DESDEO: The Modular and Open Source Framework for Interactive Multiobjective Optimization

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 proble...

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Autores principales:	G. Misitano, B. S. Saini, B. Afsar, B. Shavazipour, K. Miettinen
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Data-driven multiobjective optimization evolutionary computation interactive methods multi-criteria decision making nonlinear optimization open source software Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/4127bdfe3fe94b0991b6657e22d63246
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spelling	oai:doaj.org-article:4127bdfe3fe94b0991b6657e22d632462021-11-18T00:07:39ZDESDEO: The Modular and Open Source Framework for Interactive Multiobjective Optimization2169-353610.1109/ACCESS.2021.3123825https://doaj.org/article/4127bdfe3fe94b0991b6657e22d632462021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9591595/https://doaj.org/toc/2169-3536Interactive 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.G. MisitanoB. S. SainiB. AfsarB. ShavazipourK. MiettinenIEEEarticleData-driven multiobjective optimizationevolutionary computationinteractive methodsmulti-criteria decision makingnonlinear optimizationopen source softwareElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 148277-148295 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Data-driven multiobjective optimization evolutionary computation interactive methods multi-criteria decision making nonlinear optimization open source software Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Data-driven multiobjective optimization evolutionary computation interactive methods multi-criteria decision making nonlinear optimization open source software Electrical engineering. Electronics. Nuclear engineering TK1-9971 G. Misitano B. S. Saini B. Afsar B. Shavazipour K. Miettinen DESDEO: The Modular and Open Source Framework for Interactive Multiobjective Optimization
description	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.
format	article
author	G. Misitano B. S. Saini B. Afsar B. Shavazipour K. Miettinen
author_facet	G. Misitano B. S. Saini B. Afsar B. Shavazipour K. Miettinen
author_sort	G. Misitano
title	DESDEO: The Modular and Open Source Framework for Interactive Multiobjective Optimization
title_short	DESDEO: The Modular and Open Source Framework for Interactive Multiobjective Optimization
title_full	DESDEO: The Modular and Open Source Framework for Interactive Multiobjective Optimization
title_fullStr	DESDEO: The Modular and Open Source Framework for Interactive Multiobjective Optimization
title_full_unstemmed	DESDEO: The Modular and Open Source Framework for Interactive Multiobjective Optimization
title_sort	desdeo: the modular and open source framework for interactive multiobjective optimization
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/4127bdfe3fe94b0991b6657e22d63246
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DESDEO: The Modular and Open Source Framework for Interactive Multiobjective Optimization

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