Flower pollination–feedforward neural network for load flow forecasting in smart distribution grid

Shehu, Gaddafi Sani; Çetinkaya, Nurettin

doi:10.1007/s00521-018-3421-5

Cited by 12 publications

(4 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e suggested HOS-MLP is compared with eight wellknown and recent Oas, including ABC [12], PSO [16], BAT [28], GA [37], BBO [38], firefly algorithm (FF/FA) [39], monarch butterfly optimization (MBO) [40], and flower pollination algorithm (FPA) [41]. For all OAs, the population size was set to 70, and the maximum number of…”

Section: Resultsmentioning

confidence: 99%

Hybrid Hypercube Optimization Search Algorithm and Multilayer Perceptron Neural Network for Medical Data Classification

Tunay

Pashaei

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The hypercube optimization search (HOS) approach is a new efficient and robust metaheuristic algorithm that simulates the dove’s movement in quest of new food sites in nature, utilizing hypercubes to depict the search zones. In medical informatics, the classification of medical data is one of the most challenging tasks because of the uncertainty and nature of healthcare data. This paper proposes the use of the HOS algorithm for training multilayer perceptrons (MLP), one of the most extensively used neural networks (NNs), to enhance its efficacy as a decision support tool for medical data classification. The proposed HOS-MLP model is tested on four significant medical datasets: orthopedic patients, diabetes, coronary heart disease, and breast cancer, to assess HOS’s success in training MLP. For verification, the results are compared with eleven different classifiers and eight well-regarded MLP trainer metaheuristic algorithms: particle swarm optimization (PSO), biogeography-based optimizer (BBO), the firefly algorithm (FFA), artificial bee colony (ABC), genetic algorithm (GA), bat algorithm (BAT), monarch butterfly optimizer (MBO), and the flower pollination algorithm (FPA). The experimental results demonstrate that the MLP trained by HOS outperforms the other comparative models regarding mean square error (MSE), classification accuracy, and convergence rate. The findings also reveal that the HOS help the MLP to produce more accurate results than other classification algorithms for the prediction of diseases.

show abstract

Section: Resultsmentioning

confidence: 99%

Hybrid Hypercube Optimization Search Algorithm and Multilayer Perceptron Neural Network for Medical Data Classification

Tunay

Pashaei

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…e flower pollination algorithm is used to locate the process of intelligent logistics management. e relevant parameters are initialized, including flower population number n and conversion probability p [30,31]. e pollen position is updated by the following equation:…”

Section: Supplier Manufacturer Other Enterprisesmentioning

confidence: 99%

Intelligent Logistics System Design and Supply Chain Management under Edge Computing and Internet of Things

Wang

Chen

Dai

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

In order to carry out practical innovation of the intelligent logistics system and promote the practicality of the intelligent logistics system and supply chain management process, this study aims to optimize the design of the intelligent logistics system and supply chain management under edge computing (EC) and the Internet of ings (IoT). e ower pollination algorithm performs the positioning function in the intelligent logistics system and supply chain management. Based on the research on the design of the intelligent logistics system and supply chain management under the EC and IoT, this thesis analyzes the positioning of intelligent logistics systems and supply chain management through the ower pollination algorithm. e eXtreme Gradient Boosting (XGBoost) model is used to predict user information in the system of supply chain management information. Finally, the operation of intelligent logistics and supply chain management systems, the prediction model of supply chain management under XGBoost, and the change of supply chain management and material ow are analyzed. e results show that with the increase in the number of iterations, the optimized algorithm improves the comparison distance error by 53.57%, which has high accuracy and can meet the requirements of positioning and tracking of the intelligent logistics system and logistics status query in supply chain management. e waiting time of the intelligent logistics system is shorter than that of the supply chain management system, and the average waiting time of the system increases by 121.252 ms. e XGBoost model can well predict user information under supply chain management. After discussing the changes of the intelligent logistics system from 2018 to 2020, it is found that the operation e ciency of the supply management system is higher with the increase of the system operation days. e intelligent logistics system has a signi cant impact on the development of the logistics industry.is research gives a reference for establishing the intelligent logistics system and supply chain management system.

show abstract

“…By arranging neurons in layers, each neuron of FNN is only connected to neurons in the previous layer [23][24][25] and only receive the output of the previous layer and then pass the result to the next layer. There is no feedback between neurons in each layer.…”

Section: Fnnmentioning

confidence: 99%

Identification of Sequential Feature for Volcanic Ash Cloud Using FNN-LSTM Collaborative Computing

Liu

Sun

et al. 2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

View full text Add to dashboard Cite

Collaborative computing performs quickly and accurately the task via combining the multimedia, multi-methods, and multi-clients. Analyzing of traditional feedforward neural network (FNN), long short-term memory (LSTM) neural networks and remote sensing data, this paper proposes a new identification method of sequential feature based on FNN-LSTM collaborative calculation in the volcanic ash cloud monitoring. In this method, combining remote sensing data, the FNN network is used firstly to construct the identification model of volcanic ash cloud. Next, the LSTM network is used to identify the sequential feature of dynamic changes in volcanic ash cloud based on the text data of the volcanic ash report. And then the simulation and true volcanic ash cloud case is performed and analyzed. The experimental results show that: 1) the proposed method is high in training accuracy with 76.54% and testing accuracy with 77%, respectively, and has obvious advantages for small-scale data volumes; 2) the total accuracy and RMS of the simulation analysis reached 79.05% and 0.0149, respectively, it verified the feasibility and effectiveness in the prediction of spatiotemporal evolution; 3) the anti-noise property and the image segmentation effect is good, the obtained sequential feature of the volcanic ash cloud are closer to the actual diffusion. It can provide a reference for sequential feature extraction and dynamic monitoring of volcanic ash cloud in complex environments.

show abstract

Flower pollination–feedforward neural network for load flow forecasting in smart distribution grid

Cited by 12 publications

References 43 publications

Hybrid Hypercube Optimization Search Algorithm and Multilayer Perceptron Neural Network for Medical Data Classification

Hybrid Hypercube Optimization Search Algorithm and Multilayer Perceptron Neural Network for Medical Data Classification

Intelligent Logistics System Design and Supply Chain Management under Edge Computing and Internet of Things

Identification of Sequential Feature for Volcanic Ash Cloud Using FNN-LSTM Collaborative Computing

Contact Info

Product

Resources

About