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A modified memetic algorithm for the home health care scheduling and routing problem considering caregiver skill deviation and synchronous service

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COMPUTERS & INDUSTRIAL ENGINEERING 2025年 203卷

作者： Li, Yan Wang, Qiangqiang Wang, Heping Wang, Fuyu Zhao, Dan Anhui Univ Technol Sch Management Sci & Engn Maanshan 243000 Peoples R China Multiple Sci Management & Control Complex Syst Key Maanshan 243000 Peoples R China

Based on the real scenario that two caregivers are needed to serve an elderly patient simultaneously, the paper studies the home health care synchronous scheduling and routing problem. The value function of prospect theory is used to describe their perceived satisfaction from the perspective of caregivers' bounded rationality towards skill deviations. A mixed-integer programming model is proposed to maximize the caregivers' satisfaction and minimize the total operating cost. An improved multi-objective memetic algorithm (IMOMA) is designed to solve the problem. In the IMOMA, an improved push-forward insertion heuristic (IPFIH) algorithm is proposed to generate initial solutions. Two types of crossover operators, three types of mutation operators and four types of neighborhood search operators with the properties of the problem are designed to improve the performance of the IMOMA. Taguchi experiment is constructed to set the optimal parameters of the algorithm. Simulation experiments are conducted in cases of various scales. The results indicate that the IMOMA can efficiently solve the scheduling problem by comparing with the three algorithms. Finally, the sensitivity analysis is conducted on the key parameters of the scheduling model to explore their impact on the optimization objectives of the scheduling scheme.

关键词： Home health care Scheduling and routing Skill deviation Synchronous service An improved multi-objective memetic algorithm

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A Novel Proprietary Internet Video Traffic Dataset Generation algorithm

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APPLIED SCIENCES-BASEL 2025年第2期15卷 515-515页

作者： Chen, Tianhua Grabs, Elans Ipatovs, Aleksandrs Cano, Maria-Dolores Riga Tech Univ Inst Photon Elect & Telecommun LV-1048 Riga Latvia Univ Politecn Cartagena Dept Informat Technol & Commun Plaza Hosp 1 Cartagena 30202 Spain

Considering the exponential growth of network traffic, particularly driven by over-the-top (OTT) streaming applications, video category network traffic constitutes a significant portion of overall network traffic. However, most research has focused on the categorization and diversity of network traffic using benchmark datasets, with limited attention paid to video category network traffic. Additionally, there is a lack of proprietary Internet video traffic datasets, and the few proprietary datasets available often lack transparency and interpretability. This paper introduces a novel framework for generating proprietary Internet video traffic datasets, addressing existing gaps in dataset quality and consistency. We propose the nYFTQC algorithm, which enables the creation of fifteen detailed datasets specifically designed for Internet video traffic analysis. The proposed datasets demonstrate superior performance metrics, including completeness, consistency, and transparency. This comprehensive approach enhances the accuracy and interpretability of traffic sample analysis, providing valuable resources for future research in video category network traffic.

关键词： network traffic classification proprietary dataset algorithm interpretability

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Improving english vocabulary learning with a hybrid deep learning model optimized by enhanced search algorithm

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EGYPTIAN INFORMATICS JOURNAL 2025年 29卷

作者： Zheng, Fang Guilin Univ Technol Coll Foreign Studies Guilin 541006 Peoples R China

In this study, we propose a novel deep-learning architecture that is designed to facilitate vocabulary acquisition for second-language learners of English. A hybridized model combining a tuned LSTM and CaffeNet with the EHGS algorithm. The EHGS was selected from the other algorithms including Manta Ray Foraging Optimization (MRFO), Equilibrium Optimizer (EO), Marine Predators algorithm (MPA), Runge Kutta Optimizer (RUN), and White Shark Optimizer (WSO) since it is the most balanced algorithm out of all of them in terms of exploration vs. exploitation. From a methodological perspective, we adopt a hybrid CNN-based structural approach to enhance the learning of features and the effective processing of temporal information. It uses Oxford English Corpus and WordNet datasets for pre-training to make sure it is robust and effective. The specified model also outperformed very few with comparative evaluations using metrics of accuracy, F1-score, precision, and mean squared error (MSE). Our model showed an accuracy of 0.92 and an F1-score of 0.91 which far surpassed traditional Gaussian and LSTM methods (accuracy of 0.85 and F1-score 0.84). These findings make clear more advanced NLP techniques that can be applied for the development of intelligent education technology that can help non-native English speakers learn new vocabulary at an unprecedented rate. The better results provided by the proposed model mainly reveal its applicability in novel learning environments and offer students personalized, adapted, and immersive learning experiences.

关键词： English vocabulary learning Convolutional Neural Network LSTM CaffeNet Enhanced Hunger Game Search (HGS) algorithm Text classification Vocabulary coverage Natural Language Processing Online vocabulary learning platforms

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Quantification of Spatial Ventilation Defect Sparsity in Hyperpolarized Gas Magnetic Resonance Imaging of Lungs Utilizing a Three-Dimensional Clustering algorithm

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NMR IN BIOMEDICINE 2025年第3期38卷 e70005页

作者： Delgado, Gabriela Maria Garcia Shammi, Ummul Afia Ruppel, Mia R. Altes, Talissa A. Mugler III, John P. Meyer, Craig H. Qing, Kun de Lange, Eduard E. Mata, Jaime Ruset, Iulian C. Hersman, F. W. Thomen, Robert P. Univ Missouri Chem & Biomed Engn Columbia MO 65211 USA Univ Illinois Sch Engn Biomed Engn Chicago IL USA Univ Missouri Sch Med Radiol Columbia MO 65211 USA Univ Virginia Sch Med Radiol & Med Imaging Charlottesville VA USA Univ Virginia Biomed Engn Charlottesville VA USA City Hope Natl Med Ctr Radiat Oncol Duarte CA USA Xemed LLC Durham NH USA

Hyperpolarized gas (HPG) magnetic resonance (MR) imaging allows for the quantification of pulmonary defects with the ventilation defect percentage (VDP). Although informative, VDPs lack information regarding the spatial distribution of defects. We developed a method of quantifying the focality/sparseness of ventilation defects in hyperpolarized-gas lung MR images. The study involved a total of 56 subjects: 14 asthmatics (age mean +/- sd = 45.1 +/- 18.9), 25 COPD subjects (age = 60.6 +/- 10.4), and 17 CF subjects (age = 21.8 +/- 8.4). The analyzed data are from four different studies: Study 1 used a 3-T gradient echo (GRE) sequence, Study 2 used a 1.5-T GRE sequence, Study 3 used a 1.5-T two-dimensional spiral sequence, and Study 4 used a 1.5-T three-dimensional balanced steady-state free precession (bSSFP) sequence. We developed an algorithm that quantifies the focality/sparseness of ventilation defects as a subject's cluster index (CI). The algorithm was assessed on synthesized spherical defect clusters and 3D lung volume defects of varying sizes/distributions. CI and whole-lung VDP were calculated for asthmatic, COPD, and CF subjects. Pearson correlation coefficients and linear regression models between CI and FEV1pp, as well as CI and VDP, were performed to evaluate CI among asthma, COPD, and CF. T tests were performed to evaluate CI/VDP ratios among previously mentioned lung diseases. p values less than 0.05 were statistically significant. Subject CI well represents defect focality by visual inspection. Pearson correlation coefficients between CI and VDP were r = 0.60 (p = 2.21 x 10-2) for asthma, r = 0.79 (p = 3.15 x 10-6) for COPD, and r = 0.84 (p = 2.80 x 10-5) for CF. Pearson correlation coefficients between CI and FEV1pp was r = -0.47 (p = 0.0002). Analysis of variance (ANOVA) and a Tukey's honestly significant difference (HSD) test revealed that the ratio of whole-lung CI/VDP was significantly different between asthma/CF (p = 0.04) and CF/COPD (p = 0.008),

关键词： algorithm clustering defects gas hyperpolarized lungs MRI ventilation

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Optimal design of marine nuclear power deaerator based on improved particle swarm optimization algorithm

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ANNALS OF NUCLEAR ENERGY 2025年 217卷

作者： Zhao, Jiarui Li, Yanjun Chen, Xu Fu, Yuan Sun, Baozhi Cao, Yuanwei Shi, Jianxin Harbin Engn Univ Coll Power & Energy Engn 145 Nantong St Harbin 150001 Peoples R China Daowai Dist Commiss Discipline Inspect 55 North Fourteenth St Harbin 150000 Peoples R China

Deaerator is a key equipment in the secondary circuit system, its operating parameters and structural size have a significant impact on the thermal efficiency and arrangement rationality of the system. In this research, a mathematical model of the marine nuclear power deaerator is established, and the influence of thermal and structural parameters on the weight and volume of the deaerator is analyzed. An improved particle swarm optimization algorithm is proposed by introducing Tent chaotic mapping, evolutionary factor and Metropolis criterion, and its performance is verified. Taking the weight and volume minimization of the deaerator as the optimization objectives, the optimal design of the deaerator is carried out using the proposed IPSO algorithm, while satisfying the structural and performance constraints. The optimization results show that the volume and weight of the deaerator can be reduced by 12.979% and 10.213%, respectively, and the feasibility of the optimization design method is proved theoretically.

关键词： Secondary circuit system Deaerator Improved particle swarm optimization algorithm Optimal design

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Report on optimisation for efficient dynamic task distribution in drone swarms using QRDPSO algorithm

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AIN SHAMS ENGINEERING JOURNAL 2025年第2期16卷

作者： Converso, Giuseppe Mehiar, Duaa Rukovich, Alexander Brzhanov, Rashit Univ Naples Federico II DICMaPI Dept Chem Mat & Ind Prod Engn Naples Italy Middle East Univ MEU Fac Informat Technol Artificial Intelligence Dept Amman Jordan North Eastern Fed Inst MK Ammosova Neryungri Tech Inst Branch State Autonomous Educ Inst Higher Profess Educ Neryungri Russia Caspian Univ Technol & Engn Dept Construct Engn Aktau Kazakhstan

The primary aim was to develop a Quantum Robot Darwinian Particle Swarm Optimisation (QRDPSO) algorithm and assess its performance against the conventional RDPSO. Using MATLAB-based mathematical modelling, QRDPSO was evaluated for its efficiency in dynamic task distribution and inter-drone communication stability. The results demonstrate that QRDPSO finds optimal solutions 16.3% faster than RDPSO, with performance improvements as the swarm size increases. Specifically, when the number of drones was increased from 5 to 20, the number of iterations required for QRDPSO changed from 384 to 189. However, for RDPSO, the number of iterations changed from 439 to 242. Additionally, QRDPSO showed a 27.1% reduction in drone loss rates, outperforming RDPSO in terms of maintaining operational resources, especially in larger swarms. These findings have practical implications, as QRDPSO's efficiency and stability can support extensive drone applications requiring synchronised, reliable swarm behaviour.

关键词： algorithm Darwinian QRDPSO Quantum-optimised RDPSO Swarm

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A composite particle swarm optimization algorithm with future information inspired by non-equidistant grey predictive evolution for global optimization problems and engineering problems

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ADVANCES IN ENGINEERING SOFTWARE 2025年 202卷

作者： Hao, Rui Hu, Zhongbo Xiong, Wentao Jiang, Shaojie Yangtze Univ Sch Informat & Math Jingzhou Hubei Peoples R China Hubei Engn Univ Sch Math & Stat Xiaogan Hubei Peoples R China Yangtze Univ Jingzhou Hosp Jingzhou Hubei Peoples R China

Particle swarm optimization (PSO) and its numerous performance-enhancing variants area kind of stochastic optimization technique based on collaborative sharing of swarm information. Many variants took current particles and historical particles as current and historical information to improve their performance. If future information after each current swarm can be mined to participate in collaborative search, the algorithmic performance could benefit from the comprehensiveness of the information including historical, current and future information. This paper proposes a composite particle swarm optimization algorithm with future information inspired by non-equidistant grey predictive evolution, namely NeGPPSO. The proposed algorithm firstly employs non-equidistant grey predictive evolution algorithm to predict a future particle as future information for each particle of a current swarm. Secondly, four particles including prediction particle, particle best and swarm best of the current swarm, and a history memory particle are used as guide particles to generate four candidate positions. Finally, the best one in the four positions is greedily selected as an offspring particle. Numerical experiments are conducted on 42 benchmark functions given by the Congress on Evolutionary Computation 2014/2022 and 3 engineering problems. The experimental results demonstrate the overall advantages of the proposed NeGPPSO over several state-of-art algorithms.

关键词： Future information Improved particle swarm optimization algorithm Non-equidistant grey prediction evolutionary Particle swarm optimization algorithm

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Power density optimization for proton exchange membrane fuel cell stack based on data-driven and improved light spectrum algorithm

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ENERGY CONVERSION AND MANAGEMENT 2025年 326卷

作者： Chen, Xi Feng, Wentao Hu, Yukang You, Shuhuai Lu, Weidong Zhao, Bin Hunan Inst Sci & Technol Sch Energy & Elect Engn Yueyang 414006 Peoples R China Hunan Inst Sci & Technol Coll Mech Engn Yueyang 414006 Peoples R China Hunan Zhongding Thermal Technol Co LTD Yueyang 414006 Peoples R China Yueyang Yuanda Thermal Energy Equipment Co LTD Yueyang 14006 Peoples R China Changsha Univ Sci & Technol Sch Elect & Informat Engn Changsha 410000 Peoples R China

As a green power conversion device, the power performance of proton exchange membrane fuel cell (PEMFC) stack is determined by the actual operating parameters. The optimization of the power density and corresponding operating parameters of the PEMFC according to the target demand is essential. In this paper, a global optimization strategy for the power density of PEMFC stack is proposed, which combines the random forest algorithm (RF) and the improved light spectrum optimization algorithm (ILSO). A dataset is constructed based on the simulation results of the PEMFC mathematical model and used to train a data-driven surrogate model. The input variables of the surrogate model are identified, including operating temperature, anode pressure, cathode/ anode relative humidity and current density, and the output is power density. Prediction performance shows that the mean absolute error (MAE), mean square error (MSE), and coefficient of determination (R2) in the training set are 0.007, 0.000097 and 0.9991, respectively. The surrogate model has considerable accuracy compared to the original model with a relative error of 0.86 %. Additionally, the average optimization time of the surrogate model is 1716.3 s, which is reduced by 44.8 % compared to the original model. By employing this strategy, an optimal power density of 1.211 W/cm2 is obtained and the corresponding operating parameters under various target powers are predicted.

关键词： PEMFC Power density Random forest Improved light spectrum optimization algorithm

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The Animated Oat Optimization algorithm: A nature-inspired metaheuristic for engineering optimization and a case study on Wireless Sensor Networks

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KNOWLEDGE-BASED SYSTEMS 2025年 318卷

作者： Wang, Ruo-Bin Hu, Rui-Bin Geng, Fang-Dong Xu, Lin Chu, Shu-Chuan Pan, Jeng-Shyang Meng, Zhen-Yu Mirjalili, Seyedali North China Univ Technol Sch Informat Sci & Technol Beijing 100144 Peoples R China Southwest Jiaotong Univ State Key Lab Rail Transit Vehicle Syst Chengdu 610031 Peoples R China Univ Adelaide Sch Comp & Math Sci Adelaide 5005 Australia Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing 210044 Peoples R China Fujian Univ Technol Sch Comp Sci & Math Fuzhou 350118 Peoples R China Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Australia Obuda Univ Univ Res & Innovat Ctr H-1034 Budapest Hungary

In this paper, a novel metaheuristic algorithm called the Animated Oat Optimization algorithm (AOO) is proposed, inspired by the natural behavior of Animated Oat in the environment. AOO simulates 3 unique behaviors of Animated Oat: (i) seed dispersal through natural elements such as wind, water, and animals;(ii) under the influence of hygroscopic movement, the primary awn of Animated Oat seeds undergoes distortion and rotation, enabling the entire seed to roll and propagate;and (iii) during the rolling propagation, energy is stored upon encountering obstacles, triggering a propulsion mechanism under certain conditions to further disperse the seeds. To evaluate the algorithm's capabilities in exploration and exploitation, we utilized the CEC2022 test suite, which comprises 12 functions. Comparative analysis with 9 well-known optimization algorithms demonstrates that AOO exhibits superior competitiveness. Furthermore, we extend our evaluation to five widely-used engineering design problems to confirm the algorithm's performance in these domains. Finally, we combined AOO with DV-Hop to validate its competitiveness and effectiveness in experiments on node localization in 3-dimensional Wireless Sensor Networks. These results demonstrate AOO's ability to tackle complex optimization challenges and its potential as a reliable optimizer for practical engineering applications. Source codes of AOA are publicly available at https://***/robingit77/Animated-Oat-Optimization-algorithm-AOO- and https ://***/morealgorithms.

关键词： Animated Oat Optimization algorithm Seed dispersal mechanism Hygroscopic movement Wireless Sensor Networks Swarm intelligence algorithm Optimization Benchmark

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An Auto-Adjusting Hybrid Quantum Genetic algorithm-Spectre platform for the multi-objective optimization of analog circuit sizing

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ALEXANDRIA ENGINEERING JOURNAL 2025年 116卷 574-585页

作者： Do, Thinh Quang Nguyen, Hoang Trong Mcniven, Bradley D. E. Hoang, Trang Zhang, Lihong Dobre, Octavia A. Duong, Trung Q. Mem Univ Newfoundland Dept Elect & Comp Engn Fac Engn & Appl Sci St John NF A1B 3X9 Canada Ho Chi Minh City Univ Technol HCMUT Fac Elect & Elect Engn Dept Elect 268 Ly Thuong Kiet StDist 10 Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City Linh Trung Ward Ho Chi Minh City Vietnam Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast North Ireland

Among the phases constituting analog circuit design, circuit sizing is considered labor-intensive, formidable, and heavily experience-dependent due to its non-linearity. Asa result, design automation coupled with effective optimization techniques has arisen as a feasible candidate to address challenges with circuit design and satisfy the increasing need for high-performance circuits. Among evolutionary algorithms, the combination of the genetic algorithm (GA) and quantum computing techniques has yielded the hybrid quantum genetic algorithm (HQGA) which has proven to be an effective optimization method in many fields due to its convergence rate and near-optimal solutions. This paper introduces an upgraded version of HQGA we call the Auto-adjusting Hybrid Quantum Genetic algorithm (AHQGA) which avoids premature convergence and improves convergence speed through the use of an additional best-fitness-based scheme for rotation angles. In particular, this work proposes the utility of AHQGA for the multi-objective optimization of analog circuit sizing, with the two- stage Miller-compensated operational amplifier (op-amp) used as a topological case study. Additionally, for an objective evaluation, optimization results by AHQGA are compared with those by HQGA with fixed rotation angles and classical GA.

关键词： Auto-adjusting hybrid quantum genetic algorithm Classical genetic algorithm Hybrid quantum genetic algorithm Rotation angles Two-stage miller-compensated operational amplifier

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