6533b82cfe1ef96bd12901a0

RESEARCH PRODUCT

Data-Driven Interactive Multiobjective Optimization Using a Cluster-Based Surrogate in a Discrete Decision Space

Jose MalmbergKyle EyvindsonJussi HakanenVesa Ojalehto

subject

data-driven optimizationMathematical optimizationOptimization problemComputer scienceboreal forest managementComputer Science::Neural and Evolutionary Computationpäätöksenteko0211 other engineering and technologiesMathematicsofComputing_NUMERICALANALYSISdecision maker02 engineering and technologypreference informationSpace (commercial competition)Multi-objective optimizationComputingMethodologies_ARTIFICIALINTELLIGENCEData-drivenklusteritoptimointi0202 electrical engineering electronic engineering information engineeringCluster analysis021103 operations researchsurrogatesComputingMethodologies_PATTERNRECOGNITIONboreaalinen vyöhyke020201 artificial intelligence & image processingmetsänhoitoCluster basedclustering

description

In this paper, a clustering based surrogate is proposed to be used in offline data-driven multiobjective optimization to reduce the size of the optimization problem in the decision space. The surrogate is combined with an interactive multiobjective optimization approach and it is applied to forest management planning with promising results. peerReviewed

yearjournalcountryeditionlanguage
2019-01-01
http://urn.fi/URN:NBN:fi:jyu-201911214960