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The Power Load Forecasting Model of Combined SaDE-ELM and FA-CAWOA-SVM Based on CSSA

IEEE ACCESS 2024年 12卷 41870-41882页

作者： Wang, Zuoxun Ku, Yangyang Liu, Jian Shandong Technol & Business Univ Sch Informat & Elect Engn Yantai 264005 Peoples R China Qilu Univ Technol Shandong Acad Sci Sch Informat & Automat Jinan 250353 Peoples R China

The security and stability of the power grid are directly affected by the accuracy of power load forecasting. Additionally, it plays an important role in power system planning. In order to enhance forecasting accuracy, a combined forecasting model is proposed in this paper. Firstly, preprocessing of the original data is conducted through improved singular spectrum analysis. Subsequently, load data prediction is carried out by the adaptive evolutionary extreme learning machine (SaDE-ELM). Additionally, load data prediction is performed using the support vector machine model(SVM), which is optimized by the chaotic adaptive whale algorithm based on the firefly disturbance strategy (FA-CAWOA-LSSVM). In the final step, the weight coefficients of the two prediction models are calculated by the chaotic sparrow search algorithm (CSSA). The load prediction results are obtained through the weighted summation of the two predictions. Superior performance is demonstrated by the combined prediction model compared with other single prediction models. The data preprocessing method, based on improved singular spectrum analysis, effectively enhances prediction accuracy.

关键词： Spectral analysis Load management Load forecasting Predictive models Adaptation models Forecasting Support vector machines Stability analysis Security whale optimization algorithms Singular spectrum analysis combinatorial forecasting model power load forecasting

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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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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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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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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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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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Network Security Situation Prediction Model Based on VMD Decomposition and DWOA Optimized BiGRU-ATTN Neural Network

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IEEE ACCESS 2023年 11卷 129507-129535页

作者： Zhang, Shengcai Fu, Qiming An, Dezhi Gansu Univ Polit Sci & Law Sch Cyber Secur Lanzhou 730070 Peoples R China

The widespread adoption of Internet-of-Things (IoT) devices has resulted in a comprehensive transformation of human life. However, the network security challenges posed by the IoT devices have become increasingly severe, necessitating the implementation of effective security mechanisms. Network security situational awareness enables an effective network state prediction for better formulation of network security defense strategies. Existing network security situational prediction methods are typically constrained by situational sequence data, especially those sequences with a high degree of non-stationarity, leading to unstable predictions and low performance. Moreover, in real-world application scenarios, the network security situational sequences are often highly non-stationary. To address these challenges, we introduce a novel hybrid prediction model named Variational Mode Decomposition (VMD) - Dynamic whale optimization Algorithm (DWOA) - Bidirectional Gated Recurrent Unit (BiGRU) - Attention Mechanism (ATTN). The proposed model integrates VMD, BiGRU, ATTN, and DWOA. Initially, network security situational awareness sequences are processed using VMD to decompose them into a series of subsequences, thus reducing the non-stationarity of the original sequences. Subsequently, an enhanced DWOA optimization algorithm is introduced for tuning the hyperparameters of the BiGRU-ATTN network. Ultimately, BiGRU-ATTN is employed to predict each of these subsequences, which are then aggregated to yield the final network security situational prediction value. When compared with several existing methods on public network security datasets, the proposed VMD-DWOA-BiGRU-ATTN method demonstrated an improvement in the R<^>2 values ranging from 6.34% to 52.61%. These results substantiate that the model significantly enhances predictive performance.

关键词： Network security Predictive models Security Time series analysis Data models Prediction algorithms Internet of Things Bidirectional control whale optimization algorithms Attention mechanism bi-directional gated recurrent unit dynamic whale optimization algorithm network security situation prediction variational mode decomposition

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Research on Interference Resource optimization Based on Improved whale optimization Algorithm

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IEEE ACCESS 2025年 13卷 83136-83147页

作者： Chen, Xuyi Ma, Mingxi Liu, Chengkui Xie, Haifeng Wang, Shaoqi Air Force Command Coll Beijing 100080 Peoples R China Army 96716 Ganzhou 341001 Peoples R China

Interference resource optimization is a prerequisite for air defense suppression mission planning, and the degree of optimization of interference resources directly determines the quality of the interference results. In this problem, this paper establishes an interference resource optimization model with radar detection probability, interference effectiveness, and interference bandwidth utilization as the objective functions. Then, in the solution process, to address the issues of the whale optimization Algorithm (WOA) easily falling into local optima and low convergence accuracy, the BIO-WOA (Bernoulli Chaotic mapping In-nonlinear Factors and Opposition-based Learning Improved whale optimization Algorithm, BIO-WOA) is proposed. First, the population initialization is completed using Bernoulli chaotic mapping based on the whale optimization algorithm, increasing the diversity and uniformity of solutions and enhancing the algorithm's global search capability. Then, a nonlinear convergence factor is proposed to balance the local and global search capabilities of the algorithm. Subsequently, the centroid opposition-based learning is used to generate mutated whales, improving the algorithm's ability to escape local optima. Finally, the effectiveness of the algorithm is verified through test functions and simulation experiments.

关键词： Interference Radar Jamming whale optimization algorithms optimization Radar detection Heuristic algorithms Resource management Radar measurements Linear programming whale optimization algorithm chaotic mapping nonlinear convergence factor centroid opposition-based learning interference resource optimization

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Prediction of Converter Tapping Weight Based on Principal Component Analysis-whale optimization Algorithm-Backpropagation Algorithm Neural Network Model

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STEEL RESEARCH INTERNATIONAL 2024年第3期95卷

作者： Xue, Chao Xu, Weili Bao, Yanping Zhao, Lihua Liu, Xin Zheng, Ruixuan Univ Sci & Technol Beijing State Key Lab Adv Met Beijing 100083 Peoples R China Jianlong Xilin Iron & Steel Co Ltd Technol Ctr Yichun 153000 Peoples R China Univ Sci & Technol Beijing Sch Met & Ecol Engn Beijing 100083 Peoples R China

Tapping weight is an important parameter in converter blowing process, wherein precisely predicting the quantity of steel required in an alloy baking converter can effectively guide the requirement of alloy ingredients. In practical production, the main approach is empirical estimation, despite its low accuracy. Employing a general neural network model for prediction requires to gather the converter blowing parameters and endpoint temperature measurement sampling parameters as the model inputs. However, this data cannot be obtained until the blowing process reaches its endpoint, rendering it impractical for alloy batching that requires advance preparation for baking. In this study, a principal component analysis-whale optimization algorithm-backpropagation algorithm (PCA-WOA-BP) neural network tapping prediction model is developed using raw material parameters available before alloy baking as input. This model is integrated into the intelligent alloy reduction model of a factory. The model achieves a regression coefficient R2 of 0.823, with 98.53% of furnaces having a prediction error of less than 2 t. The tapping weight of 20 consecutive heats in actual production is predicted;the error range is less than 80% within 1 t, and the converter tapping weight is predicted accurately. By using the collected molten iron and scrap information of the converter through the adjusted principal component analysis-whale optimization algorithm-backpropagation algorithm tapping weight prediction model, a very accurate prediction value of the converter tapping weight can be obtained in the early stage of converter *** (c) 2024 WILEY-VCH GmbH

关键词： backpropagation algorithm neural networks data analyses industrial applications tapping weights whale optimization algorithms

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Dynamic Reactive Power optimization Strategy for AC/DC Hybrid Power Grid Considering Different Wind Power Penetration Levels

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IEEE ACCESS 2024年 12卷 187471-187482页

作者： Feng, Nan Feng, Yuyao Su, Yun Zhang, Yajun Niu, Tao State Grid Shanghai Elect Power Co Ltd Elect Power Res Inst Shanghai 200437 Peoples R China Chongqing Univ Elect Engn Dept Chongqing 400044 Peoples R China

With the increasing capacity of wind power integration and the further development of AC/DC hybrid power grids, the problem of voltage stability in the receiving end has become increasingly severe. The installation of dynamic reactive power compensation devices is a feasible measure to improve the voltage stability of the receiving end power grid. This paper proposes a dynamic reactive power optimization strategy for AC/DC hybrid power grids considering the integration of new energy sources. Firstly, a detailed mathematical model of the AC/DC hybrid system is established. In order to enhance the voltage restoration capability after a fault, the definition of a fault recovery indicator is provided. Subsequently, an optimal scheduling strategy for dynamic reactive power compensation devices is formulated to maximize the value of the fault recovery indicator. Considering the nonlinearity and non-convexity of the optimization model, trajectory sensitivity method and whale optimization algorithm are adopted to enhance the solution efficiency. Finally, an improved IEEE-9 test system is employed to demonstrate the effectiveness and feasibility of the proposed approach.

关键词： Reactive power Power system dynamics Mathematical models Transient analysis Power system stability Steady-state Power grids AC-DC power converters Wind power generation whale optimization algorithms Fault detection AC/DC hybrid power grids wind power dynamic reactive power fault recovery indicator trajectory sensitivity method whale optimization algorithm

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