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hybrid algorithm for image denoising of mixed speckle-impulse noise in planar laser-induced fluorescence (PLIF) imaging

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OPTICS AND LASERS IN ENGINEERING 2025年 187卷

作者： Mirzaei, Mohsen Wang, Yejun Wang, Qiu Guo, Chenglong Li, Pan Zhao, Wei Chinese Acad Sci Inst Mech State Key Lab High Temp Gas Dynam Beijing 100190 Peoples R China Vali e Asr Univ Rafsanjan Dept Mech Engn Rafsanjan Iran Univ Chinese Acad Sci Sch Engn Sci Beijing 100049 Peoples R China China Univ Min & Technol Sch Low Carbon Energy & Power Engn Xuzhou 221116 Peoples R China Chinese Acad Sci Inst Mech Key Lab Micrograv Beijing 100190 Peoples R China

Flow visualization is essential for a comprehensive understanding of complex fluid dynamics. Planar laserinduced fluorescence (PLIF) has emerged as one of the most promising techniques for multi-dimensional imaging acquisition, offering high spatial and temporal resolutions. However, PLIF images frequently suffer from impulse and speckle noise under low-density, low-pressure conditions, thereby affecting the quality of visualization. This study proposes a novel hybrid algorithm that integrates order-statistic and non-local means filters with residue feedback. The algorithm enhances image quality and eliminates noise effectively by incorporating recursive processes guided by the structural similarity index (SSIM) and anisotropic diffusion. The efficacy of the method is confirmed through evaluation using peak signal-to-noise ratio (PSNR) and SSIM on images with varying noise levels. The algorithm demonstrates computational efficiency and shows promise for denoising PLIF images.

关键词： Planar laser-induced fluorescence (PLIF) Image denoising hybrid algorithm Residue feedback Contrast enchantment

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A hybrid algorithm for the dimensional synthesis of parallel manipulators

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2025年第9期239卷 3203-3215页

作者： Yang, Chao Zhang, Hui Huang, Fengli Ye, Wei Jiaxing Nanhu Univ Coll Mech & Elect Engn Jiaxing Peoples R China Jiaxing Univ STl Valley Ind & Innovat Inst G60 Jiaxing Peoples R China Zhejiang Sci Tech Univ Natl & Local Joint Engn Res Ctr Reliabil Anal & Te Hangzhou Peoples R China

Increasing the number of non-dominated solutions among multiple objectives is beneficial for determining the optimal comprehensive performance of parallel manipulators (PMs). In engineering, we often achieve this by increasing the population size of intelligent optimization algorithms, which leads to an exponential increase in computational costs, especially for high computationally intensive objective functions. How to increase the number of non-dominated solutions without significantly increasing computational costs is a challenge faced by the dimensional synthesis of PMs. A hybrid algorithm combing particle swarm optimization (PSO) and direct search algorithms was proposed in this work to fix this issue. And, a procedure for direct search algorithm to determine the Pareto optimal front is proposed. The proposed hybrid algorithm is implemented in three steps: (1) The design parameter space is coarsely meshed to determine the amplitude of objective functions to achieve its dimensionless purpose;(2) the PSO algorithm is adopted to obtain the approximate Pareto front, as well as the Pareto front hypersurface stretched by the optimal design parameter space;(3) the neighborhood of the approximate Pareto optimal set was finely meshed, and the direct search algorithm is proposed to update the non-dominated solutions that are closer to the real Pareto front. The proposed hybrid algorithm can obtain more non-dominated solutions while avoiding the significant increase in computational costs caused by increasing population size. The proposed algorithm was implemented using the dimensional synthesis of the 2UPR-RPU PM. Compared with the PSO algorithm, which produced 80 sets of non-dominated solutions, the proposed hybrid algorithm generated 242 sets of solutions with the computational costs only increased 32%.

关键词： Parallel manipulator dimensional synthesis pareto front hybrid algorithm

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A novel multivariable optimization strategy for methanol to propylene fixed bed reactors using a hybrid algorithm

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CHEMICAL ENGINEERING SCIENCE 2025年 306卷

作者： Zhang, Chijin Hong, Xiaodong Liao, Zuwei Jiang, Binbo Wang, Jingdai Yang, Yongrong Zhejiang Univ Coll Chem & Biol Engn Hangzhou 310027 Peoples R China Zhejiang Univ Engn Res Ctr Funct Mat Intelligent Mfg Zhejiang Pr ZJU Hangzhou Global Sci & Technol Innovat Ctr Hangzhou 311215 Peoples R China

The methanol-to-propylene (MTP) process holds significant promise as a viable route for propylene production. To enhance the efficiency of this process, we conducted a comprehensive study employing a two-step approach that integrates micro- and macro-level perspectives. Firstly, we established a gas-solid two-phase reactor model tailored for an industrial six-stage fixed-bed reactor. Through a rigorous sensitivity analysis, we investigated the impact of catalyst particle size on bed pressure drops and product distribution, ultimately identifying an optimal particle size range between 2.5-3.5 mm. Subsequently, we developed a hybrid optimization algorithm that seamlessly integrates a stochastic algorithm with a deterministic algorithm. A key component of our methodology was the utilization of the orthogonal collocation method on finite elements (OCFE), which effectively converted differential equations (DEs) into algebraic equations (AEs), simplifying the optimization process. The results show that methanol throughput increases by 4.75 %, and propylene selectivity increases from 65.34 % to 73.98 %.

关键词： MTP process Particle design hybrid algorithm Reaction optimization

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A Knowledge-Driven hybrid algorithm for Solving the Integrated Production and Transportation Scheduling Problem in Job Shop

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第2期26卷 2707-2720页

作者： Yao, Youjie Wang, Cuiyu Li, Xinyu Gao, Liang Huazhong Univ Sci & Technol State Key Lab Intelligent Mfg Equipment & Technol Wuhan 430074 Peoples R China

Intelligent transportation systems, incorporating multiple AGVs, are extensively utilized in manufacturing workshops in various industries. This widespread use has spurred significant research interest in the integrated production and transportation scheduling problem, particularly in job shop environments. However, current research often fails to adequately leverage domain knowledge, leading to algorithms that struggle to find high-quality solutions for large-scale problems. To address this issue, this paper proposes a knowledge-driven hybrid algorithm (KDHA). The domain knowledge incorporated in the KDHA includes: 1) three critical path-based neighborhood structures for comprehensive neighborhood solution searches, 2) three neighborhood cropping methods to avoid ineffective searches for poor solutions, and 3) a new fast evaluation method to enhance the efficiency of neighborhood solution searching. Additionally, a new encoding method is introduced to achieve a one-to-one mapping between the chromosome and the disjunctive graph, allowing valuable information from neighborhood solutions to contribute to the algorithm's evolution. Comparative experiments between the proposed algorithm and other state-of-the-art approaches are conducted on the small-scale EX and large-scale SWV benchmarks. The results demonstrate that the proposed KDHA is able to output better solutions efficiently and consistently, and updates the best solutions of all 20 SWV instances.

关键词： Transportation Job shop scheduling Production Encoding Benchmark testing Search problems Metaheuristics Decoding Sequential analysis Periodic structures Integrated scheduling job shop transportation scheduling hybrid algorithm neighborhood structure

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A New hybrid algorithm Based on PSO and Fireworks algorithm for Optimal Design of Metasurface Absorber in RF Energy Harvesting

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IEEE TRANSACTIONS ON MAGNETICS 2025年第2期61卷

作者： Chen, Na Yang, Shiyou An, Siguang Zhejiang Univ Coll Elect Engn Hangzhou 310027 Peoples R China China Jiliang Univ Dept Elect Engn Hangzhou 314423 Peoples R China

To address the nonlinear, multimodal, and computationally intensive challenges inherent in the optimal design of metasurface absorbers for radio frequency (RF) energy harvesting, we propose an innovative hybrid algorithm that combines particle swarm optimization (PSO) and fireworks algorithm (FWA). The proposed hybrid algorithm features several enhancements: 1) a quasi-scaling (Q.S.) mechanism, inspired by the frequency response of electromagnetic (EM) devices shifting with the scaling of physical parameters, is developed to balance the exploration and exploitation searches;2) a self-adaptive inertia weight is introduced to enhance the convergence speed;and 3) an elite-preservation strategy is employed to ensure a global convergence. The effectiveness and merits of the proposed algorithm are validated by solving first mathematical test functions;then, two practical cases: a multi-band absorber (1.8-3 GHz, 5.85 GHz) and a broadband absorber (8-15 GHz), where a new dual-polarized metasurface absorber topology is employed. Both cases are efficiently optimized, demonstrating high absorption efficiencies within the targeted bands, and validated by both simulated and measured results.

关键词： Convergence Sparks Explosions Radio frequency Metasurfaces Frequency response Fireworks algorithm Standards Topology Particle swarm optimization Design optimization fireworks algorithm (FWA) hybrid algorithm metasurfaces particle swarm optimization (PSO)

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Optimizing mechanical properties of PETG fabricated parts via PSO-BFO hybrid algorithm in fused deposition modelling

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INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM 2025年 1-17页

作者： Anand, Sourabh Dhankhar, Gayatri Satyarthi, Manoj Kumar Guru Gobind Singh Indraprastha Univ Univ Sch Informat Commun & Technol Sec 16C New Delhi 110078 India

Polyethylene Terephthalate Glycol (PETG) material is utilized across various domains including orthopaedics, vascular health, and surgery, where the requirements for reliability, strength, and mechanical properties are important. In order to meet individual's needs, customized solutions are often needed. While PETG products can be manufactured through methods like injection molding, blow molding and thermoforming, these processes are costly and less adaptable to customization. In contrast, 3D printing offers a more cost-effective and customized solutions. Yet, its anisotropic nature poses challenges, leading to lower mechanical properties. In order to improve mechanical properties, our research focuses on improving important process factors such as build orientation, speed rate, infill density, nozzle temperature, and layer thickness. RSM is used in the DOE for the PETG material. Further, linear multiple regression establishes the relationship between input and output parameters, with ANOVA verifying statistical significance. Particle Swarm Optimization (PSO), Bacterial Foraging Optimization (BFO), and a hybrid method combining PSO's global search with BFO's local search are then used to optimize parameters. The hybrid method overcomes PSO's local optima trapping and BFO's slower convergence, resulting in better fitness values and faster computation times compared to individual PSO and BFO.

关键词： Fused deposition modelling Optimization PSO BFO hybrid algorithm

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A Partition-Based hybrid algorithm for Effective Imbalanced Classification

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DATA 2025年第4期10卷 54-54页

作者： Theephoowiang, Kittipong Hanskunatai, Anantaporn King Mongkuts Inst Technol Sch Sci Comp Sci Bangkok 10520 Thailand

Imbalanced classification presents a significant challenge in real-world datasets, requiring innovative solutions to enhance performance. This study introduces a hybrid binary classification algorithm designed to effectively address this challenge. The algorithm identifies different data types, pairs them, and trains multiple models, which then vote on predictions using weighted strategies to ensure stable performance and minimize overfitting. Unlike some methods, it is designed to work consistently with both noisy and noise-free datasets, prioritizing overall stability rather than specific noise adjustments. The algorithm's effectiveness is evaluated using Recall, G-Mean, and AUC, measuring its ability to detect the minority class while maintaining balance. The results reveal notable improvements in minority class detection, with Recall outperforming other methods in 16 out of 22 datasets, supported by paired t-tests. The algorithm also shows promising improvements in G-Mean and AUC, ranking first in 17 and 18 datasets, respectively. To further evaluate its performance, the study compares the proposed algorithm with previous methods using G-Mean. The comparison confirms that the proposed algorithm also exhibits strong performance, further highlighting its potential. These findings emphasize the algorithm's versatility in handling diverse datasets and its ability to balance minority class detection with overall accuracy.

关键词： imbalanced classification hybrid algorithm minority class detection

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A bi-level hybrid algorithm for solving multi-target inspection path planning problem of mobile robot in complex radioactive indoor environment

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 266卷

作者： Zhang, De Yin, Yebo Xiong, Guokai Zou, Shuliang Univ South China Sch Resource Environm & Safety Engn Hengyang 421001 Peoples R China Hunan Prov Key Lab Emergency Safety Technol & Equi Hengyang 421001 Peoples R China Univ South China Sch Comp Sci Hengyang 421001 Peoples R China Univ South China Sch Mech Engn Hengyang 421001 Peoples R China

Multi-target inspection path planning (MTIPP) of mobile robot (MR) represents a significant area of research in the context of environmental monitoring and routine inspection of nuclear power plants (NPPs). Given the challenges posed by complex radioactive indoor environments, characterized by the presence of numerous radioactive sources and dense obstacles, a bi-level multi-objective programming framework is proposed to model the MTIPP problem. To navigate this model effectively, a novel bi-level hybrid algorithm named ACO-GA-A* that integrates improved ant colony optimization (IACO), genetic algorithm (GA) with modified A* algorithm is developed. In the upper level, ACO with a GA-based non-uniform initial pheromone distribution, an adaptive heuristic function and an elite strategy for pheromone update is employed to determine the optimal traversal sequence of inspection targets. In the lower level, a modified A* algorithm, which considers multiple constraints including path length, risk degree and energy consumption, is utilized to plan pairwise paths between targets, thereby generating cost graphs. Comparative simulation experiments are conducted in various complexity radioactive scenarios. The results indicate that the modified A* can plan pairwise paths with lower total costs in shorter time compared to traditional A*, ACO, and GA. Furthermore, the ACO-GA-A* demonstrates better sensitivity, reliability, and convergence characteristics compared to some other bi-level hybrid algorithms. Subsequent real-world experimentation corroborates the effectiveness and feasibility of both the bi-level programming framework for MTIPP and the proposed ACO-GA-A* algorithm.

关键词： Mobile robots (MRs) Radioactive environment hybrid algorithm Ant colony optimization-genetic algorithm-A* (ACO-GA-A*)

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Advanced hybrid algorithm for estimation of circularity deviation in coordinate metrology

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MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING 2025年第1期33卷

作者： Gsim, Jamal Jalid, Abdelilah Es-Sadek, Mohamed Zeriab Mohammed V Univ Rabat M2CS Lab ENSAM Rabat Morocco Mohammed V Univ Rabat PCMT Lab ENSAM Rabat Morocco

In the field of precision mechanics such as automobiles or aeronautics, the estimation of defects in the shape of mechanical parts is a very common task, and the declaration of conformity is mainly based on the precision of this value. This research introduces an innovative hybrid optimization methodology that enhances the precision of estimation of circularity defects. The method proposed combines the genetic algorithm (GA) and the interior point method (IPM), this approach overcomes the limitations of traditional methods like least squares and orthogonal distance regression, which rely on initial estimates parameters to ensure convergence. The GA-IPM hybrid eliminates the need for such initial guesses, leading to faster convergence and more accurate results. To control a mechanical part, a cloud of points is preleved by using a coordinate measuring machines, after treatment of those datasets the estimation of circularity defects is calculated using the proposed algorithm GA-IPM. To validate our approach a comparative study is carried out according to the standard ISO 10 360-6, which consists of comparing the results (substitute geometry parameters) obtained with those of National Institute of Standards and Technology considered as reference values. Several examples have been processed, circle totally or partially measured in order to verify the robustness of the algorithm in terms of convergence and calculation precision, the circularity defect is subsequently calculated on the basis of these parameters. The comparison shows that our method estimates the parameters and the circularity defect well without needing a vector of initial estimates parameters. Given the results obtained, this hybrid approach represents a contribution to the estimation of the circularity deviation, thus offering industries a reliable, precise method to ensure the declaration of conformity of mechanical parts.

关键词： circular form deviation CMM circularity genetic algorithm interior point method hybrid algorithm

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Sure robust global versus robust global almost sure convergence: Implementing a dynamic random coefficient selection in a quaternion-hysteresis-based distributed hybrid algorithm

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NONLINEAR ANALYSIS-hybrid SYSTEMS 2025年 56卷

作者： Zhang, Dandan Jin, Xin Su, Hongye Zhejiang Univ Inst Cyber Syst & Control Coll Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China Fudan Univ Res Inst Intelligent Complex Syst Shanghai 200433 Peoples R China

By employing Lyapunov-like conditions along with the stochastic invariance principle, we develop a quaternion-hysteresis-based distributed stochastic hybrid feedback algorithm aimed at achieving robust global attitude synchronization, addressing both sure and almost sure convergence. The distributed hybrid algorithm integrates a hybrid control variable that experiences continuous changes and instantaneous resets according to a unit quaternion-based reset rule and the associated hysteresis-based conditions. In this hysteresis-based hybrid framework, by treating the reset coefficients as random variables, these coefficients introduce the unique source of randomness;these coefficients are dynamically and randomly selected by the reset rule, leading to varying hysteresis half-widths and, consequently, different nominal levels of robustness;the orientation of the spring force, which reversely pulls the rigid body along the rotation axis to prevent the unwinding phenomenon, is determined by the deterministic sign of the hybrid control variable, whereas the hysteresis half-widths can delay this pulling. By dynamically and randomly adjusting the stochastic hysteresis half-widths, we create a flexible tradeoff between mitigating the amount of unwinding and enhancing the nominal level of robustness to measurement noise. By imposing different constraints on the support domain of random reset coefficients, the hybrid algorithm achieves both sure robust global and robust global almost sure attitude synchronization.

关键词： Attitude synchronization hybrid algorithm Robustness Lyapunov-like conditions Stochastic invariance principle

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