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7th International Conference on Wireless Communications, Networking and Applications, WCNA 2023

作者： Chen, Tingting Zhang, Zhenya Wang, Ping Cheng, Hongmei Anhui Province Key Laboratory of Intelligent Building and Building Energy Saving Anhui Jianzhu University Hefei China School of Electronics and Information Engineering Anhui Jianzhu University Hefei China

ISBN: (纸本)9789819624089

The Insect Intelligent Building (I2B) platform is a distributed information processing system based on a microenvironment network. To solve optimization problems in microenvironments using the Particle Swarm optimization (PSO) algorithm, this paper designs the information exchange strategy of the PSO algorithm in microenvironments. It optimizes the particle velocity and position updating mechanism to propose the Microenvironment-based Particle Swarm optimization (MPSO) algorithm. In the microenvironment network, the information exchange strategy is used for collaborative computation among microenvironments, which can simultaneously solve a given optimization problem, thus generating multiple candidate optimal solutions and finally determining the optimal solution. The experimental results indicate that the MPSO algorithm proposed in this paper can solve the optimization problem more efficiently. © The Author(s) 2025.

关键词： optimization algorithms

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Study on pyrolysis characteristics of typical thermal insulation materials under light aging

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ENERGY 2025年 320卷

作者： Zhang, Wenlong Pan, Rongkun Wang, Jian Lu, Chang Liu, Miling Ding, Yanming Henan Polytech Univ Sch Safety Sci & Engn Jiaozuo 454003 Peoples R China China Univ Geosci Fac Engn Wuhan 430074 Peoples R China Henan Polytech Univ Collaborat Innovat Ctr Coal Work Safety & Clean Hi Jiaozuo 454003 Peoples R China Henan Polytech Univ Dept Nephrol Affiliated Hosp 1 Jiaozuo 454002 Peoples R China

Due to the shedding of building facades, thermal insulation materials (TIMs) are usually aged by sunlight, leading to changes in physical and chemical performance. However, there are few studies on the fire hazards of TIMs under light aging. Pyrolysis is the first process of fire occurrence, and the pyrolysis gas provides gaseous fuel for gas-phase combustion. Therefore, pyrolysis characteristics of TIMs under light aging were studied in this study. Extruded polystyrene (XPS) was aged up to 180 days by accelerated aging experiments, and its pyrolysis characteristics were studied using thermogravimetric analysis. The kinetic parameters were estimated by the Coats-Redfern method and optimized using multiple optimization algorithms. Subsequently, the Fourier transform infrared spectrometric technique was applied to analyze the pyrolysis products. The results showed that the chalking appeared and the structure of XPS was destroyed with light aging. Pyrolysis mechanisms changed and the kinetic parameters decreased. Pyrolysis was easier to occur and the reaction required less energy, indicating an increase in its fire hazards. New functional groups were generated, and the intensity of vibration bands was strengthened. This study contributes to understanding of fire risk of aging TIMs and taking appropriate measures to prevent material aging, thereby reducing fire accidents.

关键词： Thermal insulation materials Pyrolysis Light aging Kinetic parameters optimization algorithms

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A Chaotic Elite Cloning Artificial Jellyfish Algorithm for Efficient Task Allocation in IOTWSNs

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IEEE SENSORS JOURNAL 2025年第4期25卷 6905-6919页

作者： Wen, Dikun Cao, Qike Feng, Shourui Zhang, Zhehao Zhou, Peng Henan Univ Sch Software Kaifeng 475000 Peoples R China Henan Inst Sci & Technol Xinxiang 453003 Peoples R China Sun Yat Sen Univ Sch Life Sci Guangzhou 510275 Peoples R China Henan Univ Econ & Law Sch Stat & Big Data Zhengzhou 450000 Peoples R China Xinjiang Coll Sci & Technol Sch Informat Sci & Engn Urumqi 841000 Xinjiang Peoples R China Yanshan Univ Sch Informat Sci & Engn Qinhuangdao 066004 Peoples R China

The Internet of Things wireless sensor networks (IOTWSNs) are crucial in modern smart systems, where self-organizing sensor nodes enable efficient and flexible network structures for applications like environmental monitoring and smart cities. The task allocation problem in IOTWSNs is NP-hard, making effective strategies essential for optimal network performance. This article proposes an improved artificial jellyfish search algorithm (CECJS) that integrates chaotic initialization, elite, and cloning strategies to enhance global search ability and convergence speed. To evaluate CECJS's efficiency, the article introduces network gain, reflecting both network effectiveness and task completion quality. Experimental results show that CECJS significantly outperforms traditional algorithms like genetic algorithm (GA), simulated annealing (SA), and particle swarm optimization (PSO) in task allocation gains, achieving improvements of several to tens of percentage points. In addition, CECJS exhibits faster convergence, finding near-optimal solutions more efficiently, making it an effective solution for large-scale IOTWSNs task optimization.

关键词： Resource management Sensors Intelligent sensors Cloning optimization Heuristic algorithms Wireless sensor networks Jellyfish Convergence Medical services IoT wireless sensor networks jellyfish search algorithm optimization algorithms performance evaluation task allocation

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Recent advances in Multi-objective Cuckoo Search Algorithm, its variants and applications

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ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING 2025年 1-28页

作者： Makhadmeh, Sharif Naser Awadallah, Mohammed A. Kassaymeh, Sofian Al-Betar, Mohammed Azmi Sanjalawe, Yousef Kouka, Shaimaa Al-Redhaei, Anessa Univ Jordan UJ King Abdullah II School Informat Technol Dept Informat Technol Amman 11942 Jordan Ajman Univ Coll Engn & Informat Technol Artificial Intelligence Res Ctr AIRC Ajman U Arab Emirates Al Aqsa Univ Dept Comp Sci POB 4051 Gaza 4051 Palestine Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res POB 19328 Amman Jordan Aqaba Univ Technol Fac Informat Technol Software Engn Dept Aqaba Jordan

The Cuckoo Search Algorithm (CSA) is an optimization algorithm inspired by the brood parasitism behavior of cuckoo birds. It mimics the reproductive and breeding tactics of cuckoos to tackle optimization challenges. To better handle multi-objective optimization problems (MOPs), a variation called the multi-objective CSA (MOCSA) has been developed. MOCSA is designed to uncover a spectrum of solutions, each providing a balance between various objectives, thereby allowing decision-makers to choose the optimal solution according to their specific preferences. The literature has witnessed a notable increase in the number of published MOCSAs, with MOCSA research papers recorded in the SCOPUS database. This paper presents a comprehensive survey of 123 distinct variants of MOCSAs published in scientific journals. Through this survey, researchers will gain insights into the growth of MOCSA, the theoretical foundations of multi-objective optimization and the MOCSA algorithm, the various existing MOCSA variants documented in the literature, the application domains in which MOCSA has been employed, and a critical analysis of its performance. In sum, this paper provides future research directions for MOCSA. Overall, this survey provides a valuable resource for researchers seeking to explore and understand the advancements, applications, and potential future developments in the field of multi-objective CSA.

关键词： optimization algorithms

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Flight tracking control of mini-drone: Flower pollination algorithm based model-free control

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2025年第6期239卷 578-592页

作者： Glida, Hossam Eddine Abdelghani, Chelihi Chouki, Sentouh Univ Caen Normandie LIS UR 7478 UNICAEN ENSICAEN F-14000 Caen France Univ Biskra Dept Elect Engn Lab LI3CUB Biskra Algeria Constantine 1 Univ Fac Technol Dept Elect Constantine Algeria Univ Polytech Hauts Defrance UMR 8201 CNRS LAMIH Valenciennes France INSA Hauts Defrance Valenciennes France

This paper presents a novel model-free control approach, Flower Pollination Algorithm-based Model-Free Control (FPA-MFC), for trajectory tracking of mini-drone quadrotor unmanned aerial vehicles (UAVs). The proposed approach employs an adaptive estimator based on filtered signals to approximate the nonlinear dynamic functions of the system. This approximator allows the development of a robust decentralized control law able to separately manage the position and attitude dynamics of the drone. The controller design is free of any prior knowledge of the system dynamics, and the control inputs are computed solely from instantaneous input and output measurements. Indeed, this can significantly reduce the computational burden and improve the efficiency of the control algorithm while preserving its simplicity. The design gains of the control law are selected using the metaheuristic flower pollination algorithm to achieve greater trajectory tracking performance and ensure closed-loop system stability. Simulation tests conducted on the Parrot mini drone platform validate the effectiveness and superior performance of FPA-MFC, compared to similar controllers without optimization and using the particle swarm optimization algorithm.

关键词： Mini-drone model-free control optimization algorithms flower pollination algorithm

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Enhanced prediction of discharge coefficient in sharp-edged width constrictions using a novel hybrid SVR-IWOA and machine learning models

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JOURNAL OF HYDROLOGY 2025年 657卷

作者： Afaridegan, Ehsan Fatahi-Alkouhi, Reza Azarm, Paymaneh Amanian, Nosratollah Yazd Univ Fac Engn Dept Civil Engn Yazd *** Iran Univ Isfahan Dept Civil Engn & Transportat PhD Water Resource Engn & Management Esfahan *** Iran Norwegian Univ Sci & Technol NTNU Dept Civil & Environm Engn Trondheim Norway

Sharp-Edged Width Constrictions (SEWC) are hydraulic structures designed to measure flow in open channels. Accurate prediction of the discharge coefficient (Cd) in SEWC is crucial for determining water discharge in these channels. This information plays a key role in effective water resource management, supporting decision-making regarding the allocation and conservation of water for agricultural, industrial, and municipal purposes. This study introduces a novel hybrid machine learning model, combining Support Vector Regression (SVR) with the Improved Whale optimization Algorithm (IWOA). Additionally, advanced machine learning models such as NGBoost, AutoInt, and TabNet were employed to predict Cd in SEWC. The SVR-IWOA model offers automatic hyperparameter tuning, significantly enhancing prediction accuracy in complex flow conditions. To develop these models, a dataset consisting of 156 laboratory data points from SEWC experiments was utilized, with 75 % of the data allocated for training and 25 % for testing. The Isolation Forest (IF) algorithm was applied to detect and remove outliers, leading to the exclusion of 5.1 % of the original dataset. Dimensional analysis identified critical factors influencing Cd, including the ratio of upstream depth to opening width (h/b) and the constriction ratio (beta = b/B, where B is the channel width). The validity of these dimensionless parameters was confirmed using ANOVA and SHAP analyses, which highlighted beta as the most influential factor affecting Cd. Model performance was rigorously evaluated using multiple metrics, including the coefficient of determination (R2), Root Mean Squared Error (RMSE), Scatter Index (SI), Weighted Mean Absolute Percentage Error (WMAPE), and symmetric Mean Absolute Percentage Error (sMAPE). Comparative evaluations were conducted using Taylor Diagrams, Residual Error Curves (REC), and the Performance Index (PI). In the training stage, NGBoost demonstrated superior performance with a PI of 4994 an

关键词： Discharge coefficient Flow measurement Sharp-edged width constrictions Hybrid machine learning models optimization algorithms

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Identification of optimal parameters of PEMFC steady-state model using improved black kite algorithm

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2025年 106卷 1302-1321页

作者： Huang, Shiyu Xing, Yanfeng Cao, Juyong Zhang, Xiaobing Wang, Ying Shanghai Univ Engn Sci Sch Mech & Automot Engn Shanghai 201620 Peoples R China

Proton exchange membrane fuel cells (PEMFCs) are energy conversion devices that utilize renewable hydrogen energy. The reaction process in PEMFCs generates water and releases substantial heat, achieving high energy density while effectively reducing environmental pollution, making PEMFCs a focal point of research. However, the complexity of internal reactions and the multitude of parameters in PEMFC mathematical models result in nonlinear output characteristics, posing challenges to the accuracy and efficiency of identifying unknown parameters in these models. To address these challenges, this study proposes an Improved Black Kite Algorithm (IBKA). Using a 30 kW PEMFC stack testing platform, four datasets under different operating conditions were collected, and a static model suitable for high-power PEMFCs was established. To validate the effectiveness of IBKA, performance tests were conducted on benchmark functions, and the algorithm was applied to identify seven unknown parameters in both high-power and conventional static PEMFC models. The objective function for parameter identification was defined as the sum of squared errors between experimental and model outputs. Additionally, datasets from two commercial fuel cell stacks with different parameter specifications (NedStack PS6 and BCS500W) were used to compare the parameter identification results obtained from various algorithms with those of IBKA. The performance tests and model parameter identification results demonstrate that IBKA excels in accuracy, convergence speed, adaptability, and robust stability. For the high-power PEMFC static model, the combination of IBKA and the model achieved a mean squared error (MSE) below 0.15 between model and experimental outputs, enabling accurate predictions of high-power PEMFC outputs under various operating conditions and parameter specifications. For conventional PEMFCs, the objective function results based on the NedStack PS6 dataset were 1.2558, and 0.0119 for the BCS

关键词： Parameter identification IBKA Steady-state model optimization algorithms Proton exchange membrane fuel cell (PEMFC)

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Integrating Multilayer Perceptron and Support Vector Regression for Enhanced State of Health Estimation in Lithium-Ion Batteries

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IEEE ACCESS 2025年 13卷 11463-11478页

作者： Jafari, Sadiqa Kim, Jisoo Choi, Wonil Byun, Yung-Cheol Jeju Natl Univ Inst Informat Sci & Technol Dept Elect Engn Jeju Si 63243 South Korea Jeju Natl Univ Fac Software Artificial Intelligence major Dept Comp Engn Coll Engn Jeju Si 63243 South Korea Nanoom Energy Co Ltd Jeju Si 63309 Jeju Do South Korea Jeju Natl Univ Inst Informat Sci & Technol Dept Comp Engn Major Elect Engn Jeju Si 63243 South Korea

Accurately evaluating the State of Health (SOH) of batteries is crucial for guaranteeing the secure and dependable functioning of Electric Vehicles (EVs). This paper presents a novel strategy for tackling the difficulties associated with intricate preprocessing and the demand for extensive data in conventional approaches to SOH measurement. Using sophisticated machine learning algorithms, we suggest an all-encompassing methodology for predicting the SOH. Our approach includes meticulous data preparation, which includes analyzing crucial operating elements such as voltage, current, and temperature. We utilized Support Vector Regression (SVR) and Multilayer Perceptron (MLP) models, which were fine-tuned using hyperparameter optimization. The models were assessed using evaluation metrics such as Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and R-squared R-2 . In order to improve the accuracy of our predictions, we combined these models into a stacked ensemble using a Random Forest (RF) meta-model. This resulted in an $R<^>{2}$ value of 0.987, MAE of 0.02559, MSE of 0.0013, and RMSE of 0.00624. The results indicate that the ensemble outperforms individual models in predicting SOH. This research highlights the capacity of ensemble learning in predictive maintenance and battery management.

关键词： Lithium-ion battery SOH optimization algorithms optimization algorithms ensemble learning ensemble learning machine learning machine learning battery performance battery performance battery performance

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Mathematical Formulation of Intelligent Management algorithms for Isolated Microgrids: A Pareto-Based Critical Approach

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ENERGIES 2025年第6期18卷 1487-1487页

作者： Batista, Vitor dos Santos Soares, Thiago Mota Tostes, Maria Emilia de Lima Bezerra, Ubiratan Holanda Lott, Hugo Goncalves Fed Univ Para Ctr Excellence Energy Efficiency Amazon CEAMAZON BR-66075110 Belem PA Brazil Fed Univ Para Inst Technol Elect Engn Fac BR-66075110 Belem PA Brazil Norte Energia SA BR-70390025 Brasilia DF Brazil

This study proposes a simplified mathematical formulation for optimizing isolated microgrids, enhancing computational efficiency while preserving solution quality. The research focuses on the influence of Operation and Maintenance (O&M) costs for Non-Dispatchable Generators (NDGs) and the relationship between costs and pollutant emissions. The proposed simplification reduces computational requirements, improves result interpretability, and increases the scalability of optimization techniques. The O&M costs of photovoltaic and wind systems were excluded from the initial optimization and calculated afterward. A Student's t-test yielded a p-value of 87.3%, confirming no significant difference between the tested scenarios, ensuring that the simplification does not impact solution quality while reducing computational complexity. For emission-related costs, scenarios with single and multiple pollutant generators were analyzed. When only one generator type is present, modifications are needed to enable effective multi-objective optimization. To address this, two alternative mathematical formulations were tested, offering more suitable approaches for the problem. However, when multiple pollutant sources exist, cost and emission differences naturally define the problem as multi-objective without requiring adjustments. Future work will explore grid-connected microgrids and additional optimization objectives, such as loss minimization, voltage control, and device lifespan extension.

关键词： isolated microgrids optimization algorithms operation and maintenance (O&M) costs pollutant emissions energy management

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Comprehensive Fault Location on Transmission Lines Considering Variation in Line Parameters and Saturation in Current Transformers

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IEEE ACCESS 2025年 13卷 734-769页

作者： Huynh, Duy C. Ho, Loc D. HUTECH Univ HUTECH Inst Engn Ho Chi Minh City 700000 Vietnam

Precise and rapid fault location on transmission lines is a cornerstone for enhancing the reliability and cost-efficiency of power systems. This paper proposes an innovative approach to a fault location by solving an optimization problem. The process relies on transmission line parameters, which can vary significantly under various operating conditions. Inaccurate parameters can severely affect fault location results. To address this issue, the paper proposes a parameter estimation process. Additionally, when faults occur near transmission line terminals, high fault currents can cause current transformer (CT) saturation and secondary current distortion, further impacting fault location accuracy. Instead of using currents distorted by CT saturation, this paper proposes to estimate the currents for inputting into the fault location procedure. To achieve accurate parameter and current estimation at both terminals of the transmission line, advanced artificial bee colony (ABC) algorithms such as Chaos ABC algorithm and Chaos particle swarm optimization (PSO)-ABC algorithm are employed. These are enhanced variants designed to improve exploitation ability, leading to better convergence values and reduced iteration requirements. The proposal is applied for locating three-line (L-L-L), three-line to-ground (L-L-L-G), two-line (L-L), two-line to-ground (L-L-G), and line-to-ground (L-G) faults on the transmission line with various locations. More specifically, the transmission line parameter variations and the CT saturation are considered to enhance the practicality of the fault location problem. Numerical results and comparisons confirm the proposal's effectiveness, being better than the previous traditional fault location technique such as the impedance-based (IB) technique, as well as other meta-heuristic algorithms such as a grey wolf optimization (GWO) algorithm, a gravitational search (GS) algorithm, and an inclined planes optimization (IPO) algorithm. The error percenta

关键词： Fault location Power transmission lines Transmission line measurements Accuracy Fault currents Costs Circuit faults optimization Current transformers Current measurement Current transformer saturation fault location optimization algorithms parameter estimation transmission line

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