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Fractional wavelet combined with multi-scale morphology and PCNN hybrid algorithm for grayscale image fusion

SIGNAL IMAGE AND VIDEO PROCESSING 2024年第SUPPL 1期18卷 141-155页

作者： Xie, Minghang Zhang, Chenyang Liu, Ziyun Yang, Xiaozhong North China Elect Power Univ Inst Informat & Computat Sch Math & Phys Beijing 102206 Peoples R China North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China

Grayscale image fusion is an important part of the digital image processing field, which is important for the integration of image information. This paper proposes a hybrid algorithm that addresses the problem of unclear edges caused by traditional image fusion algorithms. The proposed algorithm combines the discrete fractional wavelet transform with multi-scale morphology and the pulse coupled neural network. The hybrid algorithm employs discrete fractional wavelet transform to decompose the source images and obtain subbands that include both high- and low-frequency components. An image enhancement method, enhanced by multi-scale morphological operations, is developed to process the low-frequency subband. Additionally, a simplified pulse coupled neural network method is employed to adapt the high-frequency components and generate the high-frequency decision map. Fused images show that proposed algorithm effectively suppresses the Gibbs effect. Simulation experiments confirm that the fusion effect of the hybrid algorithm in this paper is better than the existing five classical algorithms, indicating that the hybrid algorithm is an efficient grayscale image fusion method.

关键词： Grayscale image fusion Discrete fractional wavelet transform (DFRWT) Multi-scale morphology Pulse coupled neural network (PCNN) hybrid algorithm

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Optimization and performance assessment of Multi-Tuned Mass Dampers (MTMD) to mitigate seismic pounding of adjacent buildings via a novel hybrid algorithm

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JOURNAL OF BUILDING ENGINEERING 2025年 103卷

作者： Djerouni, Salah Bekdas, Gebrail Nigdeli, Sinan Melih Univ Biskra Lab Genie Energet & Mat LGEM BP 145 Biskra 07000 Algeria Istanbul Univ Cerrahpasa Dept Civil Engn TR-34320 Istanbul Turkiye

This paper addresses the vibration reduction of adjacent buildings using multiple-tuned mass dampers (MTMDs) compared with single-tuned mass dampers (STMD). The study establishes two control schemes for MTMDs. The first is an MTMD installed at the top floor (MTMDs at the top);the second is an MTMD distributed along the floor level (d-MTMDs). In order to exploit the performance of the control schemes, the determination of the optimum parameters such as mass, damping, and stiffness is recommended. A new hybrid metaheuristic algorithm called (PSO-GWO) that integrated the specific advantages of two metaheuristics (particle swarm optimizer and grey wolf optimizer) is proposed for the optimization process. To the best of the authors' knowledge, a hybrid algorithm (PSO-GWO) has never been discussed in the optimization problem of controlling adjacent buildings. Once the optimal design parameters of three control schemes, including the STMD, are found, their effectiveness and robustness regarding unprotected adjacent buildings (NC) in the frequency domain and time domain are assessed under a catalog of 462 earthquake records with different frequency contents. The main value of interest includes interstory drift of buildings 1 and 2, pounding distance, base shear demand, and stroke device demand. The process result showed that d-MTMDs mitigate pounding risk, interstory drift, and base shear demand more effectively than STMD and MTMDs at the top. This higher reduction of the d-MTMDs is achieved at the expanse of the yielding stroke device of one of the TMDs installed below the top floor.

关键词： Multiple tuned mass dampers Design optimization Earthquake excitation hybrid algorithm Grey wolf optimizer Particle swarm optimizer Adjacent buildings Optimal parameters

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hybrid algorithm for retinal blood vessel segmentation using different pattern recognition techniques

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第6期43卷 7605-7615页

作者： Sindhusaranya, B. Geetha, M. R. Rajesh, T. Kavitha, M. R. Ponjesly Coll Engn Dept Elect & Commun Engn Nagercoil Tamil Nadu India PSN Coll Engn & Technol Dept Elect & Commun Engn Tirunelveli Tamil Nadu India

Blood vessel segmentation of the retina has become a necessary step in automatic disease identification and planning treatment in the field of Ophthalmology. To identify the disease properly, both thick and thin blood vessels should be distinguished clearly. Diagnosis of disease would be simple and easier only when the blood vessels are segmented accurately. Existing blood vessel segmentation methods are not supporting well to overcome the poor accuracy and low generalization problems because of the complex blood vessel structure of the retina. In this study, a hybrid algorithm is proposed using binarization, exclusively for segmenting the vessels from a retina image to enhance the exactness and specificity of segmentation of an image. The proposed algorithm extracts the advantages of pattern recognition techniques, such as Matched Filter (MF), Matched Filter with First-order Derivation of Gaussian (MF-FDOG), Multi-Scale Line Detector (MSLD) algorithms and developed as a hybrid algorithm. This algorithm is authenticated with the openly accessible dataset DRIVE. Using Python with OpenCV, the algorithm simulation results had attained an accurateness of 0.9602, a sensitivity of 0.6246, and a specificity of 0.9815 for the dataset. Simulation outcomes proved that the proposed hybrid algorithm accurately segments the blood vessels of the retina compared to the existing methodologies.

关键词： hybrid algorithm blood vessel segmentation first-order derivation of Gaussian matched filter multi-scale line detector

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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 hybrid algorithm of 3-D Explicit Unconditionally Stable FDTD and Traditional FDTD Methods

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IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS 2024年第12期23卷 4653-4657页

作者： He, Xinbo Chen, Meng Wei, Bing Xidian Univ Sch Phys Xian 710071 Peoples R China

The explicit unconditionally stable finite-difference time-domain (EUS-FDTD) algorithm has the characteristics of explicit iteration and unconditional stability. It exhibits high efficiency in addressing electromagnetic issues with fine structures. However, it is necessary to solve partial eigenvalues and eigenvectors of the system matrix, which is inefficient when there are many unknown variables. This letter proposes a hybrid algorithm of EUS-FDTD and traditional FDTD (T-FDTD) for the three-dimensional case. The EUS-FDTD method is employed to solve the electromagnetic fields near the fine structure, and the T-FDTD algorithm is used in the remaining areas. Field value correction and exchange are performed at the regional boundaries of the two algorithms, which greatly reduces the scale of the eigenvalue solution and improves calculation efficiency. Numerical experiments demonstrate the effectiveness of the proposed method.

关键词： finite-difference time-domain hybrid algorithm Explicit unconditionally stable finite-difference time-domain hybrid algorithm

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A Novel hybrid algorithm Based on Beluga Whale Optimization and Harris Hawks Optimization for Optimizing Multi-Reservoir Operation

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WATER RESOURCES MANAGEMENT 2024年第12期38卷 4883-4909页

作者： Shen, Xiaohui Wu, Yonggang Li, Lingxi He, Peng Zhang, Tongxin Huazhong Univ Sci & Technol Coll Civil & Hydraul Engn 1037 Luoyu Rd Wuhan 430074 Hubei Peoples R China

Reservoir operation exhibits traits of nonlinearity, numerous constraints, and nonconvexity. As the number of reservoirs, such as series and parallel reservoirs, increases, the complexity of the reservoirs also increases. This study solves the complex problem using a new hybrid algorithm (MBWOHHO) based on a modified Beluga whale optimization (BWO) with Harris hawks optimization (HHO). First, in the initialization phase, an opposition-based learning strategy (OBL) is incorporated. This strategy reconstructs the initial spatial position of the population using pairwise comparisons to obtain a higher-quality initial population. Then, a differential mechanism is devised during the global search phase. This strategy enhances global exploration capabilities by cross-combining local optimal individuals with ordinary individuals. Finally, BWO and HHO are organically integrated via a population-based mechanism. This strategy effectively maximizes the strengths of both algorithms while maintaining a balance between exploration and exploitation. Several experiments are conducted across various types and complexities of benchmark functions, including 18 classical and 14 CEC2014 functions. The results of the three cascade reservoir optimization experiments show that MBWOHHO has obvious advantages over the comparison algorithms.

关键词： Beluga whale optimization Harris hawks optimization hybrid algorithm Four-reservoir operation Ten-reservoir operation

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Optimal Design of PMa-SynRM for Electric Vehicles Using a Subregion-Assisted hybrid algorithm with Adaptive Nelder-Mead Simplex

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2024年第7期19卷 4103-4116页

作者： Lee, Ji-Sung Lim, Dong-Kuk Univ Ulsan Dept Elect Elect & Comp Engn Ulsan 44610 South Korea

The optimal design of a motor can be characterized as a multi-modal optimization problem because numerous performance factors must be considered. Moreover, a time-consuming finite element method (FEM) is essential for precise analysis of motor characteristics. Especially in multi-modal problems, computation time is increased. This article proposes a subregion-assisted hybrid algorithm (SAHA) and its hybridization with the adaptive Nelder-Mead simplex (ANM) algorithm to address the computational burden in multi-modal problems. The SAHA method acts as a global search method to reduce meaningless function calls and to enhance the ability to find undiscovered optima using the subregion and region exclusive space-filling method (RESM). In addition, the ANM acts as a local search method, enabling rapid convergence and reducing function calls using information obtained from the SAHA. The performance of the proposed algorithm was validated through comparison with the niching genetic algorithm (NGA), which is a conventional multi-modal optimization algorithm. Then, the proposed algorithm was applied to the optimal design of a permanent-magnet-assisted synchronous reluctance motor (PMa-SynRM) for electric vehicle traction to reduce torque ripple while considering the average torque and efficiency. Finally, irreversible demagnetization analysis and stress analysis were performed to verify the effectiveness of the selected optimal model.

关键词： Design optimization Electric vehicle hybrid algorithm Multimodal Permanent magnet assisted synchronous reluctance motors Traction motors

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Reactor lightweight shielding optimization method based on parallel embedded genetic particle-swarm hybrid algorithm

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PROGRESS IN NUCLEAR ENERGY 2024年 168卷

作者： Zheng, Songchuan Pan, Qingquan He, Donghao Liu, Xiaojing Shanghai Jiao Tong Univ Coll Smart Energy Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Sch Nucl Sci & Engn Shanghai 200240 Peoples R China

Reactor lightweight shielding design is a multi-objective optimization problem, which must balance multidimensional design parameters such as dose, weight and volume. Combining the strong global search ability of the genetic algorithm (GA) and the fast convergence speed of the particle swarm optimization (PSO) algorithm, we proposed a Parallel Embedded Genetic Particle-swarm hybrid algorithm (PEGPHA) for reactor lightweight shielding optimization. After parallelizing GA and PSO generation by generation, reorganizing the population for each generation, and updating the key parameters in the iterative equations, the respective advantages of GA and PSO are balanced. Applied and tested on a small helium-xenon cooled reactor, PEGPHA has a higher convergence rate than GA and has a higher optimization efficiency than PSO. The number of ideal solutions obtained by PEGPHA is 1.23 times higher than GA, 2.22 times higher than PSO, and the average fitness of the ideal solutions is reduced by 2.9% and 2.8% compared with GA and PSO, respectively. The average optimization depth of PEGPHA is also shown to be 1.40 times and 2.10 times higher than that of GA and PSO. PEGPHA provides a larger ideal space for choosing high-quality solutions that are lighter in weight and smaller in volume while meeting dose limits, showing great application potential.

关键词： Lightweight shielding Genetic algorithm Particle swarm optimization Parallel embedded genetic particle swarm hybrid algorithm

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hybrid algorithm Based on Phasor Particle Swarm Optimization and Firefly algorithm 14th

Hybrid Algorithm Based on Phasor Particle Swarm Optimization...

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14th International Conference on Swarm Intelligence (ICSI)

作者： Chen, Peilin Wu, Chenhan Liu, Xiaole Wang, Yongjin Whittle Sch & Studios Shenzhen Campus Shenzhen 518067 Peoples R China Coll Int Educ Shenzhen 518048 Peoples R China Shenzhen Univ Coll Management Shenzhen 518060 Peoples R China

ISBN: (纸本)9783031366215;9783031366222

This paper proposes a new hybrid algorithm (HBPPSO) based on the Phasor Particle Swarm Optimization (PPSO) and the Firefly algorithm (FA) to improve particle velocity and position updates. Initially, a trigonometric updating approach is presented for nonlinearly adjusting particle velocity. The particle position is then controlled using an exponential updating technique. Following that, six groups of experiments are performed to validate the optimization performance of the proposed HFPPSO method. The phasor particle swarm optimization algorithm and particle swarm optimization are chosen as the comparison algorithms. Finally, the experimental findings show that HBPPSO unifies the strengths of managing the learning parameters in both PPSO and FA algorithms while alleviating the weakness of premature convergence, improving searching accuracy and avoiding slipping into local optimum.

关键词： hybrid algorithm Firefly algorithm Phasor Particle Swarm Optimization Trigonometric Update Exponential Update

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A MODIFIED hybrid algorithm FOR SOLVING PSEUDOMONOTONE EQUILIBRIUM PROBLEMS

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2024年第2期86卷 25-32页

作者： Suparatulatorn, Raweerote Department of Mathematics Faculty of Science and Office of Research Administration Chiang Mai University Chiang Mai50200 Thailand

This work modifies a hybrid algorithm that combines the subgradient ex-tragradient algorithm with the inertial technique to solve a pseudomonotone equilibrium problem with a Lipschitz-like condition in a real Hilbert space. A strong convergence theorem is established under certain mild conditions for the bifunction and the control parameters. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： hybrid algorithm inertial technique process innovation pseudomonotone equilibrium problem

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