Mir Imran, Gul Faiza, Mir Suleman, Abualigah Laith, Zitar Raed Abu, Hussien Abdelazim G, Awwad Emad Mahrous, Sharaf Mohamed
School of Avionics and Electrical Engineering, College of Aeronautical Engineering, NUST, Risalpur 23200, Pakistan.
Department of Electrical Engineering, Air University, Aerospace and Aviation Campus Kamra, Kamra 43600, Pakistan.
Biomimetics (Basel). 2023 Jul 7;8(3):294. doi: 10.3390/biomimetics8030294.
This study proposes an adaptable, bio-inspired optimization algorithm for Multi-Agent Space Exploration. The recommended approach combines a parameterized Aquila Optimizer, a bio-inspired technology, with deterministic Multi-Agent Exploration. Stochastic factors are integrated into the Aquila Optimizer to enhance the algorithm's efficiency. The architecture, called the Multi-Agent Exploration-Parameterized Aquila Optimizer (MAE-PAO), starts by using deterministic MAE to assess the cost and utility values of nearby cells encircling the agents. A parameterized Aquila Optimizer is then used to further increase the exploration pace. The effectiveness of the proposed MAE-PAO methodology is verified through extended simulations in various environmental conditions. The algorithm viability is further evaluated by comparing the results with those of the contemporary CME-Aquila Optimizer (CME-AO) and the Whale Optimizer. The comparison adequately considers various performance parameters, such as the percentage of the map explored, the number of unsuccessful runs, and the time needed to explore the map. The comparisons are performed on numerous maps simulating different scenarios. A detailed statistical analysis is performed to check the efficacy of the algorithm. We conclude that the proposed algorithm's average rate of exploration does not deviate much compared to contemporary algorithms. The same idea is checked for exploration time. Thus, we conclude that the results obtained for the proposed MAE-PAO algorithm provide significant advantages in terms of enhanced map exploration with lower execution times and nearly no failed runs.
本研究提出了一种适用于多智能体空间探索的、受生物启发的优化算法。推荐的方法将参数化的天鹰座优化器（一种受生物启发的技术）与确定性多智能体探索相结合。随机因素被集成到天鹰座优化器中以提高算法效率。这种架构称为多智能体探索 - 参数化天鹰座优化器（MAE - PAO），首先使用确定性多智能体探索来评估围绕智能体的附近单元格的成本和效用值。然后使用参数化天鹰座优化器进一步加快探索速度。通过在各种环境条件下进行扩展模拟，验证了所提出的MAE - PAO方法的有效性。通过将结果与当代的CME - 天鹰座优化器（CME - AO）和鲸鱼优化器的结果进行比较，进一步评估了算法的可行性。比较充分考虑了各种性能参数，例如地图探索的百分比、未成功运行的次数以及探索地图所需的时间。在模拟不同场景的众多地图上进行比较。进行了详细的统计分析以检查算法的有效性。我们得出结论，与当代算法相比，所提出算法的平均探索率偏差不大。对探索时间也进行了同样的分析。因此，我们得出结论，所提出的MAE - PAO算法所获得的结果在以更低的执行时间增强地图探索且几乎没有失败运行方面具有显著优势。
