Research on tugboat scheduling optimization model considering the reliability of tugboat matching scheme

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Апрель 7, 2025

The selection and scheduling of tugboat matching schemes are key tasks in assistance operation management. With large ships requiring more assistance, a two-stage multi-criteria decision-making method is proposed. This includes normal distribution-based multi-attribute group with triangular fuzzy numbers to determine scheme reliability. A planning model for multiple berthing bases then established, targeting the minimization total fuel cost bi-objective problem solved using posteriori method, actual data from Nansha Port validating proposed method. Meanwhile, priority-based encoding Memetic algorithm designed address characteristics problem, solution results 25 test cases generated range Guangzhou compared analyzed CPLEX, genetic algorithms, simulated annealing algorithms. verify feasibility algorithm. enhanced helps decision-makers quickly select suitable optimize scheduling, demonstrating effective reliability evaluation optimization.

Язык: Английский

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An adaptive hierarchical hybrid kernel ELM optimized by aquila optimizer algorithm for bearing fault diagnosis

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Апрель 8, 2025

As a critical component of rotating machinery, the operating status rolling bearings is not only related to significant economic interests but also has far-reaching impact on social security. Hence, ensuring an effective diagnosis faults in paramount maintaining operational integrity. This paper proposes intelligent bearing fault method that improves classification accuracy using stacked denoising autoencoder (SDAE) and adaptive hierarchical hybrid kernel extreme learning machine (AHHKELM). First, (HKELM) initially constructed, leveraging SDAE's deep network architecture for automatic feature extraction. The functions address limitations single by effectively capturing both linear nonlinear patterns data. Subsequently, (HHKELM) refined through enhanced Aquila Optimizer (AO) algorithm, which iteratively optimizes hyperparameter combination. AO algorithm further incorporating chaos mapping, implementing balanced search strategy, fine-tuning parameter [Formula: see text], collectively improve its ability escape local optima conduct global searches, thus strengthening robustness model during optimization. Experimental results CWRU , MFPT JNU datasets demonstrate autoencoder-adaptive (SDAE-AHHKELM) better accuracy, robustness, generalization than KELM other methods.

Язык: Английский

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Enhancing neurological disease diagnostics: fusion of deep transfer learning with optimization algorithm for acute brain stroke prediction using facial images

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Апрель 10, 2025

Язык: Английский

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Hierarchical multi step Gray Wolf optimization algorithm for energy systems optimization

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Март 15, 2025

Gray Wolf Optimization (GWO), inspired by the social hierarchy and cooperative hunting behavior of gray wolves, is a widely used metaheuristic algorithm for solving complex optimization problems in various domains, including engineering design, image processing, machine learning. However, standard GWO can suffer from premature convergence sensitivity to parameter settings. To address these limitations, this paper introduces Hierarchical Multi-Step (HMS-GWO) algorithm. HMS-GWO incorporates novel hierarchical decision-making framework that more closely mimics observed wolf packs, enabling each type (Alpha, Beta, Delta, Omega) execute structured multi-step search process. This approach enhances exploration exploitation, improves solution diversity, prevents stagnation. The performance evaluated on benchmark suite 23 functions, showing 99% accuracy, with computational time 3 s stability score 0.9. Compared other advanced techniques such as GA, PSO, MMSCC-GWO, WCA, CCS-GWO, demonstrates significantly better performance, faster improved accuracy. While suffers convergence, mitigates issue employing process diversity. These results confirm outperforms terms both speed quality, making it promising across domains enhanced robustness efficiency.

Язык: Английский

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