Aquila Optimizer: A novel meta-heuristic optimization algorithm
Aquila Optimizer: A novel meta-heuristic optimization algorithm
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DOI：
10.1016/j.cie.2021.107250
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发表时间：
2021-05-06
影响因子：
7.9
通讯作者：
Gandomi, Amir H.
中科院分区：
文献类型：
--
作者：
Abualigah, Laith;Yousri, Dalia;Gandomi, Amir H.
该文借鉴了阿奎拉在捕食过程中的自然行为，提出了一种新的基于种群的优化方法--阿奎拉优化器(AO)。因此，该算法的优化过程表现为四种方法：垂直俯仰高空选择搜索空间、短滑翔攻击等高飞行搜索发散空间搜索、低空飞行慢降攻击搜索收敛空间和俯冲行走抓取猎物。为了验证新优化器为不同优化问题找到最优解的能力，进行了一系列实验。例如，在第一个实验中，应用AO来求解众所周知的23个函数。第二个和第三个实验系列旨在评估AO的性能，以找到更复杂问题的解决方案，例如分别为30个CEC2017测试函数和10个CEC2019测试函数。最后，使用了一组七个真实世界的工程问题。实验结果表明，与已有的元启发式算法相比，该算法具有一定的优越性。
This paper proposes a novel population-based optimization method, called Aquila Optimizer (AO), which is inspired by the Aquila's behaviors in nature during the process of catching the prey. Hence, the optimization procedures of the proposed AO algorithm are represented in four methods; selecting the search space by high soar with the vertical stoop, exploring within a diverge search space by contour flight with short glide attack, exploiting within a converge search space by low flight with slow descent attack, and swooping by walk and grab prey. To validate the new optimizer's ability to find the optimal solution for different optimization problems, a set of experimental series is conducted. For example, during the first experiment, AO is applied to find the solution of well-known 23 functions. The second and third experimental series aims to evaluate the AO's performance to find solutions for more complex problems such as thirty CEC2017 test functions and ten CEC2019 test functions, respectively. Finally, a set of seven real-world engineering problems are used. From the experimental results of AO that compared with well-known meta-heuristic methods, the superiority of the developed AO algorithm is observed.