6533b7d6fe1ef96bd1266e12
RESEARCH PRODUCT
Experiments with classification-based scalarizing functions in interactive multiobjective optimization
Katja KaarioKaisa MiettinenMarko M. Mäkeläsubject
Mathematical optimizationInformation Systems and ManagementGeneral Computer SciencePareto principleManagement Science and Operations ResearchMulti-objective optimizationMultiple objective programmingIndustrial and Manufacturing EngineeringSet (abstract data type)Nonlinear systemSingle objective optimization problemConflicting objectivesModeling and SimulationBenchmark (computing)Mathematicsdescription
In multiobjective optimization methods, the multiple conflicting objectives are typically converted into a single objective optimization problem with the help of scalarizing functions and such functions may be constructed in many ways. We compare both theoretically and numerically the performance of three classification-based scalarizing functions and pay attention to how well they obey the classification information. In particular, we devote special interest to the differences the scalarizing functions have in the computational cost of guaranteeing Pareto optimality. It turns out that scalarizing functions with or without so-called augmentation terms have significant differences in this respect. We also collect a set of mostly nonlinear benchmark test problems that we use in the numerical comparisons.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2006-12-01 | European Journal of Operational Research |