Search Results (52)

A robust speed controller integrating the slime mold algorithm (SMA) with sliding mode theory (SMT) is proposed for induction motor (IM) drives operating under field-oriented control (FOC). Unlike conventional controllers with fixed gain parameters, the proposed exponential reaching law sliding mode controller (ERLSMC) defines the sliding mode dynamic trajectory control gain, exponential reaching gain, and constant-speed reaching gain as the search space for the SMA. An adaptive fitness function based on the speed error and its rate of change is constructed to continuously evaluate and update these gain parameters, thereby determining the optimal controller gains according to the current operating state. Consequently, larger gain values are assigned when the system state is far from the sliding mode dynamic trajectory to accelerate the reaching process, whereas smaller gain values are adopted near the sliding mode dynamic trajectory to suppress chattering and reduce overshoot. Matlab/Simulink (2024b version) simulations are conducted to evaluate the proposed controller in an IM drive system and compare its performance with constant-speed reaching law sliding mode control, exponential reaching law sliding mode control, and zebra optimization algorithm (ZOA)-based ERLSMC methods. The simulation results demonstrate that the proposed controller achieves superior performance in both speed command tracking and load regulation response. Full article

Real-world information diffusion frequently spans multiple heterogeneous platforms and relational layers, making multilayer influence maximization (MLIM) a critical and challenging problem. Existing methods for multilayer networks often rely on local structural signals for surrogate evaluation, failing to accurately characterize multi-hop diffusion and inter-layer coupling effects. In discrete combinatorial search, meta-heuristic random exploration often disrupts the structural inheritance and reuse of effective node configurations, compromising search stability and quality. To address these challenges, this paper proposes a Probabilistic-Based Multilayer Slime Mold Algorithm (PB-MSMA). It employs the slime mold algorithm as its search framework to perform discrete combinatorial optimization within a controlled candidate space. It utilizes the Preference-based Expected Diffusion Value (P-EDV) as a surrogate fitness metric during the search phase. This design reduces the need for repeated Monte Carlo simulations for iterative candidate evaluation while improving the characterization of inter-layer and higher-order diffusion effects. Furthermore, a probabilistic pipeline mechanism is introduced to encode recurring effective node configurations from historical searches as statistical priors, guiding the search process to enhance structural inheritance and stability. After the seed sets are obtained, the final influence spread of all compared methods is evaluated using 10,000 Monte Carlo simulations under the MLIC model. Experiments on six real-world multilayer network datasets and nine seed budgets show that PB-MSMA achieves a dataset-level improvement range of 3.68–14.50% over representative baselines, including CELF, DPSOMIM, Degree, DIRCI, and PRGC, with an average improvement of 10.32%. These results indicate that PB-MSMA provides an efficient seed-selection strategy for multilayer diffusion scenarios where repeated simulation-based evaluation is costly. Full article

Optimized dispatch of microgrids is crucial for improving the economic performance and long-term sustainability of modern low-carbon power systems. In particular, accurate economic dispatch modeling for battery energy storage systems (BESSs) is essential for properly evaluating the operational benefits and lifetime costs of microgrids. However, when both battery cycle aging and calendar aging are considered, the resulting scheduling model becomes highly nonlinear, high-dimensional, non-convex, and multimodal, which poses substantial challenges to conventional optimization methods. To alleviate the above problem, a symmetry-guided multi-strategy differential hybrid slime mold algorithm (MDHSMA) is introduced for the day-ahead economic dispatch of microgrids under a refined battery degradation framework. First, a chaotic bimodal mirrored Latin hypercube sampling strategy is designed to exploit symmetry during population initialization, thereby enhancing diversity and improving structured coverage of the search space. Second, a history-driven adaptive differential evolution mechanism is integrated to balance global exploration and local exploitation more effectively during the iterative search process. Third, a state-aware stagnation handling framework is incorporated to maintain population vitality and further improve convergence accuracy and robustness. MDHSMA is evaluated against 12 state-of-the-art optimizers on the CEC2017 and CEC2022 benchmark suites and two representative engineering optimization problems to verify its overall performance. In addition, it is applied to a microgrid case study with refined BESS degradation modeling. The results show that MDHSMA achieves the lowest comprehensive operating cost by effectively coordinating electricity arbitrage and battery life consumption. Moreover, it guides the energy storage system toward shallow charge–-discharge patterns, thereby mitigating accelerated degradation caused by excessive cycling. These results confirm the effectiveness and practical value of the proposed method for sustainable microgrid dispatch in complex nonconvex optimization scenarios. Full article

This paper introduces a novel metric-driven evaluation framework for Remaining Useful Life (RUL) prognosis in rotating machinery, featuring robust uncertainty quantification. Accurate RUL prediction is vital for optimizing maintenance and preventing failures, but existing methods often struggle with complex nonlinear degradation or lack reliable uncertainty estimates. Our proposed framework integrates a probabilistic Deep State Space Model (DSSM) with a variational inference approach to model complex, non-linear degradation trends and inherent aleatoric uncertainty. A key innovation is the use of the Slime Mold Algorithm (SMA) for efficient hyperparameter optimization, ensuring maximum accuracy. Furthermore, an online adaptation mechanism, governed by a heuristic reinforcement learning agent, allows the model to continuously update its knowledge and adapt to concept drift in real-time. Experimental validation on the IMS bearing dataset demonstrates superior RUL prediction accuracy, evidenced by the lowest Root Mean Square Error (RMSE) of 8.1829 cycles, and a PICP of 0.59416. This dual capability makes the framework highly suitable for real-world predictive maintenance, enhancing safety and reliability. Full article

During deep and ultra-deep oil and gas drilling, downhole high-temperature and high-pressure conditions significantly affect the measurement accuracy of piezoresistive pressure sensors. To improve measurement accuracy under such extreme conditions, this study proposes an intelligent temperature compensation method based on a Modified Slime Mold Algorithm (MSMA). An experimental platform covering the full operating range of 0–175 °C and 0–170 MPa was established to acquire sensor outputs, and samples were collected at various temperature and pressure points to construct a dataset. Key parameters of the compensation model were optimized using the MSMA, enhancing the model’s fitting capability. Results indicate that, after compensation, the sensor exhibits a maximum full-scale error of 0.26% and a maximum sensitivity drift of −0.019% FS/°C, significantly reducing errors compared with traditional interpolation and polynomial fitting methods. The optimized compensation model was further deployed on an embedded hardware platform, enabling high-precision temperature compensation in an engineering context. Experimental data demonstrate that the embedded implementation maintains compensation accuracy while meeting real-time application requirements, making it suitable for downhole pressure monitoring and for output correction of other intelligent sensors operating under complex field conditions. Full article

The marine centrifugal pump is one of the most energy-intensive pieces of equipment in ship auxiliary machinery, and the efficient design of its hydraulic components can effectively reduce the total energy consumption of the ship system. Aiming at the complex three-dimensional twisted blade profile structure of the marine centrifugal pump, this paper optimized the clonal selection algorithm and constructed an automatic hydraulic optimization design method for the high-efficiency centrifugal pump impeller. Considering the multi-condition operation characteristics of the marine centrifugal pump, a performance test platform for the marine centrifugal pump was built, and the actual operating conditions of the model pump were tested to obtain its performance characteristics under operating conditions. The numerical simulation method was employed to capture and analyze the internal flow field and flow characteristics of the model pump. Addressing the design challenges of the marine centrifugal pump impeller, which involve multiple parameters with significant interactions, a traditional clonal selection algorithm was enhanced using a Slime Mold Algorithm, and a hybrid Clonal Selection Algorithm integrated with Slime Mold and Tangent Flight mechanisms was established. Based on the MATLAB and ANSYS platforms, an automated hydraulic optimization design framework for the centrifugal pump impeller was established. Using the optimized clonal selection algorithm, with the operational efficiency of the model pump as the optimization objective and controlling ten key geometric parameters of the blade profile through Bézier curves, the blade profile optimization design was achieved. The pump hydraulic efficiency under the rated flow condition increased by 7%. The unsteady internal flow efficiency of the optimized marine centrifugal pump was significantly improved. The blade optimization alleviated flow separation phenomena on the tangential surface of the impeller and in partial regions of the volute, reduced the flow loss area, and significantly decreased overall flow losses. Full article

This paper suggests a novel approach based on machine learning (ML) and deep learning (DL) for medical image classification in a fast and accurate manner. The proposed method merges the strengths of the convolutional neural network (CNN) using the VGG19 model for feature extraction with an artificial neural network (ANN) classifier for medical dataset classification. The suggested model is improved by applying the slime mold algorithm (SMA) to the task of feature selection and the particle swarm optimization (PSO) approach to optimize the ANN classifier. PSO is a crucial component in neural network design to optimize the ANN setup and hyperparameters. Through adjustments to the bias and weight parameters, the PSO approach enhances the ANN method’s ability to classify medical images. The experiments were conducted on the LC25000 histopathological dataset, which comprises 25,000 histopathological images of lung and colon cancer tissue, partitioned into five classes, each with 5000 images: lung benign tissue, lung adenocarcinoma, lung squamous cell carcinoma, colon adenocarcinoma, and colon benign tissue. The results demonstrated that the suggested model (CNN-PSO-ANN) does better at illness detection than ANN alone. The proposed model is evaluated utilizing several metrics, like accuracy, RMSE, and MAE. The accuracy rate is 94.1% when ANN is utilized independently, while the percentage increases to 98.8% when PSO is employed with the ANN. Additionally, the proposed model is compared with other medical data classification systems that utilize PSO and neural networks. The proposed model (CNN-PSO-ANN) performed better than the other models. With the suggested CNN-PSO-ANN model, diseases, especially cancer, can be found and treated earlier and better. Full article

Accurate and robust bearing fault diagnosis is crucial for the reliability of rotating machinery. To improve the precision of bearing fault classification, this study introduces a novel methodology that integrates the Adaptive t-distribution Slime Mold Algorithm (AtSMA) with the Least Squares Support Vector Machine (LSSVM). During the signal processing phase, Local Mean Decomposition (LMD) is employed to extract intrinsic mode functions from bearing vibration signals, which are subsequently reconstructed using the Pearson correlation coefficient method. Key features, such as sample entropy, permutation entropy, and energy entropy, are calculated to create a comprehensive feature vector for fault diagnosis. To enhance the convergence stability and global exploration capabilities of the Slime Mold Algorithm (SMA), an adaptive t-distribution mutation mechanism is incorporated to increase population diversity. Additionally, an improved step size strategy is implemented to prevent premature convergence and to expedite optimization speed. AtSMA is utilized to optimize the kernel parameters and penalty factor of LSSVM, thereby enhancing fault classification accuracy. Experimental evaluations conducted on two benchmark bearing datasets reveal that the proposed method achieves an average diagnostic accuracy of 96% on the Case Western Reserve University (CWRU) dataset and 93.25% on the Xi’an Jiaotong University dataset, surpassing conventional optimization algorithms and diagnostic techniques. These findings substantiate the superior diagnostic precision and robustness of the proposed approach under various fault scenarios and dynamic operating conditions. Full article

This study suggests a hybrid approach that combines improved feature selection and intelligent diagnosis to increase the operational safety and intelligent diagnosis capabilities of hydropower units. In order to handle the vibration data, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used initially. A novel comprehensive index is constructed by combining the Pearson correlation coefficient, mutual information (MI), and Kullback–Leibler divergence (KLD) to select intrinsic mode functions (IMFs). Next, feature extraction is performed on the selected IMFs using Refined Composite Multiscale Fluctuation Dispersion Entropy (RCMFDE). Then, time and frequency domain features are screened by calculating dispersion and combined with IMF features to build a hybrid feature vector. The vector is then fed into a CNN-GRU-Attention model for intelligent diagnosis. The improved slime mold algorithm (ISMA) is employed for the first time to optimize the hyperparameters of the CNN-GRU-Attention model. The experimental results show that the classification accuracy reaches 96.79% for raw signals and 93.33% for noisy signals, significantly outperforming traditional methods. This study incorporates entropy-based feature extraction, combines hyperparameter optimization with the classification model, and addresses the limitations of single feature selection methods for non-stationary and nonlinear signals. The proposed approach provides an excellent solution for intelligent optimized diagnosis of hydropower units. Full article

The accurate prediction of joint shear strength is crucial for rock mass engineering design and geological hazard assessment. However, traditional machine learning (ML) models often suffer from local optima and limited generalization ability when dealing with complex nonlinear problems, thereby compromising prediction accuracy and stability. To address these challenges, this study proposes a hybrid ML model that integrates a multilayer perceptron (MLP) with the slime mold algorithm (SMA), termed the SMA-MLP model. While MLP exhibits strong nonlinear mapping capability, SMA enhances its training process through global optimization and parameter tuning, thereby improving predictive accuracy and robustness. A dataset with five input variables was constructed to evaluate the performance of the SMA-MLP model comprehensively. The proposed model was compared with other ML models. The results indicate that SMA-MLP outperforms these models in key metrics such as the root mean squared error (RMSE) and the correlation coefficient (R²), achieving an R² of 0.97 and an RMSE as low as 0.10 MPa on the test set. Furthermore, feature importance analysis reveals that normal stress has the most significant influence on joint shear strength. This study demonstrates the superiority of SMA-MLP in predicting joint shear strength, highlighting its potential as an efficient and accurate tool for rock mass engineering analysis and providing reliable technical support for geological hazard assessment. Full article

To address the limitation of current upper limb rehabilitation exoskeletons—where pattern recognition-based assistance disrupts patients’ continuous motion—this study proposes a mechanomyography-based model for predicting shoulder and elbow joint angles. Small contact microphones were employed to collect mechanomyography signals, leveraging their ability to capture vibration signals above 8 Hz, making them ideal for mechanomyography acquisition. After extracting raw mechanomyography data, a bandpass filter (10–50 Hz) was applied to eliminate low- and high-frequency noise. To reduce computational overhead during model training, a Broad Learning System was adopted, which iteratively refines predictions by incrementally expanding nodes in the feature and enhancement layers rather than adding hidden layers. The Slime Mold Algorithm was further used to optimize hyperparameters of the Broad Learning System, enhancing prediction accuracy. Experimental results demonstrate that mechanomyography signals exhibit a typical central frequency range of 10–50 Hz, and the Slime Mold Algorithm-optimized Broad Learning System model achieved a minimum coefficient of determination (R²) of 0.978, effectively predicting arm joint angles. This approach shows promise for exoskeletons, combining high control accuracy, real-time joint angle prediction, and computational efficiency. Full article

In addressing the challenges posed by the sluggish convergence rate, suboptimal stability, and susceptibility to local optimization in function optimization problems, a multi-strategy-based enhanced slime mold optimization algorithm (MSSMA) has been proposed. This algorithm integrates chaotic mapping and inverse learning to enhance the convergence speed of the initial population. Additionally, a novel balancing factor, B, has been introduced to ensure a more equitable distribution of the algorithm’s exploration and exploitation. The enhanced Lévy flight strategy and the elite tangent search strategy have been integrated to further enhance the algorithm’s global search capability and optimization finding ability. The simulation experiments have demonstrated that the enhanced algorithm exhibits faster convergence speed, enhanced stability, and a superior ability to escape local optima when compared to the other five algorithms in 50 benchmark test functions and multi-UAV cooperative path planning scenarios. Full article

Multi-UAV path planning presents a critical challenge in Unmanned Aerial Vehicle (UAV) applications, particularly in environments with various obstacles and restrictions. These conditions transform multi-UAV path planning into a complex optimization problem with multiple constraints, significantly reducing the number of feasible solutions and complicating the generation of optimal flight trajectories. Although the slime mold algorithm (SMA) has proven effective in optimization missions, it still suffers from limitations such as inadequate exploration capacity, premature convergence, and a propensity to become stuck in local optima. These drawbacks degrade its performance in intricate multi-UAV scenarios. This study proposes a self-adaptive improved slime mold algorithm called AI-SMA to address these issues. Firstly, AI-SMA incorporates a novel search mechanism to balance exploration and exploitation by integrating ranking-based differential evolution (rank-DE). Then, a self-adaptive switch operator is introduced to increase population diversity in later iterations and avoid premature convergence. Finally, a self-adaptive perturbation strategy is implemented to provide an effective escape mechanism, facilitating faster convergence. Extensive experiments were conducted on the CEC 2017 benchmark test suite and multi-UAV path-planning scenarios. The results show that AI-SMA improves the quality of optimal fitness by approximately $7.83\%$ over the original SMA while demonstrating superior robustness and effectiveness in generating collision-free trajectories. Full article

This study is dedicated to the development of a multifunctional device that integrates air conditioning, humidification, and air purification functions, aimed at meeting the demands for energy efficiency, space-saving, and comfortable indoor environments in modern residential and commercial settings. The research focuses on achieving a balance between performance, energy consumption, and noise levels by combining bionic design principles with advanced optimization algorithms to propose innovative design and optimization methods. Specific methods include the establishment and optimization of mathematical models for air conditioning, air purification, and humidification functions. The air conditioning module employs a nonlinear programming model optimized through the Parrot Optimizer (PO) Algorithm to achieve uniform temperature distribution and minimal energy consumption. The air purification function is based on a bionic model and optimized using the Deep ACO Algorithm to ensure high efficiency and low noise levels. The humidification function utilizes a mist diffusion model optimized through the Slime Mold Algorithm (SMA) to enhance performance. Ultimately, a multi-objective optimization model is constructed using the Beluga Whale Optimization (BWO), successfully integrating the three main functions and designing a compact segmented cylindrical device that achieves a balance of high efficiency and multifunctionality. The optimization results indicate that the device exhibits superior performance, with a Clean Air Delivery Rate (CADR) of 400 m³/h, a humidification rate of 1.2 kg/h, a temperature uniformity index of 0.08, and a total power consumption controlled within 1600 W. This study demonstrates the significant potential of bionic design and optimization technology in the development of multifunctional indoor environment control devices, enhancing not only the overall performance of the device but also the comfort and sustainability of the indoor environment. Future work will focus on system scalability, experimental validation, and further optimization of bionic characteristics to expand the device’s applicability and enhance its environmental adaptability. Full article

Tailings ponds can recycle water resources through the water recirculation system by clarifying and purifying the wastewater discharged from the mining production process. Due to factors such as flooding and heavy rainfall, once a tailings dams burst, the spread of heavy metals in the tailings causes underground and surface water pollution, endangering the lives and properties of people downstream. To effectively assess the potential impact of tailings dams bursting, many problems such as the difficulty of taking values in predicting the volume of silt penetration through empirical formulae, model testing, and numerical simulation need to be solved. In this study, 65 engineering cases were collected to develop a sample dataset containing dam height and storage capacity. The Support Vector Machine (SVM) algorithm was used to develop a nonlinear regression model for tailings discharge volume after tailings dam failure. In addition, the model penalty parameter C and kernel function g were optimized using the powerful global search capability of the Slime Mold Algorithm (SMA) to develop an SMA–SVM prediction model for tailings discharge volume. The results indicate that the volume of tailings discharged increases nonlinearly with increasing dam height and tailings storage capacity. The SMA-SVM model showed higher prediction accuracy compared to the predictions made by the Random Forest (RF), Radial Basis Function (RBF), and Least Squares SVM (LS-SVM) algorithms. The average absolute error in tailings discharge volume compared to actual values was 30,000 m³, with an average relative error of less than 25%. This is very close to practical engineering scenarios. The ability of the SMA-SVM optimization algorithm to produce predictions with minimal error relative to actual values was further confirmed by the combination of numerical simulations. In addition, the numerical simulations revealed the flow characteristics and inundation area of the discharged sediment during tailings dam failure, and the research results can provide reference for water resource protection and downstream safety prevention and control of tailings ponds. Full article