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Real Time Intelligent Detection of PQ Disturbances With Variational Mode Energy Features and Hybrid Optimized Light GBM Classifier

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IEEE ACCESS 2024年 12卷 47155-47172页

作者： Mishra, Sairam Mallick, Ranjan Kumar Gadanayak, Debadatta Amaresh Nayak, Pravati Sharma, Renu Panda, Gayadhar Al-Numay, Mohammed S. Siano, Pierluigi Siksha O Anushandhan Deemed Univ Dept Elect Engn Bhubaneswar 751030 Odisha India Siksha O Anusandhan Deemed Univ Dept Elect & Elect Engn Bhubaneswar 751030 Odisha India Natl Inst Technol Meghalaya Shillong 793003 Meghalaya India King Saud Univ Coll Engn Elect Engn Dept Riyadh 11421 Saudi Arabia Univ Salerno Dept Management & Innovat Syst I-84084 Salerno Italy

The modern era power system is constantly undergoing constructive changes and implementations both in source and load side. Certainly, the distributed generators, unconventional/nonlinear loads, charging stations etc are mostly integrated through power electronics interfaces. As a result, frequent power quality disturbances appear in the system that is to be mitigated at the earliest. Since detection is the prerequisite for mitigation, therefore the article presents a novel intelligent power quality detection scheme to detect and classify the PQ Events. At first, the energy feature of the 5 band limited modes are calculated from variational mode decomposed voltage signals. Then the mode energy features are utilized to train a novel Hybrid Arithmetic whale Optimized light gradient boosting machine classifier. A total of 15 different PQ events have been investigated and exceptional classification results have obtained with optimum computational complexity, both under noiseless and noisy conditions. Moreover, the accuracy of the proposed PQ classification schemes found to be towering against other related pre-published works. Finally, the ability of the proposed detection scheme is validated in real time though OPAL-RT 4510 and grid simulator hardware in loop setup.

关键词： Transient analysis Boosting Power quality Mathematical models Harmonic analysis Bagging whale optimization algorithms optimization methods Gradient methods variational mode decomposition whale optimization arithmetic optimization light gradient boosting machine

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Multi-Step-Ahead Wind Speed Forecast System: Hybrid Multivariate Decomposition and Feature Selection-Based Gated Additive Tree Ensemble Model

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IEEE ACCESS 2024年 12卷 58750-58777页

作者： Joseph, Lionel P. Deo, Ravinesh C. Casillas-Perez, David Prasad, Ramendra Raj, Nawin Salcedo-Sanz, Sancho Univ Southern Queensland Sch Math Phys & Comp Springfield Qld 4300 Australia Univ Southern Queensland Ctr Appl Climate Sci Toowoomba Qld 4350 Australia Univ Rey Juan Carlos Dept Signal Proc & Commun Madrid 28942 Spain Univ Fiji Sch Sci & Technol Dept Sci Lautoka Fiji Univ Alcala Dept Signal Proc & Commun Madrid 28805 Spain

Wind, being a clean and sustainable resource, boasts environmental advantages. However, its electricity generation faces challenges due to unpredictable variations in wind speed (WS). Accurate predictions of these variations would allow mixed grids to adjust their energy mix in real-time, ensuring overall stability. For this purpose, the paper develops a new hybrid gated additive tree ensemble (H-GATE) model for accurate multi-step-ahead WS predictions. First, the multivariate empirical mode decomposition (MEMD) simultaneously demarcates the multivariate data into intrinsic mode functions (IMFs) and residuals. These components represent underlying trends, periodicity, and stochastic patterns in WS variations. The IMF and residual components are pooled in respective sets, and an opposition-based whale optimization algorithm (OBWOA) is applied for dimensionality reduction. The selected features are used by GATE tuned with Bayesian optimization (BO) to forecast the individual IMF and residual components. The outputs are summed to obtain the final multi-step-ahead WS forecasts. The proposed H-GATE is benchmarked against standalone (S-GATE, S-CLSTM, and S-ABR) and hybrid (H-CLSTM and H-ABR) models. Based on all statistical metrics and diagnostic plots, H-GATE outperforms all comparative models at all forecast horizons, accumulating the lowest mean absolute percentage error (MAPE) of 6.13 - 9.93% (at t(L+1) ), 8.67 - 14.07% (at t(L+2 )), and 11.60 - 18.37% (at t(L+3) ) across all three sites. This novel multi-step-ahead WS forecasting strategy can significantly benefit grid operators by helping anticipate fluctuations in wind power generation. This can assist in optimizing energy dispatch schedules, reducing reliance on backup power sources, and enhancing overall grid stability. Practical implementation of this method can help meet the rising energy demands through renewable wind energy.

关键词： Forecasting Long short term memory Predictive models Logic gates Computational modeling Autoregressive processes Wind power generation Velocity measurement Wind speed whale optimization algorithms Bayes methods Wind speed forecasting gated additive tree ensemble multivariate empirical mode decomposition opposition-based whale optimization algorithm Bayesian optimization

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Improved Marine Predators Algorithm Optimized BiGRU for Strip Exit Thickness Prediction

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IEEE ACCESS 2024年 12卷 56719-56729页

作者： Luo, Hao Chen, Jiaxuan Sun, Zhan Zhang, Ying Zhang, Li Liaoning Univ Coll Informat Shenyang 110036 Peoples R China

The strip exit thickness is an important indicator for measuring the production quality of steel rolling products. Controlling its accuracy is crucial in producing high-quality rolling products in the steel industry. This study proposed an improved marine predators algorithm (IMPA) optimized bidirectional gated recurrent unit (BiGRU) for predicting the strip exit thickness. Firstly, an adaptive weight factor was introduced into the marine predators algorithm (MPA) to overcome the drawbacks of slow early convergence speed and limited search range of the algorithm, addressing the issue of local optimization. Secondly, an average fitness strategy was developed to enhance the quality of the algorithm population, improve optimization accuracy, and strengthen algorithm stability. In addition, IMPA was used to optimize parameters, reducing the impact of hyper-parameters such as the number of hidden layer neurons, learning rate, and batch size on the prediction accuracy of BiGRU, thereby establishing IMPA-BiGRU model. The experiments were conducted on an actual rolling process dataset. The experimental results showed that IMPA outperformed traditional MPA, particle swarm optimization (PSO), grey wolf optimization (GWO), whale optimization algorithm (WOA), and recent variants of MPA. The IMPA-BiGRU also demonstrated excellent prediction capabilities for strip exit thickness, meeting the practical production requirements for high-quality rolling products.

关键词： Prediction algorithms Statistics Social factors Predictive models optimization Vectors Parameter estimation Feature detection Marine animals Heuristic algorithms whale optimization algorithms BiGRU parameter optimization feature selection marine predators algorithm heuristic algorithm strip thickness

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Block size optimization for PoW consensus algorithm based blockchain applications by using whale optimization algorithm

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2022年第2期30卷 406-419页

作者： Aygun, Betul Arslan, Hilal Izmir Demokrasi Univ IIBF Management Informat Syst Izmir Turkey Ankara Yildirim Beyazit Univ Fac Engn & Nat Sci Dept Software Engn Ankara Turkey

Blockchain-based applications come up with cryptocurrencies, especially Bitcoin, introducing a distributed ledger technologies for peer-to-peer networks and essentially records the transactions in blocks containing hash value of the previous blocks. Block generation constitutes the basis of this technology, and the optimization of such systems is among the most crucial concerns. Determining either the block size or the number of transactions in the block brings out a remarkable problem that has been solved by the miners in recent years. First, higher block size results in higher transaction time, on the other hand, smaller block size has many disadvantages such as security, lower transaction fees, lower transaction numbers in a given time interval, which makes it unable to compete with other currency systems due to this bulky structure and higher block generation time. In this study, multiobjective optimization problem (OP) is proposed by minimizing block generation and transmission time. This multiobjective OP is transformed into a single OP by applying weighted sum method. To determine the optimal block size, particle swarm optimization (PSO) algorithm and whale optimization evolutionary algorithm (WOA) are employed. Although both algorithms have capability to reach optimum block size and corresponding time, WOA achieves better performance than PSO in terms of the convergence speed and output fluctuation. Moreover, analysis of the prediction of optimum block size is carried out under different weights which creates many optimization functions. Experimental results indicate that if higher weight is assigned to the transmission time, then block size decreases sharply. Furthermore, the experimental results reveal that design of the blockchain network and number of nodes in network profoundly affect the block size selection due to the time constraints.

关键词： Blockchain block size whale optimization algorithms particle swarm optimization

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Real-Time Road Surface Unevenness Recognition for ITS Using BWO-BiGRU-Transformer With Adaptive CKF

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年

作者： Cao, Xianteng Niu, Huawei Chen, Zhengqing Hunan Univ State Key Lab Bridge Safety & Resilience Changsha 410082 Peoples R China Hunan Univ State Key Lab Adv Design & Mfg Technol Vehicle Changsha 410082 Peoples R China

The recognition of the road surface unevenness can provide an important support for optimizing the maintenance and management strategies of local intelligent transportation networks. This study proposes a BiGRU-Transformer model optimized with Beluga whale optimization (BWO) and combined with the Cubature Kalman Filter (CKF) for road surface unevenness recognition in intelligent transportation systems. The model addresses the limitations of existing methods in time series data processing and nonlinear state estimation. It uses BiGRU to model short-term vehicle vibration dependencies and incorporates the Transformer's self-attention mechanism to capture long-term and global dynamics. BWO optimizes the global hyperparameters of the model, avoiding local optima and improving accuracy. CKF updates the noise matrix in real-time, enhancing prediction and generalization through recursive nonlinear state estimation. Experimental results demonstrate significant improvements in performance, including recognition accuracy, noise immunity, and convergence speed, when applied to real road datasets. The proposed model outperforms existing methods, offering enhanced road condition identification and providing effective real-time decision support for intelligent transportation systems, thereby improving management efficiency and responsiveness.

关键词： Roads Neural networks Vibrations Vehicle dynamics optimization Mathematical models Accuracy whale optimization algorithms Feature extraction Training Road surface unevenness BiGRU-Transformer Intelligent transportation Cubature Kalman Filter Beluga whale optimization

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Optimizing Energy Consumption and Latency in IoT Through Edge Computing in Air-Ground Integrated Network With Deep Reinforcement Learning

IEEE OPEN JOURNAL OF VEHICULAR TECHNOLOGY

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IEEE OPEN JOURNAL OF VEHICULAR TECHNOLOGY 2025年 6卷 412-425页

作者： That, Vitou Chhea, Kimchheang Lee, Jung-Ryun Chung Ang Univ Dept Intelligent Energy Ind Convergence Seoul 156756 South Korea Chung Ang Univ Dept Intelligent Energy Ind Seoul 156756 South Korea Chung Ang Univ Sch Elect & Elect Engn Seoul 156756 South Korea

With the increasing computational demands of Internet of Things (IoT) applications, air-ground integrated networks (AGIN), leveraging the capabilities of Unmanned Aerial Vehicles (UAVs) and High-Altitude Platform (HAP), provides an essential solution to these challenges. In this paper, we propose a framework that facilitates local computing at IoT devices and offers the flexibility to offload tasks to aerial platforms when necessary. Specifically, we formulate a multi-objective optimization model aiming at simultaneously minimizing energy consumption and reducing task latency by adjusting control variables such as transmit power, offloading decisions, and UAV placement in a distributed network of IoT devices. Our proposed framework employs Deep Deterministic Policy Gradient (DDPG) techniques to dynamically optimize network operations, allowing for efficient real-time adjustments to network conditions and task demands. The performance of the proposed algorithm is compared to traditional algorithms, including the whale optimization Algorithm (WOA), Gradient Search with Barrier, and Bayesian optimization (BO). Simulation results show that this approach significantly minimizes energy consumption and latency, outperforming conventional optimization methods. Additionally, scalability tests confirm that our framework can efficiently integrate an increasing number of IoT devices and UAVs.

关键词： Internet of Things Energy consumption Autonomous aerial vehicles optimization Resource management Heuristic algorithms Servers Delays Vehicular and wireless technologies whale optimization algorithms Internet of Things (IoT) air-ground integrated networks (AGIN) edge computing Deep Deterministic Policy Gradient (DDPG) UAV placements multi-objective optimization

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Multi-strategy optimized marine predator algorithm for customized association rule mining

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IEEE Sensors Journal 2025年第12期25卷 22532-22544页

作者： Liu, Qin Chen, Yang-Yang Yu, Rui Liu, Tianrun Zhang, Jing Southeast University School of Cyber Science and Engineering Nanjing210096 China Southeast University School of Automation Nanjing210096 China Advanced Ocean Institute of Southeast University Nantong226000 China

Note that current association rule mining algorithms are lacking in customized mining interested rules and uninterested rules for users. This paper proposed a novel heuristic optimization algorithm named Differential Variability (DV) and Oscillatory Functions (OF) based Marine Predators Algorithm (DVOFMPA) to solve the customized association rule mining (CARM) problem. First, differential variability is utilized in the high-velocity ratio phase to improve the quality of the prey. Then, the oscillatory function is applied in the unit velocity ratio phase to enhance the sensitivity of the prey. Finally, a customized value function based on the user’s interested items, uninterested items, and the values of optimization weight factors is designed to evaluate the filter rules associated with the user-specified *** effectiveness of DVOFMPA is tested on 7 public datasets and 2 augmented datasets with different sizes and sparseness. Compared to the baseline algorithms, the number of interested rules improves at least 7.2% and at the same time the number of uninterested rules reduces 12.7%, which indicates the advantages of the proposed DVOFMPA in the customized association rule mining. © 2001-2012 IEEE.

关键词： Association rule learning optimization Animals Heuristic algorithms Memory management Intelligent sensors whale optimization algorithms Training Symbols Particle swarm optimization

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Dependency Structure-Based Rules Using Root Node Technique for Explicit Aspect Extraction From Online Reviews

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IEEE ACCESS 2023年 11卷 65117-65137页

作者： Mishra, Pallavi Panda, Sandeep Kumar ICFAI Fdn Higher Educ Fac Sci & Technol IcfaiTech Dept Comp Sci & Engn Hyderabad 501203 India ICFAI Fdn Higher Educ Fac Sci & Technol IcfaiTech Dept Artificial Intelligence & Data Sci Hyderabad 501203 India

Aspect-based sentiment analysis (ABSA) includes aspect extraction and sentiment analysis on those extracted aspects. This paper is mainly focused on the explicit aspect extraction from product based online customer reviews. Many research studies on explicit aspect extractions have adopted dependency rule-based techniques but limited their focus to only nouns and noun phrases as potential aspects. Moreover, extraction of multiple-aspects and multi-word aspects from online reviews are also not addressed by previous research studies. In this paper, we have proposed a Dependency structure-based rules using ROOT Node (DS-RN) technique using spaCy dependency parser to extract nouns, noun phrases, verbs, verb phrases in addition to single word, multiple aspects and multi word aspect extractions from customer review datasets. In our proposed methodology, 30 new dependency-based rules are formulated and implemented on 5 different product-based datasets. This study is based on the pattern analysis of dependency structures of review sentences to develop dependency-based rules for explicit aspect extraction. The proposed approach also incorporated lexicon-based pruning techniques to remove irrelevant aspects and retain correct aspects. The performance results on 5 different product-based customer review datasets demonstrate that our proposed DS-RN approach outperforms all other state-of-the-art baseline works with averaged value of precision as 87%, recall with 97% and 91% as F1-score.

关键词： Sentiment analysis Feature extraction Task analysis Batteries Electronic commerce whale optimization algorithms Periodic structures Aspects dependency structure-based rule explicit aspect extraction opinion lexicon ROOT node sentiment analysis

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A Hybrid Heuristic Algorithm for Optimizing the Optimal Landing Time Window Problem in Intelligent Air Transportation Systems

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年

作者： Lu, Yong Chen, Shikang Pan, Xiuqin Minzu Univ China Sch Informat Engn Beijing 100081 Peoples R China

The Aircraft Landing Problem (ALP) involves optimizing the scheduling of flight arrivals and departures while simultaneously managing airport resources to maximize flight utilization, minimize delays, reduce costs, and enhance overall operational efficiency. However, existing algorithms for solving ALP often face challenges in converging optimally and effectively handling penalty values associated with timing violations. To address these issues, this paper proposes a novel hybrid heuristic algorithm, AGWOA, designed to enhance convergence rates and effectively manage penalties in ALP. AGWOA integrates Artificial Bee Colony (ABC) and Genetic Algorithm (GA) techniques into the whale optimization Algorithm (WOA), leveraging their complementary strengths to strengthen global search efficiency and refine local constraint handling. This integration accelerates convergence and significantly mitigates penalty costs. Moreover, AGWOA incorporates an adaptive coefficient to facilitate improved convergence, along with a Fast Convergence Update Mechanism (FCUM) to guide the algorithm toward optimal solutions more efficiently. Experimental results conducted on public datasets demonstrate that AGWOA outperforms existing advanced algorithms in both convergence speed and penalty minimization. Specifically, AGWOA achieves a 4.05% decrease in penalty costs compared to baseline algorithms. These results underscore AGWOA's effectiveness in overcoming the challenge of slow convergence and its competitive advantage over other methods in optimizing ALP. The proposed algorithm offers a promising solution for real-world ALP applications, significantly enhancing airline operational efficiency and optimizing resource management.

关键词： Heuristic algorithms Aircraft Convergence Mathematical models Airports whale optimization algorithms Genetic algorithms Spirals whales Resource management ALP WOA heuristic algorithm problem optimization intelligent air transportation systems

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Double Filter and Double Wrapper Feature Selection Algorithm for High-Dimensional Data Analysis

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IEEE ACCESS 2025年 13卷 86185-86202页

作者： Chen, Hong Zheng, Yuefeng Jilin Normal Univ Sch Math & Comp Sci Siping 136000 Peoples R China

With the advent of the big data era, we often deal with datasets containing a large number of redundant features, and in this context, dimensionality reduction of data becomes crucial. To address this issue, this study proposes a double filter and double wrapper (DFDW) feature selection algorithm for high-dimensional data. In the double filter stage, the algorithm first evaluates all features from two perspectives using two filter algorithms: ReliefF and the Pearson correlation coefficient. It then selects the top k features and obtains a candidate feature subset F by taking the intersection. Next, the standard Cauchy distribution was used for population initialization. Subsequently, the algorithm enters the double wrapper stage, where it uses the Random Walk whale optimization Algorithm (RWWOA) and the improved Adaptive Differential Evolution (ADE) to jointly optimize and obtain the optimal feature subset. Among them, in order to overcome the problem of single algorithm falling into the local optimum, the Algorithm Iteration Mechanism is proposed, which selectively runs two wrapper algorithms to make the algorithm jump out of local optimum and explore a broader optimization space. Finally, we verified the effectiveness of the algorithm through three sets of comparative experiments. The experimental results show that the DFDW algorithm performed well in obtaining the optimal feature subsets on 10 high-dimensional datasets, with an average classification accuracy of more than 95.1% on 8 datasets, a dimensionality reduction rate of less than 0.64% on all datasets, and the lowest dimensionality reduction rate of 0.19%.

关键词： Feature extraction Classification algorithms whale optimization algorithms Filtering algorithms Correlation coefficient Correlation Dimensionality reduction Computational efficiency Vectors High dimensional data Feature selection whale optimization algorithm differential evolution ReliefF Pearson correlation coefficient

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