Opposition-based moth swarm algorithm

Oliva, Diego; Esquivel-Torres, Sara; Hinojosa, Salvador; Pérez‐Cisneros, Marco; Osuna-Enciso, Valentín; Ortega-Sánchez, Noé; Heidari, Ali Asghar

doi:10.1016/j.eswa.2021.115481

Cited by 42 publications

(10 citation statements)

References 63 publications

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“…In the opposite case, it tends to delay convergence. With these circumstances in mind, the OBL 39,40 mechanism helps to achieve better global optimization by generating the opposite solution. The exploration is intensied in this way, because it accelerates the evolution of the candidate solutions.…”

Section: Analytical Methods Papermentioning

confidence: 99%

Transformer fault diagnosis research based on LIF technology and IAO optimization of LightGBM

et al. 2023

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Section: Analytical Methods Papermentioning

confidence: 99%

Transformer fault diagnosis research based on LIF technology and IAO optimization of LightGBM

et al. 2023

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“…To enhance the predictive outcomes of the MBLSTM model, the MHA is employed. The nighttime performance of moths is simulated to MHA has established in [20]. During this technique, the exploration and exploitation balance examine a separation of candidate solutions developing the populations.…”

Section: Hyperparameter Tuning Using Mhamentioning

confidence: 99%

Intelligent Smart Grid Stability Predictive Model for Cyber-Physical Energy Systems

Dutta¹,

Faraj²,

Albagory³

et al. 2023

Computer Systems Science and Engineering

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A cyber physical energy system (CPES) involves a combination of processing, network, and physical processes. The smart grid plays a vital role in the CPES model where information technology (IT) can be related to the physical system. At the same time, the machine learning (ML) models find useful for the smart grids integrated into the CPES for effective decision making. Also, the smart grids using ML and deep learning (DL) models are anticipated to lessen the requirement of placing many power plants for electricity utilization. In this aspect, this study designs optimal multi-head attention based bidirectional long short term memory (OMHA-MBLSTM) technique for smart grid stability prediction in CPES. The proposed OMHA-MBLSTM technique involves three subprocesses such as pre-processing, prediction, and hyperparameter optimization. The OMHA-MBLSTM technique employs min-max normalization as a pre-processing step. Besides, the MBLSTM model is applied for the prediction of stability level of the smart grids in CPES. At the same time, the moth swarm algorithm (MHA) is utilized for optimally modifying the hyperparameters involved in the MBLSTM model. To ensure the enhanced outcomes of the OMHA-MBLSTM technique, a series of simulations were carried out and the results are inspected under several aspects. The experimental results pointed out the better outcomes of the OMHA-MBLSTM technique over the recent models.

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“…The critical area of interest is to provide remote monitoring and data collection. The sensors are made as small and energy efficient as possible; they do not have the computing power to preprocess any data, and so send all collected data to the fog subsystem [35][36][37]. The fog subsystem processes the data, and finally, it is stored in cloud repositories placed in the cloud subsystem.…”

Section: User Subsystemmentioning

confidence: 99%

A Fog-Cluster Based Load-Balancing Technique

et al. 2022

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The Internet of Things has recently been a popular topic of study for developing smart homes and smart cities. Most IoT applications are very sensitive to delays, and IoT sensors provide a constant stream of data. The cloud-based IoT services that were first employed suffer from increased latency and inefficient resource use. Fog computing is used to address these issues by moving cloud services closer to the edge in a small-scale, dispersed fashion. Fog computing is quickly gaining popularity as an effective paradigm for providing customers with real-time processing, platforms, and software services. Real-time applications may be supported at a reduced operating cost using an integrated fog-cloud environment that minimizes resources and reduces delays. Load balancing is a critical problem in fog computing because it ensures that the dynamic load is distributed evenly across all fog nodes, avoiding the situation where some nodes are overloaded while others are underloaded. Numerous algorithms have been proposed to accomplish this goal. In this paper, a framework was proposed that contains three subsystems named user subsystem, cloud subsystem, and fog subsystem. The goal of the proposed framework is to decrease bandwidth costs while providing load balancing at the same time. To optimize the use of all the resources in the fog sub-system, a Fog-Cluster-Based Load-Balancing approach along with a refresh period was proposed. The simulation results show that “Fog-Cluster-Based Load Balancing” decreases energy consumption, the number of Virtual Machines (VMs) migrations, and the number of shutdown hosts compared with existing algorithms for the proposed framework.

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Opposition-based moth swarm algorithm

Cited by 42 publications

References 63 publications

Transformer fault diagnosis research based on LIF technology and IAO optimization of LightGBM

Transformer fault diagnosis research based on LIF technology and IAO optimization of LightGBM

Intelligent Smart Grid Stability Predictive Model for Cyber-Physical Energy Systems

A Fog-Cluster Based Load-Balancing Technique

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