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Thermal desorption-photoionization ion mobility-electronic nose (TD-PIM-Nose) with distance-probability joint decision SVMa* algorithm: A novel system for Daqu Grade identification

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FOOD CHEMISTRY 2025年第Pt 3期463卷 141360页

作者： Cheng, Shiwen Han, Qiang Qin, Yumei Chen, Li Mao, Yuezhong Yang, Jianmei Zheng, Ruihang Han, Jianzhong Qin, Zihan Chen, Chuang Tian, Shiyi Zhejiang Gongshang Univ Sch Food Sci & Biotechnol Hangzhou 310018 Peoples R China Zhejiang Gongshang Univ Collaborat Innovat Ctr Stat Data Engn Technol & Ap Hangzhou 310018 Peoples R China Zhejiang Gongshang Univ China UK Joint Res Lab Eating Behav & Appetite Hangzhou 310018 Peoples R China Jiangsu Yanghe Brewery Joint Stock Co Ltd Suqian 223800 Peoples R China Chinese Acad Sci Dalian Inst Chem Phys Dalian 116023 Peoples R China

Electronic nose is a bionic technology that uses sensor arrays and pattern recognitiona* algorithms to mimic the human olfactory system. This study developed a thermal desorption-photoionization ion mobility-electronic nose (TD-PIM-Nose) system, employing thermal desorption for direct sampling and humidity control, with a photoionization ion mobility tube as virtual sensor array for component separation and detection, and pattern recognitiona* algorithms for signal processing to differentiate and identify samples. Furthermore, it was applied to assess four quality grades of Daqu samples ("Excellent+", "Excellent", "Grade I", and "Grade II") determined by the Check-All-That-Apply (CATA) method. Characteristic compound differences among these grades were identified using fingerprint spectra and reduced mobility values. A distance-probability joint decision support vector machine (SVM)a* algorithm model was established, validated against sensory CATA standards. Results showed identification accuracies: 90 %, 90 %, 96.88 %, and 100 % for respective grades. These findings demonstrated the promising potential of the TD-PIM-Nose system in Daqu quality grading.

关键词： TD-PIM-Nose Quality grading Distance-probability joint decision SVM algorithm CATA method Daqu

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Greedy-assisted teaching-learning-based optimizationa* algorithm for cost-based hybrid flow shop scheduling

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 273卷

作者： Ullah, Wasif Ab Rashid, Mohd Fadzil Faisae Mutasim, Muhammad Ammar Nik Univ Malaysia Pahang Al Sultan Abdullah Fac Mfg & Mechatron Engn Technol Pekan 26600 Malaysia Univ Malaysia Pahang Al Sultan Abdullah Fac Mech & Automot Engn Technol Pekan 26600 Malaysia

Production scheduling is a strategic process that organizes the execution of jobs on available resources to optimize specific objectives. One significant scheduling challenge is the Cost-based Hybrid Flow Shop (CHFS) problem, which involves optimizing job scheduling across multiple stages to minimize scheduling-related costs. However, limited attention has been given to CHFS when considering holistic cost models using efficienta* algorithms. This paper presents a novel Greedy-Assisted Teaching-Learning-Based Optimization (GTLBO)a* algorithm for CHFS. Unlike previous studies that focus on isolated cost factors, this research formulated an integrated mathematical model for CHF holistically capturing labor, energy consumption, maintenance, and late penalty costs. The GTLBOa* algorithm incorporates a unique hybrid initialization strategy, generating 10 % of the initial population using a Greedya* algorithm to enhance exploration efficiency. The performance of GTLBO was evaluated through computational experiments involving 12 test instances, with comparativea* algorithms included for analysis. Results from the Wilcoxon rank-sum test indicated a significant difference between the outputs of GTLBO and othera* algorithms, with GTLBO outperforming the comparativea* algorithms in 75 % of the test instances. Additionally, the case study validation showed that GTLBO can reduce costs by 0.23 % to 4.31 % compared to othera* algorithms. This research offers valuable insights for manufacturers seeking to optimize CHFS scheduling to reduce production expenses.

关键词： Cost optimization Hybrid flow shop scheduling Teaching learning-based optimization algorithm

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CosIn: A statistical-baseda* algorithm for computation of space-speed time delay in pedestrian motion

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2025年 170卷

作者： Wang, Jinghui Lv, Wei Cao, Shuchao Wang, Zhensheng Wuhan Univ Technol Sch Safety Sci & Emergency Management Wuhan Peoples R China Wuhan Univ Technol China Res Ctr Emergency Management Wuhan Peoples R China Jiangsu Univ Sch Automot & Traff Engn Zhenjiang Peoples R China Beijing Normal Univ Sch Artificial Intelligence Zhuhai Peoples R China

Precise assessment of Space-speed time delay (TD) is critical for distinguishing between anticipation and reaction behaviors within pedestrian motion. Besides, the TD scale is instrumental in the evaluation of potential collision tendency of the crowd, thereby providing essential quantitative metrics for assessing risk. In this consideration, this paper introduced the CosIna* algorithm for evaluating TD during pedestrian motion, which includes both the CosIn-1 and CosIn-2a* algorithms. CosIn-1a* algorithm analytically calculates TD, replacing the numerical method of discrete cross-correlation, whereas the CosIn-2a* algorithm estimates the TD from a statistical perspective. Specifically, the CosIn-1a* algorithm addresses the precise computation of TD for individual pedestrians, while the CosIn-2a* algorithm is employed for assessing TD at the crowd scale, concurrently addressing the imperative of real-time evaluation. Efficacy analyses of the CosIn-1 and CosIn-2a* algorithms are conducted with data from single-file pedestrian experiments and crowd-crossing experiments, respectively. During this process, the discrete cross-correlation method was employed as a baseline to evaluate the performance of botha* algorithms, which demonstrated notable accuracy. Thisa* algorithm facilitate the precise evaluation of behavior patterns and collision tendency within crowds, thereby enabling us to understand the crowds dynamics from a new perspective.

关键词： Space-speed time delay Crowds classification Fourier analysis Statistical analysis algorithm

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Decomposition-based dual-population evolutionarya* algorithm for constrained multi-objective problem

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SWARM AND EVOLUTIONARY COMPUTATION 2025年 95卷

作者： Wang, Yufeng Zhang, Yong Xu, Chunyu Bai, Wen Zheng, Ke Dong, Wenyong Nanyang Inst Technol Nanyang 473000 Henan Peoples R China Zhengzhou Univ Light Ind Zhengzhou 450002 Henan Peoples R China Wuhan Univ Wuhan 430072 Hubei Peoples R China

Constrained multi-objective optimization problems require optimizing and solving multiple objectives while satisfying the constraints. However, in the process of solving this problem, some constraints created infeasible obstacle regions, which led to the neglect of a portion of the constrained Pareto front (CPF). In order to solve this problem, A novel decomposition-based dual-population constrained multi-objective evolutionarya* algorithm (DD-CMOEA) is proposed. DD-CMOEA adopts a dual population collaborative search strategy, which can quickly find CPF. In the first stage, DD-CMOEA conducts dual population searches on CPF and unconstrained Pareto front (UPF) separately. During the search process, sub-population A uses unconstrained global exploration to obtain information that helps sub-population B jump through infeasible obstacle areas. In the second stage, when the convergence of the sub-population searching for UPF stagnates, the angle- based constraint advantage principle is used for reverse search. It ensures that the searched CPF solution set can be evenly distributed throughout the entire search space. The experimental results on three standard benchmark function suites show that DD-CMOEA outperforms the other six state-of-the-arta* algorithms in solving constrained multi-objective optimization problems.

关键词： Dual-population Two stage Angle-based constraint dominance principle Constrained multi-objective evolutionary algorithm

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A Polynomial-Timea* algorithm for Detection of Uncovered Transitions in a Petri Net-Based Concurrent System

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APPLIED SCIENCES-BASEL 2025年第2期15卷 680-680页

作者： Wojnakowski, Marcin Malinski, Maxim Wisniewski, Remigiusz Obuchowicz, Andrzej Li, Zhiwu Konarczak, Dawid Univ Zielona Gora Inst Control & Computat Engn Ul Prof Z Szafrana 2 PL-65516 Zielona Gora Poland Univ Zielona Gora Doctoral Sch Exact & Tech Sci Al Wojska Polskiego 69 PL-65762 Zielona Gora Poland Univ Sci & Technol Inst Syst Engn Taipa Macau Peoples R China Univ Zielona Gora Comp Ctr Ul Podgorna 50a PL-65246 Zielona Gora Poland

This paper introduces a novela* algorithm for the efficient verification of a Petri net-based concurrent control system. The proposed method is based on the computation of transition invariant coverage to detect possible errors in the modelled system. Transition invariants play a crucial role in ensuring the correctness and reliability of such systems;however, existing methods often struggle with high computational demand, especially in the case of large and complex systems. The proposed approach addresses this challenge by performing a fast polynomial-time analysis to identify uncovered transitions, thereby streamlining the verification process. The effectiveness and efficiency of the proposed technique is verified experimentally with a set of 386 test modules (benchmarks) and compared against two well-known established methods: the classical method proposed by Mart & iacute;nez-Silva (as a referencea* algorithm) and PIPE (Platform Independent Petri Net Editor) tool. The results of the experiments confirm high performance of the presenteda* algorithm, which was able to compute results for all the tested cases. In contrast, both the referencea* algorithm as well as the PIPE tool failed to deliver results for all examined models within one hour. The proposeda* algorithm is especially useful in early design stages, offering system designers timely insights into potential issues.

关键词： Petri nets preliminary verification transition invariants concurrent systems uncovered transitions algorithm computational complexity

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Strategic layout planning of carbon emission monitoring devices in the open industrial area based on an improved particle swarm optimizationa* algorithm

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MEASUREMENT 2025年 244卷

作者： Ding, Ning Jiang, Wenting Xi, Yanheng Li, Hongwei Su, Jun Prasad, Krishnamachar Southwest Petr Univ Sch Elect Engn & Informat Chengdu 610500 Peoples R China Chengdu SchrO Dinger Energy Carbon Technol Co Ltd Chengdu 610041 Peoples R China Xiamen Univ Technol Sch Elect Engn & Automat Xiamen 361012 Peoples R China Auckland Univ Technol Sch Engn Comp & Math Sci Auckland 1142 New Zealand

As global attention on reducing carbon emissions intensifies, accurate and efficient monitoring in industrial environments is crucial for meeting regulatory requirements and promoting sustainable practices. However, existing carbon emission monitoring technologies are limited, particularly in open industrial scenarios, making effective monitoring challenging. This study introduces an innovative approach to optimize the placement of carbon emission monitoring devices in such settings, combining the Gaussian plume diffusion model with realtime meteorological data to accurately map emission distributions. An enhanced-improved particle swarm optimization (PSO)a* algorithm proposed to address the complexities of diverse emission sources and varying environmental conditions, ensuring precise and continuous monitoring. The methodology includes filtering emission data based on detection thresholds, constructing a dynamic monitoring surface, and verifyinga* algorithm randomness to enhance optimization performance. Applied to the Fuyang industrial area, covering 408,457 square meters with three major emission sources, the optimal placement of five CO2 sensors with 150 m communication distance achieved 96.6 % monitoring coverage. This research offers a scalable, efficient solution for carbon monitoring in open industrial environments, significantly improving emission detection accuracy and contributing to global carbon reduction efforts.

关键词： Carbon emission monitoring Open industrial scenarios Improved particle swarm optimization algorithm Carbon emission concentration Monitorable surface

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Improving english vocabulary learning with a hybrid deep learning model optimized by enhanced searcha* 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 EHGSa* algorithm. The EHGS was selected from the othera* algorithms including Manta Ray Foraging Optimization (MRFO), Equilibrium Optimizer (EO), Marine Predatorsa* algorithm (MPA), Runge Kutta Optimizer (RUN), and White Shark Optimizer (WSO) since it is the most balanceda* 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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A composite particle swarm optimizationa* 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, thea* algorithmic performance could benefit from the comprehensiveness of the information including historical, current and future information. This paper proposes a composite particle swarm optimizationa* algorithm with future information inspired by non-equidistant grey predictive evolution, namely NeGPPSO. The proposeda* algorithm firstly employs non-equidistant grey predictive evolutiona* 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-arta* algorithms.

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

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Report on optimisation for efficient dynamic task distribution in drone swarms using QRDPSOa* 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)a* 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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Power density optimization for proton exchange membrane fuel cell stack based on data-driven and improved light spectruma* 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 foresta* algorithm (RF) and the improved light spectrum optimizationa* 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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