Abstract
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.
Highlights
Optimization in many real-life problems is typically characterized by several conflicting objectives to be considered simultaneously
The NAUTILUS family [35] contains interactive tradeoff-free methods. This means that the decision maker (DM) does not deal with Pareto optimal solutions but gradually approaches the Pareto front starting from an inferior solution
USE CASE 4: SWITCHING METHODS As interactive multiobjective optimization methods vary in the type of preference information they require from a DM and the type of information they provide to the DM, it is sometimes desirable to switch between iterations to a method that is better suited to the changing needs of the DM
Summary
Optimization in many real-life problems is typically characterized by several conflicting objectives to be considered simultaneously. DESDEO enables solving computationally expensive simulation-based and data-driven problems using surrogate models, including uncertainty considerations It contains implementations of several old and new interactive methods by various developers covering methods of both MCDM and EMO types. We mean the ability to use final or intermediate results of one method in another method, such as generating approximated Pareto optimal solutions utilizing an EMO method and using the solutions in an MCDM method or switching the method during the solution process, e.g., when the DM wants to change the type of preference information This opens up new opportunities for utilizing different features of various methods while the DM is not limited to using only one method or one type of preferences.
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