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  • Open Access

    ARTICLE

    Short-Term Wind Power Forecast Based on STL-IAOA-iTransformer Algorithm: A Case Study in Northwest China

    Zhaowei Yang1, Bo Yang2,*, Wenqi Liu1, Miwei Li2, Jiarong Wang2, Lin Jiang3, Yiyan Sang4, Zhenning Pan5

    Energy Engineering, Vol.122, No.2, pp. 405-430, 2025, DOI:10.32604/ee.2025.059515 - 31 January 2025

    Abstract Accurate short-term wind power forecast technique plays a crucial role in maintaining the safety and economic efficiency of smart grids. Although numerous studies have employed various methods to forecast wind power, there remains a research gap in leveraging swarm intelligence algorithms to optimize the hyperparameters of the Transformer model for wind power prediction. To improve the accuracy of short-term wind power forecast, this paper proposes a hybrid short-term wind power forecast approach named STL-IAOA-iTransformer, which is based on seasonal and trend decomposition using LOESS (STL) and iTransformer model optimized by improved arithmetic optimization algorithm (IAOA).… More >

  • Open Access

    ARTICLE

    Enhanced Multi-Object Dwarf Mongoose Algorithm for Optimization Stochastic Data Fusion Wireless Sensor Network Deployment

    Shumin Li1, Qifang Luo1,2,*, Yongquan Zhou1,2

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1955-1994, 2025, DOI:10.32604/cmes.2025.059738 - 27 January 2025

    Abstract Wireless sensor network deployment optimization is a classic NP-hard problem and a popular topic in academic research. However, the current research on wireless sensor network deployment problems uses overly simplistic models, and there is a significant gap between the research results and actual wireless sensor networks. Some scholars have now modeled data fusion networks to make them more suitable for practical applications. This paper will explore the deployment problem of a stochastic data fusion wireless sensor network (SDFWSN), a model that reflects the randomness of environmental monitoring and uses data fusion techniques widely used in… More >

  • Open Access

    ARTICLE

    Multi-Objective Hybrid Sailfish Optimization Algorithm for Planetary Gearbox and Mechanical Engineering Design Optimization Problems

    Miloš Sedak*, Maja Rosić, Božidar Rosić

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 2111-2145, 2025, DOI:10.32604/cmes.2025.059319 - 27 January 2025

    Abstract This paper introduces a hybrid multi-objective optimization algorithm, designated HMODESFO, which amalgamates the exploratory prowess of Differential Evolution (DE) with the rapid convergence attributes of the Sailfish Optimization (SFO) algorithm. The primary objective is to address multi-objective optimization challenges within mechanical engineering, with a specific emphasis on planetary gearbox optimization. The algorithm is equipped with the ability to dynamically select the optimal mutation operator, contingent upon an adaptive normalized population spacing parameter. The efficacy of HMODESFO has been substantiated through rigorous validation against established industry benchmarks, including a suite of Zitzler-Deb-Thiele (ZDT) and Zeb-Thiele-Laumanns-Zitzler (DTLZ) More >

  • Open Access

    ARTICLE

    Evolutionary Particle Swarm Optimization Algorithm Based on Collective Prediction for Deployment of Base Stations

    Jiaying Shen1, Donglin Zhu1, Yujia Liu2, Leyi Wang1, Jialing Hu1, Zhaolong Ouyang1, Changjun Zhou1, Taiyong Li3,*

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 345-369, 2025, DOI:10.32604/cmc.2024.060335 - 03 January 2025

    Abstract The wireless signals emitted by base stations serve as a vital link connecting people in today’s society and have been occupying an increasingly important role in real life. The development of the Internet of Things (IoT) relies on the support of base stations, which provide a solid foundation for achieving a more intelligent way of living. In a specific area, achieving higher signal coverage with fewer base stations has become an urgent problem. Therefore, this article focuses on the effective coverage area of base station signals and proposes a novel Evolutionary Particle Swarm Optimization (EPSO)… More >

  • Open Access

    ARTICLE

    DDoS Attack Autonomous Detection Model Based on Multi-Strategy Integrate Zebra Optimization Algorithm

    Chunhui Li1,2, Xiaoying Wang1,2,*, Qingjie Zhang1,2, Jiaye Liang1, Aijing Zhang1

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 645-674, 2025, DOI:10.32604/cmc.2024.058081 - 03 January 2025

    Abstract Previous studies have shown that deep learning is very effective in detecting known attacks. However, when facing unknown attacks, models such as Deep Neural Networks (DNN) combined with Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) combined with LSTM, and so on are built by simple stacking, which has the problems of feature loss, low efficiency, and low accuracy. Therefore, this paper proposes an autonomous detection model for Distributed Denial of Service attacks, Multi-Scale Convolutional Neural Network-Bidirectional Gated Recurrent Units-Single Headed Attention (MSCNN-BiGRU-SHA), which is based on a Multi-strategy Integrated Zebra Optimization Algorithm (MI-ZOA). The… More >

  • Open Access

    ARTICLE

    A Multi-Objective Particle Swarm Optimization Algorithm Based on Decomposition and Multi-Selection Strategy

    Li Ma1, Cai Dai1,*, Xingsi Xue2, Cheng Peng3

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 997-1026, 2025, DOI:10.32604/cmc.2024.057168 - 03 January 2025

    Abstract The multi-objective particle swarm optimization algorithm (MOPSO) is widely used to solve multi-objective optimization problems. In the article, a multi-objective particle swarm optimization algorithm based on decomposition and multi-selection strategy is proposed to improve the search efficiency. First, two update strategies based on decomposition are used to update the evolving population and external archive, respectively. Second, a multi-selection strategy is designed. The first strategy is for the subspace without a non-dominated solution. Among the neighbor particles, the particle with the smallest penalty-based boundary intersection value is selected as the global optimal solution and the particle… More >

  • Open Access

    ARTICLE

    Joint Estimation of SOH and RUL for Lithium-Ion Batteries Based on Improved Twin Support Vector Machineh

    Liyao Yang1, Hongyan Ma1,2,3,*, Yingda Zhang1, Wei He1

    Energy Engineering, Vol.122, No.1, pp. 243-264, 2025, DOI:10.32604/ee.2024.057500 - 27 December 2024

    Abstract Accurately estimating the State of Health (SOH) and Remaining Useful Life (RUL) of lithium-ion batteries (LIBs) is crucial for the continuous and stable operation of battery management systems. However, due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance, direct measurement of SOH and RUL is challenging. To address these issues, the Twin Support Vector Machine (TWSVM) method is proposed to predict SOH and RUL. Initially, the constant current charging time of the lithium battery is extracted as a health indicator (HI), decomposed using Variational Modal Decomposition (VMD), and… More >

  • Open Access

    ARTICLE

    Grid-Connected/Islanded Switching Control Strategy for Photovoltaic Storage Hybrid Inverters Based on Modified Chimpanzee Optimization Algorithm

    Chao Zhou1, Narisu Wang1, Fuyin Ni1,2,*, Wenchao Zhang1

    Energy Engineering, Vol.122, No.1, pp. 265-284, 2025, DOI:10.32604/ee.2024.057380 - 27 December 2024

    Abstract Uneven power distribution, transient voltage, and frequency deviations are observed in the photovoltaic storage hybrid inverter during the switching between grid-connected and island modes. In response to these issues, this paper proposes a grid-connected/island switching control strategy for photovoltaic storage hybrid inverters based on the modified chimpanzee optimization algorithm. The proposed strategy incorporates coupling compensation and power differentiation elements based on the traditional droop control. Then, it combines the angular frequency and voltage amplitude adjustments provided by the phase-locked loop-free pre-synchronization control strategy. Precise pre-synchronization is achieved by regulating the virtual current to zero and… More >

  • Open Access

    ARTICLE

    Hybrid Metaheuristic Lion and Firefly Optimization Algorithm with Chaotic Map for Substitution S-Box Design

    Arkan Kh Shakr Sabonchi*

    Journal of Information Hiding and Privacy Protection, Vol.6, pp. 21-45, 2024, DOI:10.32604/jihpp.2024.058954 - 31 December 2024

    Abstract Substitution boxes (S-boxes) are key components of symmetrical cryptosystems, acting as nonlinear substitution functions that hide the relationship between the encrypted text and input key. This confusion mechanism is vital for cryptographic security because it prevents attackers from intercepting the secret key by analyzing the encrypted text. Therefore, the S-box design is essential for the robustness of cryptographic systems, especially for the data encryption standard (DES) and advanced encryption standard (AES). This study focuses on the application of the firefly algorithm (FA) and metaheuristic lion optimization algorithm (LOA), thereby proposing a hybrid approach called the… More >

  • Open Access

    ARTICLE

    RSSI-Based 3D Wireless Sensor Node Localization Using Hybrid T Cell Immune and Lotus Optimization

    Weiwei Hu1, Kiran Sree Pokkuluri2, Rajesh Arunachalam3,*, Bander A. Jabr4, Yasser A. Ali4, Preethi Palanisamy5

    CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4833-4851, 2024, DOI:10.32604/cmc.2024.055561 - 19 December 2024

    Abstract Wireless Sensor Network (WSNs) consists of a group of nodes that analyze the information from surrounding regions. The sensor nodes are responsible for accumulating and exchanging information. Generally, node localization is the process of identifying the target node’s location. In this research work, a Received Signal Strength Indicator (RSSI)-based optimal node localization approach is proposed to solve the complexities in the conventional node localization models. Initially, the RSSI value is identified using the Deep Neural Network (DNN). The RSSI is conceded as the range-based method and it does not require special hardware for the node… More >

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