Drone Behavior Inspired Optimization: A Multi-Agent Search Algorithm with Local Reconnaissance, Global Collaboration, and Environmental Perturbation
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| Format: | Recurso digital |
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2025
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| _version_ | 1866901563954429952 |
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| author | Zhang, Jincheng |
| author_facet | Zhang, Jincheng |
| contents | <p><span>This paper proposes a new optimization algorithm, the core idea of</span> <span>which is derived from the behavioral characteristics of multiple UAVs flying and executing missions in complex environments. Unlike traditional particle swarm optimization, genetic algorithm, or ant colony algorithm, this algorithm fully simulates the local reconnaissance, global collaboration, airflow disturbance, and formation intelligence mechanisms of UAVs, mapping them into the optimization search process, thereby achieving efficient search for the global optimal solution. The algorithm design introduces a multi-level dynamic model and environmental perception mechanism, and proposes a complete set of mathematical formulas, including velocity update, position update, local reconnaissance attraction, global collaboration attraction, airflow disturbance, and formation collaboration mechanism. This paper elaborates on the algorithm's theoretical model, mathematical formula derivation, and multi-strategy adaptive control strategy, providing a systematic theoretical basis for the research of UAV heuristic optimization algorithms.</span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17493690 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Drone Behavior Inspired Optimization: A Multi-Agent Search Algorithm with Local Reconnaissance, Global Collaboration, and Environmental Perturbation Zhang, Jincheng <p><span>This paper proposes a new optimization algorithm, the core idea of</span> <span>which is derived from the behavioral characteristics of multiple UAVs flying and executing missions in complex environments. Unlike traditional particle swarm optimization, genetic algorithm, or ant colony algorithm, this algorithm fully simulates the local reconnaissance, global collaboration, airflow disturbance, and formation intelligence mechanisms of UAVs, mapping them into the optimization search process, thereby achieving efficient search for the global optimal solution. The algorithm design introduces a multi-level dynamic model and environmental perception mechanism, and proposes a complete set of mathematical formulas, including velocity update, position update, local reconnaissance attraction, global collaboration attraction, airflow disturbance, and formation collaboration mechanism. This paper elaborates on the algorithm's theoretical model, mathematical formula derivation, and multi-strategy adaptive control strategy, providing a systematic theoretical basis for the research of UAV heuristic optimization algorithms.</span></p> |
| title | Drone Behavior Inspired Optimization: A Multi-Agent Search Algorithm with Local Reconnaissance, Global Collaboration, and Environmental Perturbation |
| url | https://doi.org/10.5281/zenodo.17493690 |