Data-driven interactive multiobjective optimization using cluster based surrogate in discrete decision space
Authors
Date
2018Copyright
This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited.
Tutkielma esittää klusteripohjaisen sijaismallin diskreetin päätöksentekoavaruuden dimension pienentämiseksi ja lineaaristen kokonaislukuoptimointitehtävien yksinkertaistamiseksi. Sijaismalli on suunnattu erityisesti datapohjaisten päätöksenteko-ongelmien interaktiiviseen ratkaisemiseen, sillä se yhdistää sijaismallin interaktiota
helpottavan vaikutuksen ja interaktiivisen NIMBUS menetelmän hyvän
suorituskyvyn sijaismallin tuloavaruudessa. Kehitettyä sijaismallia
ja metodia myös sovellettiin monitavoitteiseen metsätalousongelmaan
hyvin tuloksin. This thesis presents a cluster based surrogate model approach for reducing dimension of discrete decision space and so for simplifying integer linear optimization problems. The model is especially aimed for solving data-driven decision making problems interactively, as the surrogate makes interaction more seamless and the interactive NIMBUS method manages well within the product space of the surrogate. The developed cluster based surrogate and method were also applied for a Boreal Forest management problem with promising results.
Keywords
surrogate metamodel clustering cluster based synchronous NIMBUS multiobjective decision making interactive optimization sijaismalli metamalli surrogaatti klusterointi klusteripohjainen synkroninen NIMBUS monitavoitteinen päätöksenteko interaktiivinen päätöksenteko optimointi monitavoiteoptimointi matemaattinen optimointi multi-objective optimisation optimisation mathematical optimisation
Metadata
Show full item recordCollections
- Pro gradu -tutkielmat [28096]
Related items
Showing items with similar title or keywords.
-
Data-Driven Interactive Multiobjective Optimization Using a Cluster-Based Surrogate in a Discrete Decision Space
Hakanen, Jussi; Malmberg, Jose; Ojalehto, Vesa; Eyvindson, Kyle (Springer, 2019)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 data-driven multiobjective optimization of metallurgical properties of microalloyed steels using the DESDEO framework
Saini, Bhupinder Singh; Chakrabarti, Debalay; Chakraborti, Nirupam; Shavazipour, Babooshka; Miettinen, Kaisa (Elsevier BV, 2023)Solving real-life data-driven multiobjective optimization problems involves many complicated challenges. These challenges include preprocessing the data, modelling the objective functions, getting a meaningful formulation ... -
Treed Gaussian Process Regression for Solving Offline Data-Driven Continuous Multiobjective Optimization Problems
Mazumdar, Atanu; López-Ibáñez, Manuel; Chugh, Tinkle; Hakanen, Jussi; Miettinen, Kaisa (MIT Press, 2023)For offline data-driven multiobjective optimization problems (MOPs), no new data is available during the optimization process. Approximation models (or surrogates) are first built using the provided offline data and an ... -
Probabilistic Selection Approaches in Decomposition-based Evolutionary Algorithms for Offline Data-Driven Multiobjective Optimization
Mazumdar, Atanu; Chugh, Tinkle; Hakanen, Jussi; Miettinen, Kaisa (IEEE, 2022)In offline data-driven multiobjective optimization, no new data is available during the optimization process. Approximation models, also known as surrogates, are built using the provided offline data. A multiobjective ... -
An Interactive Framework for Offline Data-Driven Multiobjective Optimization
Mazumdar, Atanu; Chugh, Tinkle; Hakanen, Jussi; Miettinen, Kaisa (Springer, 2020)We propose a framework for solving offline data-driven multiobjective optimization problems in an interactive manner. No new data becomes available when solving offline problems. We fit surrogate models to the data to ...