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Novel formulations and metaheuristic algorithms for predictive maintenance of aircraft engines with remaining useful life prediction

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2025年 261卷

作者： Wang, Lubing Zhao, Xufeng Pham, Hoang Nanjing Univ Aeronaut & Astronaut Coll Econ & Management Nanjing 211106 Peoples R China Rutgers State Univ Dept Ind & Syst Engn POB 909 Piscataway NJ 08855 USA

Advanced sensor technology has driven the remaining useful life (RUL) prediction of aircraft engines. However, only a few studies have considered incorporating RUL prediction results into maintenance plans. To address this problem, this paper investigates a novel predictive maintenance framework for aircraft engines. First, a hybrid deep learning model is developed to predict the aircraft engine RUL. Based on the predicted RUL, two new mixed integer linear programming models are developed to deal with the predictive maintenance problem of aircraft engines, which targets to minimize the maximum maintenance completion time for all aircraft engines. Since commercial solvers (e.g. CPLEX) solving it is time-consuming as the problem scale increases, we develop a new fast and effective hybrid metaheuristic algorithm based on the problem features, which combines a genetic algorithm and a variable neighborhood search algorithm. Finally, numerical experiments from the NASA aircraft engine dataset validate the proposed predictive maintenance framework can provide the optimal predictive maintenance plan in less than 10 s for large-scale maintenance problems, thereby reducing aircraft maintenance completion time.

关键词： Aircraft engines Predictive maintenance Remaining useful life Deep learning metaheuristic algorithms

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Optimization of Extreme Learning Machine with metaheuristic algorithms for Modelling Water Quality Parameters of Tamburawa Water Treatment Plant in Nigeria

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WATER RESOURCES MANAGEMENT 2025年第3期39卷 1377-1401页

作者： Abba, Sani I. Pham, Quoc Bao Malik, Anurag Costache, Romulus Gaya, Muhammad Sani Abdullahi, Jazuli Mati, Sagiru Usman, A. G. Saini, Gaurav Prince Mohammad Bin Fahd Univ Dept Civil Engn Al Khobar 31952 Saudi Arabia Univ Silesia Katowice Inst Earth Sci Fac Nat Sci Bedzinska St 60 PL-41200 Sosnowiec Poland Punjab Agr Univ Reg Res Stn Bathinda 151001 Punjab India Transilvania Univ Brasov Dept Civil Engn 5 Turnului St Brasov 500152 Romania Kano Univ Sci & Technol Dept Elect Engn Wudil Nigeria Baze Univ Ctr Clean Energy & Climate Change Abuja Nigeria Near East Univ Operat Res Ctr Healthcare Nicosia Cyprus Netaji Subhas Univ Technol Dept Civil Engn West Campus Delhi India

Sustainable management of available water resources needs robust and reliable intelligent tools to address emerging water challenges. These days, artificial intelligence (AI) based tools are more efficient and prominent in addressing issues related to water treatment plants. Therefore, in the current study, the extreme learning machine (ELM) was optimized with four different metaheuristic algorithms, namely particle swarm optimization (PSO-ELM), genetic algorithm (GA-ELM), biogeography-based optimization (BBO-ELM), and BBO-PSO-ELM for modelling treated water quality parameters, i.e., pHT, Turbidity (TurbT), total dissolved solids (TDST), and HardnessT of Tamburawa water treatment plant (TWTP) located in Nigeria. The performance of the hybrid ELM models was evaluated using mean absolute error (MAE), root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), Pearson correlation coefficient (PCC), and Willmott index (WI) as well as graphically. The obtained numerical and visualized results indicate that the BBO-PSO-ELM model performed superior in modeling pHT (MAE = 0.403, RMSE = 0.514, NSE = 0.863, PCC = 0.935, WI = 0.964), TDST (MAE = 11.818 mg/L, RMSE = 16.058 mg/L, NSE = 0.711, PCC = 0.853, WI = 0.923), and HardnessT (MAE = 2.624 mg/L, RMSE = 3.497 mg/L, NSE = 0.818, PCC = 0.909, WI = 0.947), while BBO-ELM demonstrated superior performance in TurbT (MAE = 0.385 mg/L, RMSE = 0.694 mg/L, NSE = 0.996, PCC = 0.999, WI = 0.999) modelling. Generally, the findings suggested that the proposed hybrid ELM model has the potential to predict the water quality parameters of TWTP in Nigeria effectively.

关键词： Water quality parameters ELM metaheuristic algorithms TWTP Nigeria

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ESMR4FBP: A pLM-based regression prediction model for specific properties of food-derived peptides optimized multiple bionic metaheuristic algorithms

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FOOD CHEMISTRY 2025年第Pt 3期464卷 141840页

作者： Zhang, Ruihao Li, Yonghui Jiang, Qinbo Li, Yang Cai, Zhe Zhang, Hui Zhejiang Univ Coll Biosyst Engn & Food Sci Hangzhou 310058 Peoples R China Zhejiang Univ Innovat Ctr Yangtze River Delta Future Food Lab Jiaxing 314100 Peoples R China Kansas State Univ Dept Grain Sci & Ind Manhattan KS 66506 USA

Due to the growing emphasis on food safety, peptide research is increasingly focusing on food sources. Traditional methods for determining peptide properties are expensive. While artificial intelligence (AI) models can reduce cost, existing peptide models often lack accuracy. This study aimed to develop a regression model capable of predicting peptide properties. We integrated the ESM-2 model with the LSTM architecture and optimized the model structure using three metaheuristic algorithms, including WOA, SSA, and HHO. Using an antioxidant tripeptide dataset, our model achieved an R2 of 0.9458 and RMSE of 0.3135, outperforming the state-of-the-art (SOTA) model by 11.66 % and 50.00 %, respectively. The developed model was further applied to the bitter peptide dataset, resulting in R2 of 0.8385 and RMSE of 0.4414, respectively. These results suggest that our model has the potential to accurately predict the properties of various types of peptides.

关键词： Food-derived peptides metaheuristic algorithms Artificial intelligence Protein language model Regression model

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Breast cancer prediction with feature-selected XGB classifier, optimized by metaheuristic algorithms

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JOURNAL OF BIG DATA 2025年第1期12卷 1-26页

作者： Sarker, Proshenjit Ksibi, Amel Jamjoom, Mona M. Choi, Kwonhue Nahid, Abdullah Al Samad, Md Abdus Khulna Univ ECE Discipline Khulna 9208 Bangladesh Princess Nourah bint Abdulrahman Univ Coll Comp & Informat Sci Informat Syst Dept POB 84428 Riyadh 11671 Saudi Arabia Princess Nourah bint Abdulrahman Univ Coll Comp & Informat Sci Dept Comp Sci POB 84428 Riyadh 11671 Saudi Arabia Yeungnam Univ Dept Informat & Commun Engn Gyongsan 38541 South Korea

Breast cancer, caused by uncontrolled cell growth in milk ducts or lobules, has the highest mortality rate among women worldwide, with Asia reporting the most deaths. Early detection improves survival rates and reduces treatment costs. This study aims to develop a feature selection-based classifier to enhance breast cancer prediction, using a minimal dataset to maximize performance. Earlier works on the Breast Cancer Coimbra dataset used many features but failed to achieve high accuracy or explain misclassifications. We addressed this by reducing the features while maintaining performance. A Wrapper Model with metaheuristic algorithms: Whale Optimization, Bald Eagle Search, and Sea Lion Optimization and Extreme Gradient Boost Classifier. SHAP explained feature importance for both overall and individual predictions. Our study achieved F-scores of 97.43%, 95%, and 94.74% for SLOA_XGB, BESA_XGB, and WOA_XGB, respectively, on the Breast Cancer Coimbra dataset. Each method reduced the features from 9 to 4. SHAP analysis identified Glucose as having the highest impact on model predictions. Additionally, we found a link between the mean values of certain features and misclassification likelihood. This study analyzed data from 116 subjects, with the SLOA_XGB classifier achieving the best performance: 97.43% F-score, 97.14% accuracy, 97.14% precision, and 100% recall using only Glucose, Age, Resistin, and Adiponectin. These results highlight the potential for early breast cancer detection with fewer features while maintaining high predictive accuracy.

关键词： Uncontrolled Breast cancer Wrapper model metaheuristic algorithms Whale optimization Bald eagle search Sea lion optimization Extreme gradient boost SHAP

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An Integrated Model for Developing a Global Logistics Network Using metaheuristic algorithms

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2025年第2期32卷 433-444页

作者： Karimi Mehrabadi, Reza Roghanian, Emad Piroozfar, Shahnaz Shojaie, Abbas Islamic Azad Univ South Tehran Branch Dept Ind Engn Tehran Iran KN Toosi Univ Technol Dept Ind Engn Tehran Iran

The globalization of economies has heightened the importance of efficient logistics networks, particularly in the post-COVID-19 era where supply chain resilience is paramount. This study focuses on designing logistics hub networks to enhance global shipment flow management. A hub-based structure is adopted, aligned with existing global logistics frameworks. The research addresses key challenges such as optimizing warehouse and hub locations to streamline cargo delivery. An integrated model is developed for route planning, incorporating multi-modal transportation and cost-time trade-offs. The study employs metaheuristic algorithms including Strength Pareto Evolutionary Algorithm (SPEA-II), Non-Dominated Sorting Genetic Algorithm II (NSGA-II), and Multi-Objective Grey Wolf Optimization Algorithm to solve the proposed model. Detailed numerical analyses demonstrate the model's capability to generate optimal global solutions, underscoring its practical utility in real-world logistics management.

关键词： global logistics location allocation logistics hubs metaheuristic algorithms

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Performance analysis and optimization of photovoltaic-thermal system with direct contact membrane distillation using metaheuristic algorithms

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SEPARATION AND PURIFICATION TECHNOLOGY 2025年 364卷

作者： Maqbool, Faisal Kumar, Laveet Soomro, Mujeeb Iqbal Harijan, Khanji Mehran Univ Engn & Technol Dept Mech Engn Jamshoro 76062 Pakistan Qatar Univ Coll Engn Dept Mech & Ind Engn Doha Qatar Mehran Univ Engn & Technol Dept Mech Engn SZAB Campus Khairpur Mirs 66020 Sindh Pakistan

Photovoltaic-Thermal (PV/T) systems integrated with Direct Contact Membrane Distillation (DCMD) offer a promising solution for sustainable water desalination by simultaneously utilizing solar energy for electricity generation and thermal applications. This study presents a dynamic optimization framework to enhance the seasonal performance of PV/T-DCMD systems under variable climatic conditions. The objective is to maximize electrical efficiency, permeate flux, and outlet temperature by considering the effects of flow rates (1-10 L/min), solar irradiation (10-1000 W/m2), and ambient temperatures (291-324 K). metaheuristic algorithms, including Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), are employed to determine the optimal operating parameters. The results reveal that PSO achieved an optimal outlet temperature of 329.45 K and permeate flux of 19.89 kg/m2 & sdot;h at a flow rate of 1.0 L/min, solar irradiation of 1000 W/m2, and ambient temperature of 324 K. In comparison, GA yielded a slightly lower outlet temperature of 329.01 K and permeate flux of 19.80 kg/ m2 & sdot;h under the same conditions. Electrical efficiency stabilized at approximately 0.13 for both optimization techniques. These findings highlight the advantages of reduced flow rates and elevated solar irradiation in optimizing the system's thermal and electrical performance. This comprehensive analysis not only demonstrates the operational characteristics of PV/T-DCMD systems but also establishes a foundation for future advancements in hybrid optimization techniques and material innovations to further enhance system performance under dynamic weather conditions.

关键词： Optimization Direct contact membrane distillation Photovoltaic thermal collectors metaheuristic algorithms

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Data-based modeling for prediction of supercapacitor capacity: Integrated machine learning and metaheuristic algorithms

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JOURNAL OF THE TAIWAN INSTITUTE OF CHEMICAL ENGINEERS 2025年 170卷

作者： Azimi, Hamed Kalhor, Ebrahim Ghorbani Nabavi, Seyed Reza Behbahani, Mohammad Vardini, Mohammad Taghi Islamic Azad Univ Dept Chem Tabriz Branch Tabriz Iran Univ Mazandaran Fac Chem Dept Appl Chem Babolsar Iran Shahid Chamran Univ Ahvaz Fac Sci Dept Chem Ahvaz Iran

Background: Accurately predicting the specific capacity of supercapacitors (SCs) is essential for improving their energy efficiency and performance. This requires robust methods to model the complex, nonlinear relationships among variables. Methods: In this study, the dataset was divided into three optimal clusters using k-means, based on supercapacitor capacity, each displaying distinct features. Additionally, the unclustered dataset was also analyzed. The training of Multi-Layer Perceptron (MLP) neural networks was examined using six metaheuristic algorithms. Neural network hyperparameters were optimized via grid search, and metaheuristic algorithms via random search. Performance, convergence, and adaptability were evaluated for clustered and unclustered datasets, focusing on accuracy, speed, and generalization. Significant findings: The cluster-based MLP models demonstrated exceptional predictive accuracy, outperforming unclustered models. Notably, the MLP integrated with Invasive Weed Optimization (MLP-IWO) in cluster 2, with a population size (Np) of 40, achieved the highest coefficient of determination (R2=0.9998), representing a 105.53 % improvement compared to the best unclustered model (R2 = 0.4864). Similarly, the MLP integrated with the Firefly Algorithm (MLP-FA) in clusters 1 and 3 (Np = 30) achieved R2 values of 0.9983 and 0.9927, respectively. These findings highlight the effectiveness of integrating clustering with metaheuristic optimization for enhancing prediction accuracy in SCs capacity modeling.

关键词： Machine learning Supercapacitors Capacity prediction K -means clustering metaheuristic algorithms

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The improvement of the distributed computing efficiency in cloud-fog environments using data mining and metaheuristic algorithms

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JOURNAL OF SUPERCOMPUTING 2025年第4期81卷 1-55页

作者： Mabadifar, Tahmineh Attarzadeh, Iman Mahdipour, Ebrahim Islamic Azad Univ Fac Engn Dept Comp Engn Sci & Res Branch Tehran Iran Islamic Azad Univ Fac Engn Dept Comp Engn Cent Tehran Branch Tehran Iran

The Internet of Things highly depends on computing environments like cloud computing to process and store information. The use of cloud computing by smart devices leads to challenges such as delay and increased energy consumption of sensors. A primary solution to the mentioned problems is fog computing. Task scheduling is the most critical issue that significantly affects improving the performance of cloud-fog systems. Task scheduling is an NP-hard problem, and applying data mining methods and metaheuristic algorithms to obtain optimal solutions in a reasonable computing time is a fundamental requirement. This paper proposes a new model based on metaheuristic algorithms using the combination of golden jackal optimization (GJO) and beluga whale optimization (BWO) algorithm called GJOBWO to solve the task scheduling problem in a cloud-fog environment. In the hybrid model, the BWO algorithm is used to solve the issues of the GJO algorithm, such as getting stuck in the local optimum and imbalance between the exploration and exploitation stages. Performing the exploration and exploitation steps is essential because correct execution may lead to efficient solutions. Also, the k-means algorithm inspired by clustering is used to prioritize tasks. The evaluation of the hybrid model has been done using continuous optimization functions and task scheduling problems. First, the hybrid model has been implemented on 68 standard functions and compared with particle swarm optimization (PSO), sine cosine algorithm (SCA), whale optimization algorithm (WOA), grey wolf optimizer (GWO), ant lion optimizer (ALO), and GJO and BWO algorithms. Then, the hybrid model has been tested on the task scheduling problem and compared with WOA, GJO, and BWO algorithms. The results show that the hybrid model has effectively minimized the makespan rate and the degree of imbalance. Also, the average improvement percentage of the combined model based on the PIR criterion compared to the four algorithms S

关键词： Cloud computing Task scheduling metaheuristic algorithms GJO algorithm BWO algorithm

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Predicting backbreak due to blasting using LSSVM optimized by metaheuristic algorithms

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ENVIRONMENTAL EARTH SCIENCES 2025年第6期84卷 1-14页

作者： Shahani, Niaz Muhammad Zheng, Xigui China Univ Min & Technol Sch Mines Xuzhou 221116 Jiangsu Peoples R China Shanxi Guxian Jingu Coal Ind Co Ltd Linfen 041000 Shanxi Peoples R China Liupanshui Normal Univ Sch Mines & Civil Engn Liupanshui 553004 Peoples R China Guizhou Guineng Investment Co Ltd Liupanshui 553600 Peoples R China

Backbreak is an undesirable outcome in blasting operations caused by factors such as equipment failure, improper fragmentation, unstable mine walls, reduced drilling efficiency, and other issues that contribute to increased mining operation costs. To overcome these problems effectively, this study developed a least square support vector machine (LSSVM) model and optimized it using metaheuristic algorithms, including genetic algorithm (GA)-LSSVM, particle swarm optimization (PSO)-LSSVM, and grey wolf optimization (GWO)-LSSVM, to predict the efficiency and accuracy of backbreak due to blasting in surface mines using burden (m), spacing (m), stemming (m), powder factor (kg/ms), and stiffness ratio (m/m) as input parameters. Among the models evaluated, the GWO-LSSVM model demonstrated superior performance compared to the LSSVM, GA-LSSVM, and PSO-LSSVM models, achieving a coefficient of determination of 0.998 and 0.997, mean absolute error of 0.0068 and 0.1209, root mean squared error of 0.0825 and 0.1936, and SI of 0.021 and 0.044 on the training and testing datasets, respectively. Sensitivity analysis of the GWO-LSSVM model revealed that the powder factor exerted the most significant influence, while the burden had the least impact on backbreak. This developed method has proven to significantly enhance the performance evaluation of backbreak in surface mines and offers valuable engineering applications in mining and other related fields.

关键词： Backbreak Fracture mechanics metaheuristic algorithms GWO-LSSVM Surface mines

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Mixture copula parameter estimation with metaheuristic algorithms, comparative study under hydrological context

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2025年第4期39卷 1307-1326页

作者： Gontara, Emna Chebana, Fateh Inst Natl Rech Sci Ctr Eau Terre Environm 490 Rue Couronne Quebec City PQ G1K 9A9 Canada

Hydrological events are often described by several dependent characteristics, such as peak, volume, and duration for floods. They can be studied in a multivariate framework where copulas are a powerful tool to capture the dependence structure. A multivariate Hydrological frequency analysis is generally based on homogeneity, serial-independence, and stationarity. However, the homogeneity assumption is not often fulfilled due to several reasons, such as climate change, human activities, and the mixture nature of multiple generating processes, potentially affecting the copula selection. As a result, using a single copula (non-mixture) may not be appropriate, and a mixture of copulas is needed. A growing number of studies have recently been conducted on parameter estimation for mixture copulas. However, the hydrological literature on mixture copulas is still in its infancy. Furthermore, the developed estimation methods have numerous optimization-related drawbacks. Due to their successful application for several optimization tasks, we consider metaheuristic algorithms for mixture copulas' parameter estimation. A simulation study is performed to evaluate and compare the effectiveness of these algorithms under hydrological constraints. Simulation results show similar performance in terms of relative errors, whereas a considerable difference is denoted concerning the needed time to converge to optimal parameters of mixture copula models. Moreover, two applications to real-world datasets are provided.

关键词： Hydrologic frequency analysis Homogeneity Mixture copulas Parameter estimation metaheuristic algorithms Maximum pseudo-likelihood Monte Carlo simulation

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