A Multi-Colony Social Learning Approach for the Self-Organization of a Swarm of UAVs
This research offers an improved method for the self-organization of a swarm of UAVs based on a social learning approach. To start, we use three different colonies and three best members i.e., unmanned aerial vehicles (UAVs) randomly placed in the colonies. This study uses max-min ant colony optimiz...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-04-01
|
| Series: | Drones |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-446X/6/5/104 |
| _version_ | 1827669446394642432 |
|---|---|
| author | Muhammad Shafiq Zain Anwar Ali Amber Israr Eman H. Alkhammash Myriam Hadjouni |
| author_facet | Muhammad Shafiq Zain Anwar Ali Amber Israr Eman H. Alkhammash Myriam Hadjouni |
| author_sort | Muhammad Shafiq |
| collection | DOAJ |
| description | This research offers an improved method for the self-organization of a swarm of UAVs based on a social learning approach. To start, we use three different colonies and three best members i.e., unmanned aerial vehicles (UAVs) randomly placed in the colonies. This study uses max-min ant colony optimization (MMACO) in conjunction with social learning mechanism to plan the optimized path for an individual colony. Hereinafter, the multi-agent system (MAS) chooses the most optimal UAV as the leader of each colony and the remaining UAVs as agents, which helps to organize the randomly positioned UAVs into three different formations. Afterward, the algorithm synchronizes and connects the three colonies into a swarm and controls it using dynamic leader selection. The major contribution of this study is to hybridize two different approaches to produce a more optimized, efficient, and effective strategy. The results verify that the proposed algorithm completes the given objectives. This study also compares the designed method with the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to prove that our method offers better convergence and reaches the target using a shorter route than NSGA-II. |
| first_indexed | 2024-03-10T03:01:13Z |
| format | Article |
| id | doaj.art-ea33763a431245a2933b2e9583075d04 |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2024-03-10T03:01:13Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj.art-ea33763a431245a2933b2e9583075d042023-11-23T10:44:00ZengMDPI AGDrones2504-446X2022-04-016510410.3390/drones6050104A Multi-Colony Social Learning Approach for the Self-Organization of a Swarm of UAVsMuhammad Shafiq0Zain Anwar Ali1Amber Israr2Eman H. Alkhammash3Myriam Hadjouni4Electronic Engineering Department, Sir Syed University of Engineering & Technology, Karachi 75300, PakistanElectronic Engineering Department, Sir Syed University of Engineering & Technology, Karachi 75300, PakistanElectronic Engineering Department, Sir Syed University of Engineering & Technology, Karachi 75300, PakistanDepartment of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi ArabiaDepartment of Computer Sciences, College of Computer and Information Science, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaThis research offers an improved method for the self-organization of a swarm of UAVs based on a social learning approach. To start, we use three different colonies and three best members i.e., unmanned aerial vehicles (UAVs) randomly placed in the colonies. This study uses max-min ant colony optimization (MMACO) in conjunction with social learning mechanism to plan the optimized path for an individual colony. Hereinafter, the multi-agent system (MAS) chooses the most optimal UAV as the leader of each colony and the remaining UAVs as agents, which helps to organize the randomly positioned UAVs into three different formations. Afterward, the algorithm synchronizes and connects the three colonies into a swarm and controls it using dynamic leader selection. The major contribution of this study is to hybridize two different approaches to produce a more optimized, efficient, and effective strategy. The results verify that the proposed algorithm completes the given objectives. This study also compares the designed method with the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to prove that our method offers better convergence and reaches the target using a shorter route than NSGA-II.https://www.mdpi.com/2504-446X/6/5/104social learningant colony optimizationmulti-agent system |
| spellingShingle | Muhammad Shafiq Zain Anwar Ali Amber Israr Eman H. Alkhammash Myriam Hadjouni A Multi-Colony Social Learning Approach for the Self-Organization of a Swarm of UAVs Drones social learning ant colony optimization multi-agent system |
| title | A Multi-Colony Social Learning Approach for the Self-Organization of a Swarm of UAVs |
| title_full | A Multi-Colony Social Learning Approach for the Self-Organization of a Swarm of UAVs |
| title_fullStr | A Multi-Colony Social Learning Approach for the Self-Organization of a Swarm of UAVs |
| title_full_unstemmed | A Multi-Colony Social Learning Approach for the Self-Organization of a Swarm of UAVs |
| title_short | A Multi-Colony Social Learning Approach for the Self-Organization of a Swarm of UAVs |
| title_sort | multi colony social learning approach for the self organization of a swarm of uavs |
| topic | social learning ant colony optimization multi-agent system |
| url | https://www.mdpi.com/2504-446X/6/5/104 |
| work_keys_str_mv | AT muhammadshafiq amulticolonysociallearningapproachfortheselforganizationofaswarmofuavs AT zainanwarali amulticolonysociallearningapproachfortheselforganizationofaswarmofuavs AT amberisrar amulticolonysociallearningapproachfortheselforganizationofaswarmofuavs AT emanhalkhammash amulticolonysociallearningapproachfortheselforganizationofaswarmofuavs AT myriamhadjouni amulticolonysociallearningapproachfortheselforganizationofaswarmofuavs AT muhammadshafiq multicolonysociallearningapproachfortheselforganizationofaswarmofuavs AT zainanwarali multicolonysociallearningapproachfortheselforganizationofaswarmofuavs AT amberisrar multicolonysociallearningapproachfortheselforganizationofaswarmofuavs AT emanhalkhammash multicolonysociallearningapproachfortheselforganizationofaswarmofuavs AT myriamhadjouni multicolonysociallearningapproachfortheselforganizationofaswarmofuavs |