Computer Methods in Applied Mechanics and Engineering, Год журнала: 2024, Номер 431, С. 117251 - 117251
Опубликована: Авг. 16, 2024
Язык: Английский
Results in Engineering, Год журнала: 2025, Номер unknown, С. 104215 - 104215
Опубликована: Фев. 1, 2025
Язык: Английский
Процитировано
2
Scientific Reports, Год журнала: 2024, Номер 14(1)
Опубликована: Дек. 30, 2024
The study suggests a better multi-objective optimization method called 2-Archive Multi-Objective Cuckoo Search (MOCS2arc). It is then used to improve eight classical truss structures and six ZDT test functions. aims minimize both mass compliance simultaneously. MOCS2arc an advanced version of the traditional (MOCS) algorithm, enhanced through dual archive strategy that significantly improves solution diversity performance. To evaluate effectiveness MOCS2arc, we conducted extensive comparisons with several established algorithms: MOSCA, MODA, MOWHO, MOMFO, MOMPA, NSGA-II, DEMO, MOCS. Such comparison has been made various performance metrics compare benchmark efficacy proposed algorithm. These comprehensively assess algorithms' abilities generate diverse optimal solutions. statistical results demonstrate superior evidenced by Additionally, Friedman's & Wilcoxon's corroborate finding consistently delivers compared others. show highly effective improved algorithm for structure optimization, offering significant promising improvements over existing methods.
Язык: Английский
Процитировано
14
Advanced Engineering Informatics, Год журнала: 2024, Номер 62, С. 102783 - 102783
Опубликована: Авг. 28, 2024
Процитировано
12
Scientific Reports, Год журнала: 2024, Номер 14(1)
Опубликована: Окт. 22, 2024
The increasing complexity and high-dimensional nature of real-world optimization problems necessitate the development advanced algorithms. Traditional Particle Swarm Optimization (PSO) often faces challenges such as local optima entrapment slow convergence, limiting its effectiveness in complex tasks. This paper introduces a novel Hybrid Strategy (HSPSO) algorithm, which integrates adaptive weight adjustment, reverse learning, Cauchy mutation, Hook-Jeeves strategy to enhance both global search capabilities. HSPSO is evaluated using CEC-2005 CEC-2014 benchmark functions, demonstrating superior performance over standard PSO, Dynamic Adaptive Inertia Weight PSO (DAIW-PSO), Hummingbird Flight patterns (HBF-PSO), Butterfly Algorithm (BOA), Ant Colony (ACO), Firefly (FA). Experimental results show that achieves optimal terms best fitness, average stability. Additionally, applied feature selection for UCI Arrhythmia dataset, resulting high-accuracy classification model outperforms traditional methods. These findings establish an effective solution
Язык: Английский
Процитировано
11
Advances in Engineering Software, Год журнала: 2025, Номер 203, С. 103862 - 103862
Опубликована: Фев. 6, 2025
Язык: Английский
Процитировано
1
Applied Thermal Engineering, Год журнала: 2024, Номер 256, С. 124052 - 124052
Опубликована: Июль 29, 2024
Язык: Английский
Процитировано
6
Mathematics, Год журнала: 2025, Номер 13(3), С. 405 - 405
Опубликована: Янв. 26, 2025
The Kepler optimization algorithm (KOA) is a metaheuristic based on Kepler’s laws of planetary motion and has demonstrated outstanding performance in multiple test sets for various issues. However, the KOA hampered by limitations insufficient convergence accuracy, weak global search ability, slow speed. To address these deficiencies, this paper presents multi-strategy fusion (MKOA). Firstly, initializes population using Good Point Set, enhancing diversity. Secondly, Dynamic Opposition-Based Learning applied individuals to further improve its exploration effectiveness. Furthermore, we introduce Normal Cloud Model perturb best solution, improving rate accuracy. Finally, new position-update strategy introduced balance local search, helping escape optima. MKOA, uses CEC2017 CEC2019 suites testing. data indicate that MKOA more advantages than other algorithms terms practicality Aiming at engineering issue, study selected three classic cases. results reveal demonstrates strong applicability practice.
Язык: Английский
Процитировано
0
Scientific Reports, Год журнала: 2025, Номер 15(1)
Опубликована: Янв. 31, 2025
Chatter, a type of self-excited vibration, deteriorates surface quality and reduces tool life machining efficiency. Chatter detection serves as an effective approach to achieve stable cutting. To address the low accuracy in chatter caused by limitations both one-dimensional temporal two-dimensional image modal information, this study proposes multi-modal denoised data-driven milling method using optimized hybrid neural network architecture. A data denoising model combining Complementary Ensemble Empirical Mode Decomposition (CEEMD) Singular Value (SVD) is established. The Ivy algorithm employed optimize hyperparameters CEEMD-SVD. Multi-modal features different states are then obtained time–frequency domain methods Markov transition field methods. Sensitivity analysis conducted Pearson correlation coefficient analysis. (DBMA) for constructed integrating dual-scale parallel convolutional networks, bidirectional gated recurrent units, multi-head attention mechanisms. utilized DBMA. t-SNE visualize extracted from layers model. Results demonstrate that signals use can significantly improve state detection. Compared with other methods, proposed exhibits superior stability robustness.
Язык: Английский
Процитировано
0
Scientific Reports, Год журнала: 2025, Номер 15(1)
Опубликована: Фев. 6, 2025
The Snow Goose Algorithm (SGA) is a new meta-heuristic algorithm proposed in 2024, which has been proved to have good optimization effect, but there are still problems that easy fall into local optimal and premature convergence. In order further improve the performance of algorithm, this paper proposes an improved (ISGA) based on three strategies according real migration habits snow geese: (1) Lead goose rotation mechanism. (2) Honk-guiding (3) Outlier boundary strategy. Through above strategies, exploration development ability original comprehensively enhanced, convergence accuracy speed improved. paper, two standard test sets IEEE CEC2022 CEC2017 used verify excellent algorithm. practical application ISGA tested through 8 engineering problems, employed enhance effect clustering results show compared with comparison faster iteration can find better solutions, shows its great potential solving problems.
Язык: Английский
Процитировано
0
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0