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Nonlinear-based Chaotic Harris Hawks Optimizer: Algorithm and Internet of Vehicles application
Applied Soft Computing ( IF 8.7 ) Pub Date : 2021-06-05 , DOI: 10.1016/j.asoc.2021.107574
Amin Abdollahi Dehkordi , Ali Safaa Sadiq , Seyedali Mirjalili , Kayhan Zrar Ghafoor

Harris Hawks Optimizer (HHO) is one of the many recent algorithms in the field of metaheuristics. The HHO algorithm mimics the cooperative behavior of Harris Hawks and their foraging behavior in nature called surprise pounce. HHO benefits from a small number of controlling parameters setting, simplicity of implementation, and a high level of exploration and exploitation. To alleviate the drawbacks of this algorithm, a modified version called Nonlinear based Chaotic Harris Hawks Optimization (NCHHO) is proposed in this paper. NCHHO uses chaotic and nonlinear control parameters to improve HHO’s optimization performance. The main goal of using the chaotic maps in the proposed method is to improve the exploratory behavior of HHO. In addition, this paper introduces a nonlinear control parameter to adjust HHO’s exploratory and exploitative behaviors. The proposed NCHHO algorithm shows an improved performance using a variety of chaotic maps that were implemented to identify the most effective one, and tested on several well-known benchmark functions. The paper also considers solving an Internet of Vehicles (IoV) optimization problem that showcases the applicability of NCHHO in solving large-scale, real-world problems. The results demonstrate that the NCHHO algorithm is very competitive, and often superior, compared to the other algorithms. In particular, NCHHO provides 92% better results in average to solve the uni-modal and multi-modal functions with problem dimension sizes of D = 30 and 50, whereas, with respect to the higher dimension problem, our proposed algorithm shows 100% consistent improvement with D = 100 and 1000 compared to other algorithms. In solving the IoV problem, the success rate was 62.5%, which is substantially better in comparison with the state-of-the-art algorithms. To this end, the proposed NCHHO algorithm in this paper demonstrates a promising method to be widely used by different applications, which brings benefits to industries and businesses in solving their optimization problems experienced daily , such as resource allocation, information retrieval, finding the optimal path for sending data over networks, path planning, and so many other applications.



中文翻译:

基于非线性的混沌 Harris Hawks 优化器:算法与车联网应用

Harris Hawks Optimizer (HHO) 是元启发式领域的众多最新算法之一。HHO 算法模仿了 Harris Hawks 的合作行为以及它们在自然界中的觅食行为,称为惊喜突袭。HHO 受益于少量的控制参数设置、实现的简单性以及高水平的探索和开发。为了减轻该算法的缺点,本文提出了一种称为基于非线性的混沌哈里斯霍克斯优化(NCHHO)的修改版本。NCHHO 使用混沌和非线性控制参数来提高 HHO 的优化性能。在所提出的方法中使用混沌图的主要目标是改进 HHO 的探索行为。此外,本文引入了一个非线性控制参数来调整 HHO 的探索性和剥削性行为。所提出的 NCHHO 算法使用各种混沌图显示了改进的性能,这些图被实现以识别最有效的一个,并在几个著名的基准函数上进行测试。该论文还考虑解决一个车联网 (IoV) 优化问题,该问题展示了 NCHHO 在解决大规模现实问题中的适用性。结果表明,与其他算法相比,NCHHO 算法非常有竞争力,而且通常更胜一筹。特别是,NCHHO 在解决问题维数为 D = 30 和 50 的单模态和多模态函数时平均提供了 92% 更好的结果,而对于更高维度的问题,我们提出的算法显示 100% 一致与其他算法相比,D = 100 和 1000 的改进。在解决车联网问题时,成功率为 62.5%,与最先进的算法相比要好得多。为此,本文提出的 NCHHO 算法展示了一种被不同应用广泛使用的有前途的方法,它为行业和企业解决日常遇到的优化问题带来了好处,例如资源分配、信息检索、寻找最佳路径用于通过网络发送数据、路径规划和许多其他应用程序。

更新日期:2021-06-05
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