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Studying the effect of multisource Darwinian particle swarm optimization in search and rescue missions
Robotic Swarm Intelligence is considered one of the hottest topics within the robotics research eld nowadays, for its major contributions to di erent elds of life from hobbyists, makers and expanding to military applications. It has also proven to be more effective and effcient than other...
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2018
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| _version_ | 1867613411830923264 |
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| access_status_str | Open Access |
| author | M. Youssef, Ahmed |
| author_browse | M. Youssef, Ahmed |
| author_facet | M. Youssef, Ahmed |
| author_sort | M. Youssef, Ahmed |
| collection | Thesis |
| dc_rights_str_mv | The author retains all rights with regard to copyright. The author certifies that written permission from the owner(s) of third-party copyrighted matter included in the thesis, dissertation, paper, or record of study has been obtained. The author further certifies that IRB approval has been obtained for this thesis, or that IRB approval is not necessary for this thesis. Insofar as this thesis, dissertation, paper, or record of study is an educational record as defined in the Family Educational Rights and Privacy Act (FERPA) (20 USC 1232g), the author has granted consent to disclosure of it to anyone who requests a copy. |
| description | Robotic Swarm Intelligence is considered one of the hottest topics within the robotics research eld nowadays, for its major contributions to di erent elds of life from hobbyists, makers and expanding to military applications. It has also proven to be more effective and effcient than other robotic approaches targeting the same problem. Within this research, we targeted to test the hypothesis that using more than a single starting/ seeding point for a swarm to explore an unknown environment will yield better solutions, routes and cover more area of the search space within context of Search and Rescue applications domain. We tested such hypothesis via extending existing Particle swarm optimization techniques for search and rescue operations (i.e. Robotic Darwinian Particle Swarm Optimization and we split the swarm into smaller groups that start exploration from di erent seed positions, then took the convergence time average for di erent runs of simulations and recorded the results for quanti cation. The results presented in this work con rms the hypothesis we started with, and gives insight to how the number of robots contributing in the experiments a ect the quality of the results. This work also shows a direct correlation between the swarm size and the search space. |
| format | Thesis |
| id | oai:fount.aucegypt.edu:etds-1708 |
| institution | American University in Cairo (Egypt) |
| last_indexed | 2026-06-10T12:35:43.583Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from AUC Knowledge Fountain — bepress |
| publishDate | 2018 |
| publishDateRange | 2018 |
| publishDateSort | 2018 |
| publisher | AUC Knowledge Fountain |
| publisherStr | AUC Knowledge Fountain |
| record_format | dspace |
| source_str | AUC Knowledge Fountain — bepress |
| spelling | oai:fount.aucegypt.edu:etds-1708 Studying the effect of multisource Darwinian particle swarm optimization in search and rescue missions M. Youssef, Ahmed Robotic Swarm Intelligence is considered one of the hottest topics within the robotics research eld nowadays, for its major contributions to di erent elds of life from hobbyists, makers and expanding to military applications. It has also proven to be more effective and effcient than other robotic approaches targeting the same problem. Within this research, we targeted to test the hypothesis that using more than a single starting/ seeding point for a swarm to explore an unknown environment will yield better solutions, routes and cover more area of the search space within context of Search and Rescue applications domain. We tested such hypothesis via extending existing Particle swarm optimization techniques for search and rescue operations (i.e. Robotic Darwinian Particle Swarm Optimization and we split the swarm into smaller groups that start exploration from di erent seed positions, then took the convergence time average for di erent runs of simulations and recorded the results for quanti cation. The results presented in this work con rms the hypothesis we started with, and gives insight to how the number of robots contributing in the experiments a ect the quality of the results. This work also shows a direct correlation between the swarm size and the search space. 2018-02-01T08:00:00Z thesis application/pdf https://fount.aucegypt.edu/etds/709 https://fount.aucegypt.edu/context/etds/article/1708/viewcontent/studying_effect_multi_final.pdf The author retains all rights with regard to copyright. The author certifies that written permission from the owner(s) of third-party copyrighted matter included in the thesis, dissertation, paper, or record of study has been obtained. The author further certifies that IRB approval has been obtained for this thesis, or that IRB approval is not necessary for this thesis. Insofar as this thesis, dissertation, paper, or record of study is an educational record as defined in the Family Educational Rights and Privacy Act (FERPA) (20 USC 1232g), the author has granted consent to disclosure of it to anyone who requests a copy. Theses and Dissertations AUC Knowledge Fountain Swarm Intelligence Darwinian Particle Swarm Optimization |
| spellingShingle | Swarm Intelligence Darwinian Particle Swarm Optimization M. Youssef, Ahmed Studying the effect of multisource Darwinian particle swarm optimization in search and rescue missions |
| title | Studying the effect of multisource Darwinian particle swarm optimization in search and rescue missions |
| title_full | Studying the effect of multisource Darwinian particle swarm optimization in search and rescue missions |
| title_fullStr | Studying the effect of multisource Darwinian particle swarm optimization in search and rescue missions |
| title_full_unstemmed | Studying the effect of multisource Darwinian particle swarm optimization in search and rescue missions |
| title_short | Studying the effect of multisource Darwinian particle swarm optimization in search and rescue missions |
| title_sort | studying the effect of multisource darwinian particle swarm optimization in search and rescue missions |
| topic | Swarm Intelligence Darwinian Particle Swarm Optimization |
| url | https://fount.aucegypt.edu/etds/709 https://fount.aucegypt.edu/context/etds/article/1708/viewcontent/studying_effect_multi_final.pdf |
| work_keys_str_mv | AT myoussefahmed studyingtheeffectofmultisourcedarwinianparticleswarmoptimizationinsearchandrescuemissions |