Pareto Front Estimation for Decision Making

被引：81

作者：

Giagkiozis, Ioannis ^{[1
]}

Fleming, Peter J. ^{[2
]}

机构：

[1] Univ Sheffield, Sch Math & Stat, Sheffield S3 7RH, S Yorkshire, England

[2] Univ Sheffield, Dept Automat Control & Syst Engn, Sheffield S1 3JD, S Yorkshire, England

来源：

EVOLUTIONARY COMPUTATION | 2014年 / 22卷 / 04期

基金：

英国工程与自然科学研究理事会;

关键词：

Pareto front estimation; evolutionary algorithms; RBFNN; multi-objective optimization; nonlinear estimation; MULTIOBJECTIVE EVOLUTIONARY ALGORITHMS; GENETIC ALGORITHM; NEURAL-NETWORKS; OPTIMIZATION; APPROXIMATION; PREDICTION; FINANCE; DESIGN;

D O I：

10.1162/EVCO_a_00128

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The set of available multi-objective optimisation algorithms continues to grow. This fact can be partially attributed to their widespread use and applicability. However, this increase also suggests several issues remain to be addressed satisfactorily. One such issue is the diversity and the number of solutions available to the decision maker (DM). Even for algorithms very well suited for a particular problem, it is difficult-mainly due to the computational cost-to use a population large enough to ensure the likelihood of obtaining a solution close to the DM's preferences. In this paper we present a novel methodology that produces additional Pareto optimal solutions from a Pareto optimal set obtained at the end run of any multi-objective optimisation algorithm for two-objective and three-objective problem instances.

引用

页码：651 / 678

页数：28

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