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A novel damping discrete grey model with time-polynomial term for forecasting monthly natural gas consumption

GREY SYSTEMS-THEORY AND APPLICATION 2025年第2期15卷 305-334页

作者： Zhao, Ying Zhang, Tao Xu, Jie Yang, Jie Wu, Wen-Ze Guangxi Univ Sci & Technol Liuzhou Peoples R China Cent China Normal Univ Wuhan Peoples R China Jiangsu Univ Zhenjiang Peoples R China

PurposeThis study aims to design a novel seasonal discrete grey model for forecasting monthly natural gas consumption by incorporating damping accumulation and time-polynomial ***/methodology/approachConsidering the principle of new information priority and nonlinear patterns in the original series of monthly natural gas consumption, we establish a novel discrete seasonal grey model by adding the damping accumulation and time-polynomial term into the existing model. In addition, the order of damping accumulation and the coefficient of time-power term can be determined by the moth flame optimization (MFO) *** empirical cases show that the proposed model has a better prediction performance when compared with other benchmark models, including six seasonal grey models, one statistical model and one artificial intelligent model. Based on forecasts, the proposed model can be considered a promising tool for monthly natural gas consumption (NGC) in ***/valueBy combining the damping accumulation and the time-polynomial term, a new discrete seasonal grey model for improving the prediction performance of the existing grey model is proposed. The properties of the proposed model are given, and the newly-designed model is initially applied to predict monthly NGC in US.

关键词： Discrete seasonal grey model Damping accumulation Time-polynomial term moth flame optimization algorithm

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An investigation of Grid-Integrated Photovoltaic system intended for hybrid moth flame optimization using ANFIS techniques

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第3期42卷 2505-2519页

作者： Rajagopal, Sureshkumar Umapathy, Prabha Kumaraguru Coll Technol Dept EEE Coimbatore Tamil Nadu India Dr NGP Inst Technol Dept Elect & Elect Engn Coimbatore Tamil Nadu India

As the move towards Grid Integrated-Photovoltaic (GI-PV) system is proposed to improve the power quality development. A novel Adaptive Neuro-Fuzzy Inference System (ANFIS) based on improved moth flame optimization (MFO) algorithm is described for grid integrated approach. The solar integration of Maximum Power Point (MPP) fed into modified Switched Boost Inverter (SBI) is presented, this GI-PV connected circuit has become prominent research in a recent scenario for energy demand. Proposed MFOA-ANFIS controller has generated the duty cycle pulses to each converter circuit. The benefit of grid-tied SBI is direct control outer-loop employed to obtain MFO-ANFIS techniques. To maintain a constant voltage DC-link is employed for inner-loop, this presence of constant DC-power to grid loads with support of MFO-ANFIS assists Proportional Integral Differential (PID) method. The results acquired by the simulation expressed that the proposed controller is addressed to maintain active and reactive power exchange, regulate DC bus-link voltages, grid voltage, and grid current. The effectiveness of the practical implication research is achieved by the output as represented as minimum grid harmonics, load current, and compensator current as verified in MATLAB/Simulink platform.

关键词： Grid Integrated-Photovoltaic Maximum Power Point Adaptive Neuro-Fuzzy Inference System moth flame optimization algorithm

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Lifetime Enhancement of Sensor Networks by the moth flame optimization

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WIRELESS PERSONAL COMMUNICATIONS 2021年第4期118卷 2807-2820页

作者： Pandey, Ashish Rajan, Abhishek Nandi, Arnab Balas, Valentina E. Natl Inst Technol Silchar Dept Elect & Commun Engn Silchar Assam India Natl Inst Technol Sikkim Elect & Elect Engn Dept South Sikkim Sikkim India Aurel Vlaicu Univ Arad Fac Engn Arad Romania

Advancements in wireless communication technologies have facilitated the deployment of large-scale Wireless Sensor Networks (WSNs). Due to the constraint of associated battery power, various optimization structures have been proposed to enhance the lifetime of WSNs. In this article, the concept of supernodes is used along with moth flame optimization algorithm to improve the lifetime of the heterogeneous WSNs. The moth flame optimization algorithm is used to achieve the energy-efficient clustering and energy-aware routing. The performance of moth flame optimization algorithm is compared with the other existing protocol, including Genetic algorithm and Particle Swarm optimization algorithm. The effects of varying populations of supernodes and sensor nodes on the network metrics are also analyzed here. The influence of the number of hops on the lifetime is also investigated considering two different positions of the base-station in WSNs.

关键词： moth flame optimization algorithm Particle swarm optimization Genetic algorithm Routing Clustering

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Design of moth flame optimization heuristics for integrated power plant system containing stochastic wind

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APPLIED SOFT COMPUTING 2021年 104卷 107193-107193页

作者： Khan, Babar Sattar Raja, Muhammad Asif Zahoor Qamar, Affaq Chaudhary, Naveed Ishtiaq Univ Engn & Technol Peshawar Dept Elect Energy Syst Engn USPCAS E Peshawar Khyber Pakhtunk Pakistan COMSATS Univ Islamabad Dept Elect & Comp Engn Campus Attock Attock Pakistan Natl Yunlin Univ Sci & Technol Future Technol Res Ctr 123 Univ RdSect 3 Touliu 64002 Yunlin Taiwan Int Islamic Univ Dept Elect Engn Islamabad Pakistan

In this investigation, nature-inspired heuristic strategy exploiting moth flame optimization (MFO) algorithm combined with active-set algorithm (ASA), interior point algorithm (IPA) and sequential quadratic programming (SQP) are presented to take care of the enhancement issues of economic load dispatch (ELD) problem involving valve point loading effect (VPLE) and stochastic wind (SW). The strength of MFO algorithm is used as a global search mechanism that explore and exploit the entire search space while ASA, IPA and SQP are responsible for refinement of local optimum. The performance of the design system is based on 40 generating units including 37 thermal and 3 wind power units and is evaluated to verify the effectiveness of the scheme. The worth of the design integrated heuristic of MFO algorithm is endorsed through outcomes of the state of the art counterpart solvers in case of ELD problems integrated with wind power units in terms of cost minimization and computational complexity parameters. (C) 2021 Elsevier B.V. All rights reserved.

关键词： moth flame optimization algorithm Sequential quadratic programming Active-set algorithm Interior-point algorithm Economic load dispatch Integrated power systems

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Prediction on recommender system based on bi-clustering and moth flame optimization

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APPLIED SOFT COMPUTING 2022年第0期120卷 108626-108626页

作者： Wu, Huan-huan Ke, Gang Wang, Yang Chang, Yu-Teng Tarim Univ Coll Informat Engn Alar Peoples R China Dongguan Polytech Dept Comp Engn Dongguan Peoples R China Yu Da Univ Sci & Technol Dept Informat Management Miaoli Taiwan

Concerning the problems of weak scalability of traditional collaborative filtering recommender systems, a scalable recommender system based on bi-clustering and moth flame optimization algorithm is proposed. First of all, the users-items scoring matrix is filtered and cleaned in order to reduce the computational overhead, afterwards the bi-clustering data structures are constructed for the processed matrix, and the algorithm searches for bi-cluster containing the target user. Then, the results of biclustering are set as the initial population, and the moth flame optimization algorithm is applied to deeply optimize the similar users. Finally, the unrated items are predicted for the target user, and the recommendation list is generated for the target user. Validation experiments are carried on different scales of datasets;the results show that the proposed system achieves a good scalability, and also good recommendation performance. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Recommender system Data scalability Popularity bias moth flame optimization algorithm Matrix bi-clustering Data mining Scoring prediction Collaborative filtering Similarity evaluation Optimal solution

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Stochastic optimization for energy economics and renewable sources management: A case study of solar energy in digital twin

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SOLAR ENERGY 2023年第1期262卷

作者： Gao, Jiaxi Huang, Haiyan East China Univ Sci & Technol Sch Informat Sci & Engn Shanghai 200237 Peoples R China East China Univ Sci & Technol Key Lab Smart Mfg Energy & Chem Proc Minist Educ Shanghai 200237 Peoples R China

As power system might be damaged and even shut down due to the high uncertainty effects, they are always challenged by the high load variations. This study proposes a novel technique for managing power in microgrids (MGs) that have stochastic loads in the presence of high penetration of renewable energy sources. It is likely that the security of the users will be violated as a result of centralized optimization techniques that require considerable computing power. Conversely, in distributed methods, each load and generation are presumed to be linked to a single bus, thereby neglecting the electrical grid and its limitations. Due to this, the suggested distributed energy management strategy (EMS) decomposes the optimization problem into two levels, involving the MG centralized controller, local controllers, distribution network, and related restrictions. Because it is impossible to predict the loads precisely, the loads are regarded as stochastic variables which are modeled using probabilistic approach. The moth flame optimization algorithm is used to solve the optimization problem. A distributed EMS has been developed that integrates solar/photovoltaic (PV) units, wind turbines, diesel generators, and batteries in a digital twin area. Based on the simulations, the suggested distributed EMS is shown to be efficient and useful for the practical studies.

关键词： Stochastic optimization moth flame optimization algorithm Microgrid Energy management Optimal power flow Solar digital twin

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Application of artificial intelligence technique in optimization and prediction of the stability of the walls against wind loads in building design

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MECHANICS OF ADVANCED MATERIALS AND STRUCTURES 2024年第19期31卷 4755-4772页

作者： Hu, Danping Sun, Hongquan Mehrabi, Peyman Ali, Yasser A. Al-Razgan, Muna Luzhou Vocat & Tech Coll Sch Intelligent Construct Luzhou Sichuan Peoples R China Luzhou Key Lab Intelligent Construct & Low Carbon Luzhou 646000 Sichuan Peoples R China Western Sydney Univ Ctr Infrastruct Engn Penrith NSW 2751 Australia King Saud Univ Coll Comp & Informat Sci Dept Informat Syst Riyadh Saudi Arabia King Saud Univ Coll Comp & Informat Sci Dept Software Engn Riyadh Saudi Arabia

The present study is done to perform the optimal design of the structural components of the buildings against the unwanted wind load exerted on their outer face. To this end, the case study of the research is the outer wall made of a one-floor building modeled as a rectangular plate with only one free edge and three clamped ones. It is assumed that the wall is a sandwich plate whose core is made of auxetic material and dace-sheets are reinforced with nanoparticles of graphene platelets (GPL). Differential equations governing the system's motion are obtained within the background of the plate's shear-deformation theories. The stability analysis of the sandwich wall is performed based on the application of artificial intelligence (AI) methods optimized with an innovative optimization approach to gain a high level of accuracy. To determine the stability information of the system at the train points, the differential quadrature approach (DQA) is applied as the solver of differential equations of motion. The accuracy of the methods used in this paper is examined and verified by comparing the results with those acquired in the articles published previously. The results obtained in this study provide very useful information about the stability response of lightweight building components through AI-based solutions.

关键词： Lightweight auxetic structures composite sandwich structural components artificial intelligence methods in building design moth flame optimization algorithm deep-learning-based regressor

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Swarm-Based Extreme Learning Machine Models for Global optimization

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Computers, Materials & Continua 2022年第3期70卷 6339-6363页

作者： Mustafa Abdul Salam Ahmad Taher Azar Rana Hussien Artificial Intelligence Department Faculty of Computers and Artificial IntelligenceBenha UniversityBenha13518Egypt Scientific Computing Department Faculty of Computers and Artificial IntelligenceBenha UniversityBenha13518Egypt

Extreme Learning Machine(ELM)is popular in batch learning,sequential learning,and progressive learning,due to its speed,easy integration,and generalization ***,Traditional ELM cannot train massive data rapidly and efficiently due to its memory residence,high time and space *** ELM,the hidden layer typically necessitates a huge number of ***,there is no certainty that the arrangement of weights and biases within the hidden layer is *** solve this problem,the traditional ELM has been hybridized with swarm intelligence optimization *** paper displays five proposed hybrid algorithms“Salp Swarm algorithm(SSA-ELM),Grasshopper algorithm(GOA-ELM),Grey Wolf algorithm(GWO-ELM),Whale optimizationalgorithm(WOA-ELM)andmoth flame optimization(MFO-ELM)”.These five optimizers are hybridized with standard ELM methodology for resolving the tumor type classification using gene expression *** proposed models applied to the predication of electricity loading data,that describes the energy use of a single residence over a fouryear *** the hidden layer,Swarm algorithms are used to pick a smaller number of nodes to speed up the execution of *** best weights and preferences were calculated by these algorithms for the hidden *** results demonstrated that the proposed MFO-ELM achieved 98.13%accuracy and this is the highest model in accuracy in tumor type classification gene expression *** in predication,the proposed GOA-ELM achieved 0.397which is least RMSE compared to the other models.

关键词： Extreme learning machine salp swarm optimization algorithm grasshopper optimization algorithm grey wolf optimization algorithm moth flame optimization algorithm bio-inspired optimization classification model and whale optimization algorithm

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Convolutional Neural Network&x2014;Optimized moth flame algorithm for Shallow Landslide Susceptible Analysis

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IEEE ACCESS 2020年 8卷 32727-32736页

作者： Vu Dong Pham Quoc-Huy Nguyen Huu-Duy Nguyen Van-Manh Pham Van Manh Vu Quang-Thanh Bui VNU Univ Sci Fac Geog Ctr Appl Res Remote Sensing & GIS CARGIS Hanoi 120041 Vietnam VNU Univ Sci Fac Geog Hanoi 120041 Vietnam VNU Univ Sci Fac Environm Sci Hanoi 120041 Vietnam

Convolutional neural network (CNN) is a widely used method in solving classification and regression applications in industries, engineering, and science. This study investigates the optimizing capability of a swarm intelligence algorithm named moth flame optimizer (MFO) for the optimal search of a CNN hyper-parameters (values of filters) and weights of fully connected layers. The proposed model was run with a 3-dimensional dataset (7 width height depth), which was constructed through including seven neighbor pixels (vertically and horizontally) from landslide location and 12 predictor variables. Muong Te district, Lai Chau province, Vietnam was selected as the case study, as it had recently undergone severe impacts of landslides and flash floods. The performance of this proposed model was compared with conventional classifiers, i.e., Random forest, Random subspace, and CNN-optimized Adaptive gradient descend, by using standard metrics. The results showed that the CNN-optimized MFO (Root mean square error & x003D;0.3685, Mean absolute error & x003D;0.2888, Area under Receiver characteristic curve & x003D;0.889 and Overall accuracy & x003D;80.1056 & x0025;) outperformed the benchmarked methods in all comparing indicators. Besides, the statistical test of difference was also carried out by using the Wilcoxon signed ranked test for non-parametric variables. With these statistical measurements, the proposed model could be used as an alternative solution for landslide susceptibility mapping to support local disaster preparedness plans.

关键词： Terrain factors Rivers Indexes optimization Training Fires Convolutional neural network meta-heuristic algorithm moth flame optimization algorithm landslide susceptibility

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Fabric defect detection based on feature fusion of a convolutional neural network and optimized extreme learning machine

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TEXTILE RESEARCH JOURNAL 2022年第7-8期92卷 1161-1182页

作者： Zhou, Zhiyu Deng, Wenxiong Zhu, Zefei Wang, Yaming Du, Jiayou Liu, Xiangqi Zhejiang Sci Tech Univ Sch Informat Sci & Technol Xiasha Hang Zhou Zhejiang Peoples R China Hangzhou Dianzi Univ Sch Mech Engn Hangzhou Peoples R China Lishui Univ Lishui Peoples R China

Aiming to accurately detect various defects in the fabric production process, we propose a fabric defect detection algorithm based on the feature fusion of a convolutional neural network (CNN) and optimized extreme learning machine (ELM). Firstly, we use transfer learning to transfer the parameters of the first 13 convolutional layers and first two fully connected layers of a VGG16 network model as pre-trained by ImageNet to the initial model and fine-tune the parameters. Subsequently, the fine-tuned model is used as a feature extractor to extract features of RGB images and their corresponding L-component images. A principal component analysis is used to reduce the dimensionality of the features and fuse the reduced features. The moth flame optimization (MFO) algorithm is used to initialize the optimization variables of a parallel chaotic search (PCS) algorithm, and the PCS algorithm (as optimized by the MFO algorithm) is used to optimize the input weight and bias of the ELM (i.e., the PCS-MFO-ELM (PMELM)). Finally, the PMELM is used to replace the softmax classifier of the CNN to classify and detect fabric defect features. The experimental results show that on the amplified TILDA dataset, the precision, recall, F1-score, and accuracy rates of this algorithm for fabric holes, stains, warp breaks, dragging, and folds in fabric can reach 98.57%, 98.52%, 98.52%, and 98.50%, respectively, that is, higher than those of other algorithms. Through a validity experiment, this method is shown to be suitable for defect detection for unpatterned fabrics, regular patterned fabrics, and irregularly patterned fabrics.

关键词： Convolutional neural network principal component analysis extreme learning machine feature fusion moth flame optimization algorithm parallel chaotic search algorithm

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