Multiobjective Bilevel Optimization: A Survey of the State-of-the-Art

Optimization makes processes, systems, or products more efficient, reliable, and with better outcomes. A popular topic on optimization today is multiobjective bilevel optimization (MOBO). In MOBO, an upper level problem is constrained by the solution of a lower level one. The problem at each level c...

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Bibliographic Details
Published in:IEEE transactions on systems, man, and cybernetics. Systems Vol. 53; no. 9; pp. 1 - 0
Main Authors: Mejia-de-Dios, Jesus-Adolfo, Rodriguez-Molina, Alejandro, Mezura-Montes, Efren
Format: Journal Article
Language:English
Published: New York IEEE 01.09.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Bi-level optimization
Constraints
evolutionary algorithms
Heuristic methods
Linear programming
Metaheuristics
metaheuristics (MHs)
multiobjective optimization
Multiple objective analysis
Optimization
Surveys
Task analysis
Taxonomy
ISSN:2168-2216, 2168-2232
Online Access:Get full text
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Summary:Optimization makes processes, systems, or products more efficient, reliable, and with better outcomes. A popular topic on optimization today is multiobjective bilevel optimization (MOBO). In MOBO, an upper level problem is constrained by the solution of a lower level one. The problem at each level can include multiple conflicting objective functions and its own constraints. This survey aims to study the solution approaches proposed to solve MOBO problems, including exact methods and approximate techniques such as metaheuristics (MHs). This work explores classical literature to investigate why most classical methods, theories, and algorithms focus on linear and some convex MOBO problems to solve the optimistic MOBO. Moreover, we study and propose a taxonomy of MH-based frameworks for solving some MOBO instances, highlighting the pros and cons of five main approaches. Finally, a growing interest in MOBO has been detected in the optimization community. A significant number of possible applications and solution approaches establish an early research line to find solutions to these types of problems.
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ISSN:2168-2216
2168-2232
DOI:10.1109/TSMC.2023.3271125