DESDEO: The Modular and Open Source Framework for Interactive Multiobjective Optimization
Misitano, Giovanni, Saini, Bhupinder Singh, Afsar, Bekir, Shavazipour, Babooshka, Miettinen Kaisa. (2021). DESDEO: The Modular and Open Source Framework for Interactive Multiobjective Optimization. IEEE Access, 9, 148277-148295. https://doi.org/10.1109/ACCESS.2021.3123825
Published inIEEE Access
DisciplineLaskennallinen tiedeMultiobjective Optimization GroupComputational ScienceMultiobjective Optimization Group
© 2021 the Authors
Interactive multiobjective optimization methods incorporate preferences from a human decision maker in the optimization process iteratively. This allows the decision maker to focus on a subset of solutions, learn about the underlying trade-offs among the conflicting objective functions in the problem and adjust preferences during the solution process. Incorporating preference information allows computing only solutions that are interesting to the decision maker, decreasing computation time significantly. Thus, interactive methods have many strengths making them viable for various applications. However, there is a lack of existing software frameworks to apply and experiment with interactive methods. We fill a gap in the optimization software available and introduce DESDEO, a modular and open source Python framework for interactive multiobjective optimization. DESDEO’s modular structure enables implementing new interactive methods and reusing previously implemented ones and their functionalities. Both scalarization-based and evolutionary methods are supported, and DESDEO allows hybridizing interactive methods of both types in novel ways and enables even switching the method during the solution process. Moreover, DESDEO also supports defining multiobjective optimization problems of different kinds, such as data-driven or simulation-based problems. We discuss DESDEO’s modular structure in detail and demonstrate its capabilities in four carefully chosen use cases aimed at helping readers unfamiliar with DESDEO get started using it. We also give an example on how DESDEO can be extended with a graphical user interface. Overall, DESDEO offers a much-needed toolbox for researchers and practitioners to efficiently develop and apply interactive methods in new ways – both in academia and industry. ...
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
data-driven multiobjective optimization evolutionary computation interactive methods multi-criteria decision making nonlinear optimization open source software Pareto optimization lineaarinen optimointi optimointi päätöksenteko pareto-tehokkuus evoluutiolaskenta monitavoiteoptimointi avoin lähdekoodi
Publication in research information system
MetadataShow full item record
Related funder(s)Academy of Finland
Funding program(s)Academy Project, AoF
Additional information about fundingThis work was supported by the Academy of Finland under Grant 322221.
Showing items with similar title or keywords.
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 ...
Multiobjective shape design in a ventilation system with a preference-driven surrogate-assisted evolutionary algorithm Chugh, Tinkle; Kratky, Tomas; Miettinen, Kaisa; Jin, Yaochu; Makkonen, Pekka (ACM, 2019)We formulate and solve a real-world shape design optimization problem of an air intake ventilation system in a tractor cabin by using a preference-based surrogate-assisted evolutionary multiobjective optimization algorithm. ...
Heikkinen, Risto; Sipilä, Juha; Ojalehto, Vesa; Miettinen, Kaisa (Inderscience Publishers, 2022)We study data-driven decision support and formalise a path from data to decision making. We focus on lot sizing in inventory management with stochastic demand and propose an interactive multi-objective optimisation approach. ...
On Dealing with Uncertainties from Kriging Models in Offline Data-Driven Evolutionary Multiobjective Optimization Mazumdar, Atanu; Chugh, Tinkle; Miettinen, Kaisa; López-Ibáñez, Manuel (Springer International Publishing, 2019)Many works on surrogate-assisted evolutionary multiobjective optimization have been devoted to problems where function evaluations are time-consuming (e.g., based on simulations). In many real-life optimization problems, ...
Chugh, Tinkle (University of Jyväskylä, 2017)Multiobjective optimization problems (MOPs) with a large number of conﬂicting objectives are often encountered in industry. Moreover, these problem typically involve expensive evaluations (e.g. time consuming simulations ...