In the wireless sensor networks energy consumption is broadest and widely explored area of research. The solution for energy optimization encompasses various techniques such as efficient routing protocols, data scheduling, clustering, hardware redesigning, supervised and unsupervised network learning algorithm, and so forth. Compared with all the methods that has been so far discussed, swarm intelligence (SI) is considered to be the optimal way to find solution for reducing energy consumption as it is simple and the network formation is understood by the natural mechanism present in nature. SI approaches include ant colony optimization (ACO), particle swarm optimization, glowworm swarm optimization (GSO), and so forth. In this article, the authors provide the solution for the energy conservation problem through efficient GSO methods combined ACO. The revised beaconing glowworm swarm optimization ant colony optimization algorithm will be applied on the sensor network divided into swarms based on glowworms, the ants are introduced in the network that would parse the network by visiting the swarm heads with the principle of ACO behind it. The algorithm is tested on MATLAB 2015a for performance comparison with the HM-ACOPSO method with depicts energy conservation and efficiency in data collection.

中文翻译：

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改进的信标萤火虫群优化蚁群优化算法以定位节点并优化无线传感器网络中节点消耗的能量

在无线传感器网络中，能量消耗是最广泛且被广泛探索的研究领域。能量优化的解决方案包括各种技术，如高效路由协议、数据调度、集群、硬件重新设计、监督和非监督网络学习算法等。与迄今为止讨论的所有方法相比，群体智能（SI）被认为是寻找减少能源消耗解决方案的最佳方法，因为它很简单，并且网络形成是由自然界中存在的自然机制所理解的。SI 方法包括蚁群优化 (ACO)、粒子群优化、萤火虫群优化 (GSO) 等。在本文中，作者通过高效的 GSO 方法结合 ACO 提供了能量守恒问题的解决方案。修改后的信标萤火虫群优化蚁群优化算法将应用于基于萤火虫划分的传感器网络，将蚂蚁引入网络，通过访问群首来解析网络，其背后的ACO原理。该算法在 MATLAB 2015a 上进行了测试，以与 HM-ACOPSO 方法进行性能比较，描述了数据收集的节能和效率。