A Mapping and State-of-the-Art Survey on Multi-Objective Optimization Methods for Multi-Agent Systems
Over the last decades, researchers have studied the Multi-Objective Optimization (MOO) problem for Multi-Agent Systems (MASs). However, most of them consider the problem formulation to be the sum of objective functions, and no work has reviewed problems with the formulation of the prioritized sum of...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10352164/ |
| _version_ | 1797376369592631296 |
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| author | Shokoufeh Naderi Maude J. Blondin |
| author_facet | Shokoufeh Naderi Maude J. Blondin |
| author_sort | Shokoufeh Naderi |
| collection | DOAJ |
| description | Over the last decades, researchers have studied the Multi-Objective Optimization (MOO) problem for Multi-Agent Systems (MASs). However, most of them consider the problem formulation to be the sum of objective functions, and no work has reviewed problems with the formulation of the prioritized sum of objective functions to facilitate the study of the subject and identify the needs arising from it. In the context of Multi-Robot Systems (MRSs), most studies only focus on the mathematical development of their proposed MOO algorithm without paying attention to the real application. There is no comprehensive review to identify the reliable algorithms already applied to real platforms. Using a mapping and state-of-the-art review, this paper aims to fill these gaps by first offering a detailed overview of the discrete-time MOO methods for MASs. More specifically, we classify existing MOO methods based on the formulation of the problem into the sum of objective functions and the prioritized sum of objective functions. Secondly, we review the applications of these methods in MRSs and the practical implementation of MOO algorithms on real MRSs. Finally, we suggest future research directions to extend the existing methods to more realistic approaches, including open problems in the new research area of the prioritized sum of objective functions and practical challenges for implementing the existing methods in robotics. This work introduces the field of MAS to researchers and enables them to position themselves in the current research trends. |
| first_indexed | 2024-03-08T19:37:33Z |
| format | Article |
| id | doaj.art-a9c432b7168e44d98508acfeab4712f7 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T19:37:33Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a9c432b7168e44d98508acfeab4712f72023-12-26T00:02:51ZengIEEEIEEE Access2169-35362023-01-011113972813974410.1109/ACCESS.2023.334129510352164A Mapping and State-of-the-Art Survey on Multi-Objective Optimization Methods for Multi-Agent SystemsShokoufeh Naderi0https://orcid.org/0000-0002-7785-4552Maude J. Blondin1https://orcid.org/0000-0001-7844-8874Département Génie Électrique and Génie Informatique, Université de Sherbrooke, Sherbrooke, CanadaDépartement Génie Électrique and Génie Informatique, Université de Sherbrooke, Sherbrooke, CanadaOver the last decades, researchers have studied the Multi-Objective Optimization (MOO) problem for Multi-Agent Systems (MASs). However, most of them consider the problem formulation to be the sum of objective functions, and no work has reviewed problems with the formulation of the prioritized sum of objective functions to facilitate the study of the subject and identify the needs arising from it. In the context of Multi-Robot Systems (MRSs), most studies only focus on the mathematical development of their proposed MOO algorithm without paying attention to the real application. There is no comprehensive review to identify the reliable algorithms already applied to real platforms. Using a mapping and state-of-the-art review, this paper aims to fill these gaps by first offering a detailed overview of the discrete-time MOO methods for MASs. More specifically, we classify existing MOO methods based on the formulation of the problem into the sum of objective functions and the prioritized sum of objective functions. Secondly, we review the applications of these methods in MRSs and the practical implementation of MOO algorithms on real MRSs. Finally, we suggest future research directions to extend the existing methods to more realistic approaches, including open problems in the new research area of the prioritized sum of objective functions and practical challenges for implementing the existing methods in robotics. This work introduces the field of MAS to researchers and enables them to position themselves in the current research trends.https://ieeexplore.ieee.org/document/10352164/Multi-agent systemsmulti-objective optimizationmulti-robot systemsprioritized sum of objective functions |
| spellingShingle | Shokoufeh Naderi Maude J. Blondin A Mapping and State-of-the-Art Survey on Multi-Objective Optimization Methods for Multi-Agent Systems IEEE Access Multi-agent systems multi-objective optimization multi-robot systems prioritized sum of objective functions |
| title | A Mapping and State-of-the-Art Survey on Multi-Objective Optimization Methods for Multi-Agent Systems |
| title_full | A Mapping and State-of-the-Art Survey on Multi-Objective Optimization Methods for Multi-Agent Systems |
| title_fullStr | A Mapping and State-of-the-Art Survey on Multi-Objective Optimization Methods for Multi-Agent Systems |
| title_full_unstemmed | A Mapping and State-of-the-Art Survey on Multi-Objective Optimization Methods for Multi-Agent Systems |
| title_short | A Mapping and State-of-the-Art Survey on Multi-Objective Optimization Methods for Multi-Agent Systems |
| title_sort | mapping and state of the art survey on multi objective optimization methods for multi agent systems |
| topic | Multi-agent systems multi-objective optimization multi-robot systems prioritized sum of objective functions |
| url | https://ieeexplore.ieee.org/document/10352164/ |
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