Pareto front estimation for decision making.
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.
School of Mathematics and Statistics, University of Sheffield, Sheffield, S3 7RH, UK i.giagkiozis@sheffield.ac.uk.
Ioannis
I
Giagkiozis
Peter J
PJ
Fleming
D000465:Algorithms; D003658:Decision Making, Computer-Assisted; D008962:Models, Theoretical; D013223:Statistics as Topic / Q000379:methods*
Pareto front estimation; RBFNN; evolutionary algorithms; multi-objective optimization; nonlinear estimation