Paper Info
Title
A preliminary study on handling uncertainty in indicator-based multiobjective optimization
Abstract
Real-world optimization problems are often subject to uncertainties, which can arise regarding stochastic model parameters, objective functions and decision variables. These uncertainties can take different forms in terms of distribution, bound and central tendency. In the multiobjective context, several studies have been proposed to take uncertainty into account, and most of them propose an extension of Pareto dominance to the stochastic case. In this paper, we pursue a slightly different approach where the optimization goal is defined in terms of a quality indicator, i.e., an objective function on the set of Pareto set approximations. We consider the scenario that each solution is inherently associated with a probability distribution over the objective space, without assuming a ’true’ objective vector per solution. We propose different algorithms which optimize the quality indicator, and preliminary simulation results indicate advantages over existing methods such as averaging, especially with many objective functions.
Year
DOI
Venue
2006
10.1007/11732242_71
EvoWorkshops
Keywords
Field
DocType
different algorithm,objective vector,quality indicator,objective space,real-world optimization problem,different approach,objective function,optimization goal,different form,indicator-based multiobjective optimization,pareto dominance,preliminary study,optimization problem,probability distribution,multiobjective optimization,stochastic model
Mathematical optimization,Evolutionary algorithm,Vector optimization,Computer science,Stochastic dominance,Multi-objective optimization,Probability distribution,Stochastic modelling,Optimization problem,Pareto principle
Conference
Volume
ISSN
ISBN
3907
0302-9743
3-540-33237-5
Citations 
PageRank 
References 
17
0.66
10
Authors
2
Name
Order
Citations
PageRank
Matthieu Basseur127019.32
Eckart Zitzler24678291.01