Department of Electronics and Communication Engineering, Punjabi University, Patiala, India.
Computer Science and Engineering, Sant Longowal Institute of Engineering and Technology, Patiala, India.
Sci Rep. 2022 Sep 2;12(1):14968. doi: 10.1038/s41598-022-18001-5.
Wireless sensors are the basic requisite of today's smart infrastructure based on internet of things (IoTs), 5G and wireless sensor networks (WSNs). WSNs are widely used in industrial applications, precision agriculture and animal tracking systems, environment monitoring, smart grids, energy control systems, smart buildings and entertainment industry etc. The distributed and dynamic scheme of WSNs establishes very unique demands in developing clustering and routing protocols. In order to meet the demand of efficient WSNs, most important requirement is energy management and extension of network lifetime. So energy constraints issue is one of the most emerging area for research to reduce the complexity of network functioning. Due to the complexity of this task we need more robustness optimizer algorithms which can tackle these types of tasks. In this article we are trying to develop one improved version of chimp optimizer for energy constraint issues. In this modification have been integrated the chimp optimizer with dimension learning based hunting (DLH) search technique, known as Improved Chimp Optimizer Algorithm (IChoA). Here the DLH search strategy helps in maintaining diversity and improves the balance between exploitation and exploration. To compute the robustness in solving the optimizer issues, IChoA has been tested on 29-CEC-2017 test suites and energy constraint issues. Experimental solutions obtained by proposed methods are verified with recent methods. All simulation shows that the IChoA method can be most effective in solving the standard complex suites and energy constraint issues.
无线传感器是基于物联网 (IoT)、5G 和无线传感器网络 (WSN) 的当今智能基础设施的基本要求。WSN 广泛应用于工业应用、精准农业和动物跟踪系统、环境监测、智能电网、能源控制系统、智能建筑和娱乐业等领域。WSN 的分布式和动态方案对开发聚类和路由协议提出了非常独特的要求。为了满足高效 WSN 的需求，最重要的要求是能源管理和延长网络寿命。因此，能量约束问题是研究的新兴领域之一，旨在降低网络功能的复杂性。由于这项任务的复杂性，我们需要更多的鲁棒性优化器算法来处理这些类型的任务。在本文中，我们尝试为能量约束问题开发一种改进的类人猿优化器。在这种改进中，我们将类人猿优化器与基于维度学习的狩猎 (DLH) 搜索技术集成在一起，称为改进的类人猿优化器算法 (IChoA)。在这里，DLH 搜索策略有助于保持多样性，并在开发和探索之间取得平衡。为了计算解决优化器问题的鲁棒性，IChoA 在 29-CEC-2017 测试套件和能量约束问题上进行了测试。所提出的方法获得的实验解决方案已与最近的方法进行了验证。所有模拟结果表明，IChoA 方法在解决标准复杂套件和能量约束问题方面最为有效。
