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<p>In recent years, drone base stations (DBSs) have been playing an essential role in providing communication services in challenging and dynamic environments, particularly when the ground infrastructure is not available. These mobile base stations serve as relays between end users and other DBSs that may fall outside the range of direct communication of the ground base station (GBS). In this paper, we present a novel multi-objective optimization approach using the Multi-Objective Grasshopper Optimization Algorithm (MOGOA) [<CitationRef CitationID="CR1">1</CitationRef>] to optimize DBS locations in drone networks to ensure effective network coverage and minimize overlap and response time. The aim of the proposed method is to maximize the coverage of the network, minimize the overlap between DBSs, and minimize the end-to-end (E2E) response time for the drones to the GBS by finding the most efficient route in a multi-hop communication. The proposed method aims to improve network efficiency and reduce infrastructure costs. The effectiveness of the proposed algorithm was evaluated through comparative analyses under various environmental conditions. The evaluation also includes investigating drone collision probability based on DBS overlap, providing valuable insights into network performance and safety.</p>

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An effective multi-objective grasshopper optimization algorithm for uav network deployment: balancing coverage, overlap, and end-to-end delay

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月17日

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  • Ihab Almaameri,
  • László Blázovics

摘要

In recent years, drone base stations (DBSs) have been playing an essential role in providing communication services in challenging and dynamic environments, particularly when the ground infrastructure is not available. These mobile base stations serve as relays between end users and other DBSs that may fall outside the range of direct communication of the ground base station (GBS). In this paper, we present a novel multi-objective optimization approach using the Multi-Objective Grasshopper Optimization Algorithm (MOGOA) [1] to optimize DBS locations in drone networks to ensure effective network coverage and minimize overlap and response time. The aim of the proposed method is to maximize the coverage of the network, minimize the overlap between DBSs, and minimize the end-to-end (E2E) response time for the drones to the GBS by finding the most efficient route in a multi-hop communication. The proposed method aims to improve network efficiency and reduce infrastructure costs. The effectiveness of the proposed algorithm was evaluated through comparative analyses under various environmental conditions. The evaluation also includes investigating drone collision probability based on DBS overlap, providing valuable insights into network performance and safety.