UAV Swarm Trajectory Design for Wireless Networks Using Genetic Algorithm-Driven Repulsion Forces Article Swipe
Kaleem Arshid
,
Ali Krayani
,
Lucio Marcenaro
,
David Martín
,
Carlo S. Regazzoni
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3606121
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3606121
Uncrewed Aerial Vehicle (UAV) swarms are increasingly recognized for their versatility and affordability. These swarms have enhanced various applications, including agriculture, surveillance, delivery services, and monitoring. However, fully utilizing the capabilities of UAV swarms requires addressing challenges related to trajectory design, particularly the Multiple Traveling Salesman Problem (MTSP). It involves optimizing the paths of multiple UAVs while avoiding collisions, minimizing overlap and interference, and managing the overall size of the swarm. These challenges highlight the complexities involved in developing high-performance, organized UAV swarm operations. We propose a novel approach based on repulsion force in UAV swarm trajectory design to tackle these issues. Our method utilizes a Genetic Algorithm (GA) to generate a dynamic Repulsion Force (RF) that optimizes the distance between UAVs and the size of the swarm. This approach reduces interference and overlap while effectively navigating the limitations posed by the MTSP. Our proposed solution aims to design efficient trajectories that enhance the overall performance of UAV swarms. We compared our proposed method to existing algorithms, including the MTSPGA, Particle Swarm Optimization (PSO), 2-OPT, Ant Colony (AC) Optimization, and Simulated Annealing (SA), using simulations and evaluations. The results indicate that our proposed method effectively optimizes travel distances and times, reduces interference levels and overlapping, prevents collisions between UAVs, and enhances the size of the UAV swarm. Overall, our method outperforms current approaches, demonstrating its effectiveness for UAV-based applications.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2025.3606121
- OA Status
- gold
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- 1
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- 42
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All OpenAlex metadata
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https://openalex.org/W4413977920Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2025.3606121Digital Object Identifier
- Title
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UAV Swarm Trajectory Design for Wireless Networks Using Genetic Algorithm-Driven Repulsion ForcesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-01-01Full publication date if available
- Authors
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Kaleem Arshid, Ali Krayani, Lucio Marcenaro, David Martín, Carlo S. RegazzoniList of authors in order
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https://doi.org/10.1109/access.2025.3606121Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1109/access.2025.3606121Direct OA link when available
- Concepts
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Trajectory, Computer science, Wireless, Swarm behaviour, Genetic algorithm, Algorithm design, Wireless network, Algorithm, Artificial intelligence, Telecommunications, Machine learning, Physics, AstronomyTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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42Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W4406321907, https://openalex.org/W4412030377, https://openalex.org/W4410614057, https://openalex.org/W4401343280, https://openalex.org/W4316506832, https://openalex.org/W4403021775, https://openalex.org/W2963492582, https://openalex.org/W1926428750, https://openalex.org/W1999562671, https://openalex.org/W4384575254, https://openalex.org/W1992991445, https://openalex.org/W2108525724, https://openalex.org/W4391542568, https://openalex.org/W3141994949, https://openalex.org/W4410597120, https://openalex.org/W4394925993, https://openalex.org/W3139484821, https://openalex.org/W4405720301, https://openalex.org/W4390204116, https://openalex.org/W4411298973, https://openalex.org/W4224300633, https://openalex.org/W4385453419, https://openalex.org/W4320015793, https://openalex.org/W4399801678, https://openalex.org/W4408118535, https://openalex.org/W4360584287, https://openalex.org/W4321443982, https://openalex.org/W4383503568, https://openalex.org/W4387452700, https://openalex.org/W4385491026, https://openalex.org/W4392903398, https://openalex.org/W4378468938, https://openalex.org/W4361282957, https://openalex.org/W4310184852, https://openalex.org/W4319978025, https://openalex.org/W3217489544, https://openalex.org/W3214155505, https://openalex.org/W4385626840, https://openalex.org/W4324092074, https://openalex.org/W3033271745, https://openalex.org/W4225986950, https://openalex.org/W3134587582 |
| referenced_works_count | 42 |
| abstract_inverted_index.a | 86, 105, 111 |
| abstract_inverted_index.It | 48 |
| abstract_inverted_index.We | 84, 159 |
| abstract_inverted_index.by | 140 |
| abstract_inverted_index.in | 77, 93 |
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| abstract_inverted_index.on | 90 |
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| abstract_inverted_index.Ant | 175 |
| abstract_inverted_index.Our | 102, 143 |
| abstract_inverted_index.The | 187 |
| abstract_inverted_index.UAV | 32, 81, 94, 157, 215 |
| abstract_inverted_index.and | 11, 24, 61, 63, 122, 132, 179, 185, 198, 203, 209 |
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| abstract_inverted_index.for | 8, 226 |
| abstract_inverted_index.its | 224 |
| abstract_inverted_index.our | 161, 191, 218 |
| abstract_inverted_index.the | 29, 42, 51, 65, 69, 74, 118, 123, 126, 137, 141, 153, 168, 211, 214 |
| abstract_inverted_index.(AC) | 177 |
| abstract_inverted_index.(GA) | 108 |
| abstract_inverted_index.(RF) | 115 |
| abstract_inverted_index.This | 128 |
| abstract_inverted_index.UAVs | 55, 121 |
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| abstract_inverted_index.have | 15 |
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| abstract_inverted_index.that | 116, 151, 190 |
| abstract_inverted_index.(SA), | 182 |
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| abstract_inverted_index.Force | 114 |
| abstract_inverted_index.MTSP. | 142 |
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| abstract_inverted_index.These | 13, 71 |
| abstract_inverted_index.UAVs, | 208 |
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| abstract_inverted_index.their | 9 |
| abstract_inverted_index.these | 100 |
| abstract_inverted_index.using | 183 |
| abstract_inverted_index.while | 56, 134 |
| abstract_inverted_index.(PSO), | 173 |
| abstract_inverted_index.2-OPT, | 174 |
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| abstract_inverted_index.Colony | 176 |
| abstract_inverted_index.design | 97, 148 |
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| abstract_inverted_index.method | 103, 163, 193, 219 |
| abstract_inverted_index.swarm. | 70, 127, 216 |
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| abstract_inverted_index.tackle | 99 |
| abstract_inverted_index.times, | 199 |
| abstract_inverted_index.travel | 196 |
| abstract_inverted_index.(MTSP). | 47 |
| abstract_inverted_index.Genetic | 106 |
| abstract_inverted_index.MTSPGA, | 169 |
| abstract_inverted_index.Problem | 46 |
| abstract_inverted_index.Vehicle | 2 |
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| abstract_inverted_index.swarms. | 158 |
| abstract_inverted_index.various | 17 |
| abstract_inverted_index.However, | 26 |
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| abstract_inverted_index.Overall, | 217 |
| abstract_inverted_index.Particle | 170 |
| abstract_inverted_index.Salesman | 45 |
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| abstract_inverted_index.avoiding | 57 |
| abstract_inverted_index.compared | 160 |
| abstract_inverted_index.delivery | 22 |
| abstract_inverted_index.distance | 119 |
| abstract_inverted_index.enhanced | 16 |
| abstract_inverted_index.enhances | 210 |
| abstract_inverted_index.existing | 165 |
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| abstract_inverted_index.UAV-based | 227 |
| abstract_inverted_index.distances | 197 |
| abstract_inverted_index.efficient | 149 |
| abstract_inverted_index.highlight | 73 |
| abstract_inverted_index.including | 19, 167 |
| abstract_inverted_index.optimizes | 117, 195 |
| abstract_inverted_index.organized | 80 |
| abstract_inverted_index.repulsion | 91 |
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| abstract_inverted_index.utilizing | 28 |
| abstract_inverted_index.addressing | 35 |
| abstract_inverted_index.challenges | 36, 72 |
| abstract_inverted_index.collisions | 206 |
| abstract_inverted_index.developing | 78 |
| abstract_inverted_index.minimizing | 59 |
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| abstract_inverted_index.optimizing | 50 |
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| abstract_inverted_index.trajectory | 39, 96 |
| abstract_inverted_index.algorithms, | 166 |
| abstract_inverted_index.approaches, | 222 |
| abstract_inverted_index.collisions, | 58 |
| abstract_inverted_index.effectively | 135, 194 |
| abstract_inverted_index.limitations | 138 |
| abstract_inverted_index.monitoring. | 25 |
| abstract_inverted_index.operations. | 83 |
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| abstract_inverted_index.simulations | 184 |
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| abstract_inverted_index.Optimization | 172 |
| abstract_inverted_index.agriculture, | 20 |
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| abstract_inverted_index.particularly | 41 |
| abstract_inverted_index.trajectories | 150 |
| abstract_inverted_index.Optimization, | 178 |
| abstract_inverted_index.applications, | 18 |
| abstract_inverted_index.applications. | 228 |
| abstract_inverted_index.demonstrating | 223 |
| abstract_inverted_index.effectiveness | 225 |
| abstract_inverted_index.interference, | 62 |
| abstract_inverted_index.surveillance, | 21 |
| abstract_inverted_index.affordability. | 12 |
| abstract_inverted_index.high-performance, | 79 |
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