Sharma Himanshu, Arora Krishan, Mahajan Raghav, Ansarullah Syed Immamul, Amin Farhan, AlSalman Hussain
School of Electronics and Electrical Engineering, Lovely Professional University, Jalandhar, India.
Department of Management studies, North Campus Delina, The University of Kashmir, Delina, 193103, India.
Sci Rep. 2024 Dec 28;14(1):30714. doi: 10.1038/s41598-024-79577-8.
The traditional optimization approaches suffer from certain problems like getting stuck in local optima, low speed, susceptibility to local optima, and searching unknown search spaces, thus requiring reliance on single-based solutions. Herein, an Improved Aquila Optimizer (IAO) is proposed, which is a unique meta-heuristic optimization method motivated by the hunting behavior of Aquila. An improved version of Aquila optimizer seeks to increase effectiveness and productivity. IAO emulates the hunting behaviors of Aquila, elucidating each step of the hunting process. The IAO algorithm contains innovative elements to boost its optimization capabilities. It combines a combination of low flight with a leisurely descent for exploitation, high-altitude vertical dives, contour flying with brief gliding attacks for exploration, and controlled swooping maneuvers for effective prey capture. To assess the effectiveness of IAO, Herein, numerous experiments were carried out. Firstly, IAO was compared using 23 classical optimization functions. The achieved results demonstrate that the proposed model outperforms various champion algorithms. Secondly, the proposed algorithm is applied to five real-world engineering problems. The achieved results prove effectiveness in diverse application domains. The key findings of the research work highlight IAO's resilience and adaptability in solving challenging optimization issues and its importance as a strong optimization tool for real-world engineering applications. Convergence curves compare the speed of proposed algorithms with selected algorithms for 1000 iterations. Time complexity analysis shows that the best time is 0.00015225 which is better as compared to other algorithms also Wilcoxon ranksum test is carried out to calculate the p-value is less than 0.05 rejecting the null hypothesis. The research emphasizes the potential of IAO as a tool for tackling real-world optimization challenges by explaining its efficacy and competitiveness compared to other optimization procedures via comprehensive testing and analysis.
传统的优化方法存在一些问题，如陷入局部最优、速度慢、易受局部最优影响以及搜索未知搜索空间，因此需要依赖基于单一的解决方案。在此，提出了一种改进的天鹰座优化器（IAO），它是一种受天鹰座狩猎行为启发的独特元启发式优化方法。改进版的天鹰座优化器旨在提高有效性和生产率。IAO模仿天鹰座的狩猎行为，阐明了狩猎过程的每一步。IAO算法包含创新元素以增强其优化能力。它结合了低空飞行与悠闲下降以进行开发，高空垂直俯冲，轮廓飞行与短暂滑翔攻击以进行探索，以及可控俯冲动作以有效捕获猎物。为了评估IAO的有效性，在此进行了大量实验。首先，使用23个经典优化函数对IAO进行了比较。所取得的结果表明，所提出的模型优于各种冠军算法。其次，将所提出的算法应用于五个实际工程问题。所取得的结果证明了其在不同应用领域的有效性。该研究工作的关键发现突出了IAO在解决具有挑战性的优化问题方面的弹性和适应性，以及它作为实际工程应用中强大优化工具的重要性。收敛曲线比较了所提出算法与选定算法在1000次迭代中的速度。时间复杂度分析表明，最佳时间为0.00015225，与其他算法相比更好，并且还进行了Wilcoxon秩和检验以计算p值小于0.05，拒绝原假设。该研究通过全面测试和分析，解释了IAO与其他优化程序相比的有效性和竞争力，强调了IAO作为解决实际优化挑战工具的潜力。
