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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| Published in: | IEEE transactions on systems, man, and cybernetics. Systems Vol. 53; no. 9; pp. 1 - 0 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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New York
IEEE
01.09.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 2168-2216, 2168-2232 |
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| Abstract | 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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| AbstractList | 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. |
| Author | Rodriguez-Molina, Alejandro Mezura-Montes, Efren Mejia-de-Dios, Jesus-Adolfo |
| Author_xml | – sequence: 1 givenname: Jesus-Adolfo orcidid: 0000-0002-0367-2967 surname: Mejia-de-Dios fullname: Mejia-de-Dios, Jesus-Adolfo organization: Centro de Investigación en Matemáticas Aplicadas, Universidad Autonoma de Coahuila, Saltillo, Mexico – sequence: 2 givenname: Alejandro orcidid: 0000-0002-6901-3833 surname: Rodriguez-Molina fullname: Rodriguez-Molina, Alejandro organization: Research and Postgraduate Division, Tecnológico Nacional de México/IT de Tlalnepantla, Mexico City, Mexico – sequence: 3 givenname: Efren orcidid: 0000-0002-1565-5267 surname: Mezura-Montes fullname: Mezura-Montes, Efren organization: Artificial Intelligence Research Institute, University of Veracruz, Xalapa, Mexico |
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| SubjectTerms | Algorithms Bi-level optimization Constraints evolutionary algorithms Heuristic methods Linear programming Metaheuristics metaheuristics (MHs) multiobjective optimization Multiple objective analysis Optimization Surveys Task analysis Taxonomy |
| Title | Multiobjective Bilevel Optimization: A Survey of the State-of-the-Art |
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