Multi-Agent Variational Approach for Robotics: A Bio-Inspired Perspective
This study proposes an adaptable, bio-inspired optimization algorithm for Multi-Agent Space Exploration. The recommended approach combines a parameterized Aquila Optimizer, a bio-inspired technology, with deterministic Multi-Agent Exploration. Stochastic factors are integrated into the Aquila Optimi...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , , , , |
| منشور في: |
2023
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://depot.sorbonne.ae/handle/20.500.12458/1421 |
| الوسوم: |
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| _version_ | 1857415064869404672 |
|---|---|
| author | Mir, Imran |
| author2 | Gul, Faiza Mir, Suleman Abualigah, Laith Abu Zitar, Raed Hussien, Abdelazim G. Awwad, Emad M. Sharaf, Mohamed |
| author2_role | author author author author author author author |
| author_facet | Mir, Imran Gul, Faiza Mir, Suleman Abualigah, Laith Abu Zitar, Raed Hussien, Abdelazim G. Awwad, Emad M. Sharaf, Mohamed |
| author_role | author |
| dc.creator.none.fl_str_mv | Mir, Imran Gul, Faiza Mir, Suleman Abualigah, Laith Abu Zitar, Raed Hussien, Abdelazim G. Awwad, Emad M. Sharaf, Mohamed |
| dc.date.none.fl_str_mv | 2023-07-07T06:30:19Z 2023-07-07T06:30:19Z 2023 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | 10.3390/biomimetics8030294 https://depot.sorbonne.ae/handle/20.500.12458/1421 10.3390/biomimetics8030294 |
| dc.language.none.fl_str_mv | en |
| dc.relation.none.fl_str_mv | Biomimetics 2313-7673 |
| dc.subject.none.fl_str_mv | multi-agent numerical optimization space exploration meta-heuristic bio-inspired augmented framework Aquila Optimizer |
| dc.title.none.fl_str_mv | Multi-Agent Variational Approach for Robotics: A Bio-Inspired Perspective |
| dc.type.none.fl_str_mv | Controlled Vocabulary for Resource Type Genres::text::periodical::journal::contribution to journal::journal article |
| description | This study proposes an adaptable, bio-inspired optimization algorithm for Multi-Agent Space Exploration. The recommended approach combines a parameterized Aquila Optimizer, a bio-inspired technology, with deterministic Multi-Agent Exploration. Stochastic factors are integrated into the Aquila Optimizer to enhance the algorithm's efficiency. The architecture, called the Multi-Agent Exploration - Parameterized Aquila Optimizer (MAE-PAO), starts by using deterministic MAE to assess the cost and utility values of nearby cells encircling the agents. A parameterized Aquila Optimizer is then used to further increase the exploration pace. The effectiveness of the proposed MAE-PAO methodology is verified through extended simulations in various environmental conditions. The algorithm viability is further evaluated by comparing the results with those of the contemporary CME-Aquila Optimizer (CME-AO) and the Whale Optimizer. The comparison adequately considers various performance parameters, such as the percentage of the map explored, the number of unsuccessful runs, and the time needed to explore the map. The comparisons are performed on numerous maps simulating different scenarios. A detailed statistical analysis is performed to check the efficacy of the algorithm. We conclude that the proposed algorithm's average rate of exploration does not deviate much compared to contemporary algorithms. The same idea is checked for exploration time. Thus, we conclude that the results obtained for the proposed MAE-PAO algorithm provide significant advantages in terms of enhanced map exploration with lower execution times and nearly no failed runs. |
| id | sorbonner_070233847426fb0a8a613d1bb49c261e |
| identifier_str_mv | 10.3390/biomimetics8030294 |
| language_invalid_str_mv | en |
| network_acronym_str | sorbonner |
| network_name_str | Sorbonne University Abu Dhabi repository |
| oai_identifier_str | oai:depot.sorbonne.ae:20.500.12458/1421 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Multi-Agent Variational Approach for Robotics: A Bio-Inspired PerspectiveMir, ImranGul, FaizaMir, SulemanAbualigah, LaithAbu Zitar, RaedHussien, Abdelazim G.Awwad, Emad M.Sharaf, Mohamedmulti-agentnumerical optimizationspace explorationmeta-heuristicbio-inspiredaugmented frameworkAquila OptimizerThis study proposes an adaptable, bio-inspired optimization algorithm for Multi-Agent Space Exploration. The recommended approach combines a parameterized Aquila Optimizer, a bio-inspired technology, with deterministic Multi-Agent Exploration. Stochastic factors are integrated into the Aquila Optimizer to enhance the algorithm's efficiency. The architecture, called the Multi-Agent Exploration - Parameterized Aquila Optimizer (MAE-PAO), starts by using deterministic MAE to assess the cost and utility values of nearby cells encircling the agents. A parameterized Aquila Optimizer is then used to further increase the exploration pace. The effectiveness of the proposed MAE-PAO methodology is verified through extended simulations in various environmental conditions. The algorithm viability is further evaluated by comparing the results with those of the contemporary CME-Aquila Optimizer (CME-AO) and the Whale Optimizer. The comparison adequately considers various performance parameters, such as the percentage of the map explored, the number of unsuccessful runs, and the time needed to explore the map. The comparisons are performed on numerous maps simulating different scenarios. A detailed statistical analysis is performed to check the efficacy of the algorithm. We conclude that the proposed algorithm's average rate of exploration does not deviate much compared to contemporary algorithms. The same idea is checked for exploration time. Thus, we conclude that the results obtained for the proposed MAE-PAO algorithm provide significant advantages in terms of enhanced map exploration with lower execution times and nearly no failed runs.2023-07-07T06:30:19Z2023-07-07T06:30:19Z2023Controlled Vocabulary for Resource Type Genres::text::periodical::journal::contribution to journal::journal articleapplication/pdf10.3390/biomimetics8030294https://depot.sorbonne.ae/handle/20.500.12458/142110.3390/biomimetics8030294enBiomimetics2313-7673oai:depot.sorbonne.ae:20.500.12458/14212023-07-07T18:00:33Z |
| spellingShingle | Multi-Agent Variational Approach for Robotics: A Bio-Inspired Perspective Mir, Imran multi-agent numerical optimization space exploration meta-heuristic bio-inspired augmented framework Aquila Optimizer |
| title | Multi-Agent Variational Approach for Robotics: A Bio-Inspired Perspective |
| title_full | Multi-Agent Variational Approach for Robotics: A Bio-Inspired Perspective |
| title_fullStr | Multi-Agent Variational Approach for Robotics: A Bio-Inspired Perspective |
| title_full_unstemmed | Multi-Agent Variational Approach for Robotics: A Bio-Inspired Perspective |
| title_short | Multi-Agent Variational Approach for Robotics: A Bio-Inspired Perspective |
| title_sort | Multi-Agent Variational Approach for Robotics: A Bio-Inspired Perspective |
| topic | multi-agent numerical optimization space exploration meta-heuristic bio-inspired augmented framework Aquila Optimizer |
| url | https://depot.sorbonne.ae/handle/20.500.12458/1421 |