiu, Zewei
    Zhang, Xiaofang
    PROGRESS IN INTELLIGENCE COMPUTATION AND APPLICATIONS, 2008, : 238 - 241
  • [43] An Improved Parallel Adaptive Genetic Algorithm Based on Pareto Front for Multi-Objective Problems
    Liu, Guangyuan
    Zhang, Jingjun
    Gao, Ruizhen
    Shang, Yanmin
    2009 SECOND INTERNATIONAL SYMPOSIUM ON KNOWLEDGE ACQUISITION AND MODELING: KAM 2009, VOL 2, 2009, : 212 - 215
  • [44] A Modified Pareto Strength Ant Colony Optimization Algorithm for the Multi-objective Optimization Problems
    Ariyasingha, I. D. I. D.
    Fernando, T. G. I.
    2016 IEEE INTERNATIONAL CONFERENCE ON INFORMATION AND AUTOMATION FOR SUSTAINABILITY (ICIAFS): INTEROPERABLE SUSTAINABLE SMART SYSTEMS FOR NEXT GENERATION, 2016,
  • [45] Analysis of the Pareto Front of a Multi-objective Optimization Problem for a Fossil Fuel Power Plant
    Van Sickel, Joel H.
    Venkatesh, Paramasivam
    Lee, Kwang Y.
    2008 IEEE POWER & ENERGY SOCIETY GENERAL MEETING, VOLS 1-11, 2008, : 1109 - 1116
  • [46] Optimization techniques for crisp and fuzzy multi-objective static inventory model with Pareto front
    Sahoo, Anuradha
    Panda, Minakshi
    OPSEARCH, 2024, 61 (04) : 2242 - 2284
  • [47] Pareto-MEC for multi-objective optimization
    Sun, CY
    Qi, XH
    Li, O
    2003 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS, VOLS 1-5, CONFERENCE PROCEEDINGS, 2003, : 321 - 328
  • [48] Rapid Multi-Objective Design Optimization of Miniaturized Impedance Transformer By Pareto Front Exploration
    Koziel, Slawomir
    Bekasiewicz, Adrian
    2016 IEEE/ACES INTERNATIONAL CONFERENCE ON WIRELESS INFORMATION TECHNOLOGY AND SYSTEMS (ICWITS) AND APPLIED COMPUTATIONAL ELECTROMAGNETICS (ACES), 2016,
  • [49] An Approach to Continuous Approximation of Pareto Front Using Geometric Support Vector Regression for Multi-objective Optimization of Fermentation Process
    Wu, Jiahuan
    Wang, Jianlin
    Yu, Tao
    Zhao, Liqiang
    CHINESE JOURNAL OF CHEMICAL ENGINEERING, 2014, 22 (10) : 1131 - 1140
  • [50] An Adaptive Consensus Based Method for Multi-objective Optimization with Uniform Pareto Front Approximation
    Borghi, Giacomo
    Herty, Michael
    Pareschi, Lorenzo
    APPLIED MATHEMATICS AND OPTIMIZATION, 2023, 88 (02):