Optimization Algorithm Research Articles

Metaheuristic-enhanced Deep Learning Model for Accurate Alzheimer's Disease Diagnosis from MRI Imaging

Alzheimer’s Disease (AD) is the neuro-degenerative dementia, where the precise and early recognition of AD is vital for timely treatment to reduce mortality rate. A new automated model is implemented in this work for early discovery of AD in the Magnetic Resonance Imaging (MRI) brain scans. Initially, the input brain scans are taken from the Alzheimer's disease Neuroimaging Initiative (ADNI) database. Further, the acquired raw brain scans are visually improved by employing the binary normalization technique. The denoised brain scans are fed to the pre-trained Convolutional Neural Network (CNN) named GoogleNet for feature extraction. Next, the extracted richer feature values are fed to the Long Short Term Memory (LSTM) network for classifying the brain scan as Normal Control (NC), Mild Cognitive Impairment (MCI) and AD. In this manuscript, a Honey Badger Optimization Algorithm (HBOA) technique is incorporated with the LSTM networks for hyper-parameters optimization, where this procedure helps in diminishing the LSTM network’s complexity and computational time. The experimental results conducted on the ADNI database underscore the HBOA-based LSTM network's effectiveness, showcasing a remarkable mean classification accuracy of 97.83% in multi-class classification. Moreover, the sensitivity of HBOA based LSTM for AD/NC is 96.73% which is high when compared to the existing methodologies such as SVM with radial basis kernel function and NCSINs. This performance surpasses that of other comparative models for AD detection, emphasizing the superior capabilities and potential of the proposed method in the early detection.

Classifying congenital brain anomalies using deep learning enabled Hunter-Squirrel Search Optimization

ABSTRACT Fetal brain anomalies are probably the most well-known inborn deformities that might be related to syndromic and chromosomal contortion. Early pre-birth location of cerebrum irregularities is fundamental for enhancing clinical management pathways and counselling for parents. To address this issue, Hunter Squirrel Search Optimization (HSSO)_LeNet enabled congenital brain anomaly classification is devised in this work. Commonly, an acquired input image is allowed to the preprocessing stage, which is accomplished by the Weiner filter. The preprocessed ultrasound image is allowed for the segmentation process, where image segmentation is done through 3D U-Net. Thereafter, the segmented image is allowed into the feature extraction process. Then, the brain anomaly classification is carried out using Deep Convolutional Neural Network (DCNN), where the hyperparameters are optimally tuned using the proposed HSSO. Consequently, the proposed HSSO algorithm is devised by incorporating the Hunter – Prey Optimizer (HPO) algorithm and the Squirrel Search Algorithm (SSA). Finally, the classification of congenital brain anomalies is carried out by LeNet, which is tuned by the proposed HSSO. The proposed technique has attained an accuracy of 93.79%, a True positive rate (TPR) of 94.67%, and a True Negative Rate (TNR) of 92.15%.

A hybrid swarm intelligent optimization algorithm for antenna design problems

Meta-heuristic optimization algorithms have seen significant advancements due to their diverse applications in solving complex problems. However, no single algorithm can effectively solve all optimization challenges. The Naked Mole-Rat Algorithm (NMRA), inspired by the mating patterns of naked mole-rats, has shown promise but suffers from poor convergence accuracy and a tendency to get trapped in local optima. To address these limitations, this paper proposes an enhanced version of NMRA, called Salp Swarm and Seagull Optimization-based NMRA (SSNMRA), which integrates the search mechanisms of the Seagull Optimization Algorithm (SOA) and the Salp Swarm Algorithm (SSA). This hybrid approach improves the exploration capabilities and convergence performance of NMRA. The effectiveness of SSNMRA is validated through the CEC 2019 benchmark test suite and applied to various electromagnetic optimization problems. Experimental results demonstrate that SSNMRA outperforms existing state-of-the-art algorithms, offering superior optimization capability and enhanced convergence accuracy, making it a promising solution for complex antenna design and other electromagnetic applications.

Research on Rapid and Non-Destructive Detection of Coffee Powder Adulteration Based on Portable Near-Infrared Spectroscopy Technology

This study explores the feasibility of using portable near-infrared spectroscopy for the rapid and non-destructive detection of coffee adulteration. Spectral data from adulterated coffee samples in the 900–1700 nm range were collected and processed using five preprocessing methods. For qualitative detection, the Support Vector Machine (SVM) algorithm was applied. For quantitative detection, two optimization algorithms, Invasive Weed Optimization (IWO) and Binary Chimp Optimization Algorithm (BChOA), were used for the feature wavelength selection. The results showed that convolution smoothing combined with multiple scattering correction effectively improved the signal-to-noise ratio. SVM achieved 96.88% accuracy for qualitative detection. For the quantitative analysis, the IWO algorithm identified key wavelengths, reducing data dimensionality by 82.46% and improving accuracy by 10.96%, reaching 92.25% accuracy. In conclusion, portable near-infrared spectroscopy technology can be used for the rapid and non-destructive qualitative and quantitative detection of coffee adulteration and can serve as a foundation for the further development of rapid, non-destructive testing devices. At the same time, this method has broad application potential and can be extended to various food products such as dairy, juice, grains, and meat for quality control, traceability, and adulteration detection. Through the feature wavelength selection method, it can effectively identify and extract spectral features associated with these food components (such as fat, protein, or characteristic compounds), thereby improving the accuracy and efficiency of detection, further ensuring food safety and enhancing the level of food quality control.

Accelerated algorithms for convex and non-convex optimization on manifolds

Accelerated algorithms for convex and non-convex optimization on manifolds

Multiple global peaks big bang-big crunch algorithm for multimodal optimization

Multiple global peaks big bang-big crunch algorithm for multimodal optimization

Stability Analysis and Support Optimization of Tunnel Surrounding Rock with Weak Interlayer Based on Catastrophe Theory

A tunnel excavation model with weak interlayers is established to analyze the effect of different angles and thicknesses on the displacement of the surrounding rock. Based on catastrophe theory, a stability criterion for surrounding rock displacement is derived, providing a theoretical framework for evaluating tunnel stability through numerical simulations. Finally, to minimize support construction costs, an optimization model is established by integrating the Particle Swarm Optimization (PSO) algorithm and the Radial Basis Function (RBF) neural network. The model is then validated through engineering projects. The results show that the deformation and stability of the surrounding rock are affected by the interlayer angle and thickness. As the angle increases, the maximum deformation of the surrounding rock gradually transitions from horizontal displacement to vertical displacement, while increased thickness amplifies deformation and concentrates it at the interlayer. The stability of the surrounding rock exhibits catastrophic characteristics, with instability often occurring in the middle and rear sections of the tunnel. Calculation costs are reduced by 88% using the PSO-RBF optimization model, and construction costs decreased by 34.96% after optimizing the support parameters. This study provides theoretical support for the stability analysis and construction optimization of tunnels with weak interlayers.

A Novel Monthly Runoff Prediction Model Based on KVMD and KTCN-LSTM-SA

Due to the uncertainty of meteorological factors and the influence of human activities, the monthly runoff series often exhibit the characteristics of non-stationarity. The appropriate prediction model and the hyperparameters of the model are often difficult to determine, and this affects the model prediction performance. For obtaining the accurate runoff prediction results, a novel prediction model (KVMD-KTCN-LSTM-SA) is proposed. This hybrid model uses Kepler optimization algorithm (KOA)-optimized Variable Mode Decomposition (KVMD), KOA-optimized temporal convolutional network–long short-term memory (TCN-LSTM), and the self-attention (SA) mechanism. KVMD effectively reduces the difficulty of predicting the monthly runoff series, KOA helps to find the optimal hyperparameters of the model, TCN is combined with LSTM, and the SA mechanism effectively increases the performance of the model. Monthly runoff from three hydrological stations in the Hetian River basin and one hydrological station in the Huaihe River basin are predicted with the proposed model, and six models are selected for comparison. The KVMD-KTCN-LSTM-SA model effectively reduces runoff fluctuation and combines the advantages of multiple models and achieves satisfactory runoff prediction results. During the testing period, the proposed model achieves NSE of 0.978 and R2 of 0.982 at Wuluwati station, NSE of 0.975 and R2 of 0.986 at Tongguziluoke station, and NSE of 0.978 and R2 of 0.982 at Jiangjiaji station. The proposed hybrid model provides a new approach for monthly runoff prediction, which is capable of better managing and predicting mid-long-term runoff.

A hybrid differential evolution particle swarm optimization algorithm based on dynamic strategies

A hybrid differential evolution particle swarm optimization algorithm based on dynamic strategies

New comparative approach to multi-level thresholding: chaotically initialized adaptive meta-heuristic optimization methods

Abstract One method aimed at enhancing the performance of meta-heuristic optimization techniques is the incorporation of chaotic systems. Instead of irregular distributions in the search space, chaotic distributions are employed in the initial population of optimization algorithms to improve the efficiency of the search process. This approach enables search agents distributed in a chaotic manner to effectively explore the search space. The initial populations of both the well-established PSO algorithm and the enhanced WSO algorithm, which incorporates advanced search techniques, are distributed in the search space according to the characteristics of Logistic, Chebyshev, Circle, Sine, and Piecewise chaotic maps in this study. The original PSO and WSO algorithms, as well as the resulting chaotically initialized PSO and chaotically initialized WSO algorithms, were tested using 23 benchmark functions. Subsequently, the Otsu method was integrated into the tested optimization algorithms to obtain multi-level thresholding values. These algorithms were applied to five different test images with a manually determined number of thresholds. The results obtained were presented in the study and evaluated using statistical tests.

Investigation and optimal design of band gap tunability in fractal phononic crystals

This study investigates the properties of band gaps of circular core filling fractal phononic crystals (CCFFPCs), specifically focusing on the impact of different filling positions on the frequency of band gaps. The research demonstrates that core filling at the central positions significantly influences the formation and widening of low-frequency band gaps, while filling at corner edges predominantly affects mid-frequency band gaps, and filling at edge centers effectively opens and broadens high-frequency band gaps. These results reveal the relationship between filling positions and band gap tuning, providing a theoretical foundation for precise band gap control across a full frequency range. Moreover, this study is the first to systematically clarify the impact of core filling positions on band gap frequencies, expanding the design strategies for band gaps in fractal phononic crystals. Furthermore, this study employs genetic algorithm optimization to achieve the maximum band gap width at different frequencies, enhancing the practical value of fractal phononic crystals in engineering applications. This research deepens theoretical understanding and provides valuable guidance for optimizing their use in broadband acoustic control and energy harvesting applications.

Optimization of Ship Permanent Magnet Synchronous Motor ADRC Based on Improved QPSO

To address the impact of load variations, external environmental changes, and the tuning of the parameters on Permanent Magnet Synchronous Motors (PMSMs) used in ships, this study proposes an Active Disturbance Rejection Control (ADRC) strategy for PMSMs, optimized by the Quantum-behaved Particle Swarm Optimization (QPSO) algorithm. First, based on the PMSM model, the study addresses the limited disturbance rejection capability of the traditional fal function in the Extended State Observer (ESO) of conventional ADRC. To improve the accuracy of the state observer, the faln function is introduced as a replacement for the traditional fal function. Second, due to the numerous parameters in ADRC, which are difficult to tune, the QPSO algorithm—known for its strong global search capabilities and fast convergence speed—is utilized for parameter optimization. Additionally, the position update formula within the optimization algorithm is revised and optimized. Finally, simulation experiments are conducted using the Matlab/Simulink platform, where practical conditions, such as load fluctuations and random noise, are incorporated. The simulation results demonstrate that, compared to PSO-ADRC control, IPSO-ADRC control, and ICFO-ADRC control, the proposed method offers a superior dynamic response. Specifically, the speed control accuracy is improved by 46.7%, torque ripple is reduced by 50.8%, and harmonic distortion decreases by 23.1%. These results highlight the significant advantages of this method in enhancing system robustness, dynamic response speed, and steady-state accuracy, making it particularly suitable for PMSM control systems in complex dynamic environments, such as those encountered on ships.

Advancing Computational Intelligence: AI-Based Algorithm Design and Optimization in Programming

The research explores AI technique implementations in algorithm optimization and design frameworks to understand their crucial impact on programming challenges and efficiency increases. This investigation analyzes GPU performance through the GPU Benchmarks Compilation dataset while deeply assessing their impact on AI-based algorithm operation. The dataset provides detailed benchmarking information for GPUs that includes computational throughput combined with cost-performance ratios and energy efficiency metrics thereby establishing strong foundations for analyzing AI-driven computational developments. This research investigation uncovered major GPU capability evolutions which demonstrate why GPUs remain important for processing advanced AI processing models. The research unveils fundamental information about GPU evolution which shows how novel GPU developments deliver efficient scaling solutions for executing AI-based computational workloads. The research puts particular emphasis on energy efficiency because it addresses the growing computational needs of AI applications. This research examines the practical implications of its findings for computational intelligence frameworks that will exist in the coming years. The study reviews benchmark patterns to establish methods which optimize algorithm designs when utilizing enhanced GPU technology. The research discovers ways to combine AI methods with upcoming GPU technologies to develop advanced computational solutions that deliver maximum efficiency. The ongoing study supports computational intelligence research through its work to connect artificial intelligence methods with recent advancements in hardware. The research shows how AI-based algorithm optimization methods can propel breakthroughs in programming and problem-solving techniques. Findings from this research create an academic foundation for upcoming studies of GPU performance alongside AI integration which continues to further advance the discipline of computational intelligence with real-world applications.

Mine water inflow prediction model based on variational mode decomposition and gated recurrent units optimized by improved chimp optimization algorithm

Water damage accidents occur frequently in mines in China, and accurate prediction of incoming water has become an important guarantee for the safe and efficient mining of coal resources. To improve the accuracy of mine water prediction, this paper proposes the VMD-iCHOA-GRU mine water prediction model by selecting and improving it according to the previous research results in decomposition method, time series prediction model and optimization algorithm. After processing the raw data and setting the model parameters, MAE, RMSE, MAPE and R2 are selected as the evaluation indexes of prediction accuracy, and VMD-GRU model, iCHOA-GRU model, CHOA-GRU model and GRU model are selected as the comparison models to validate the advantages of the VMD-iCHOA-GRU model in the prediction of mine inrush water. The results show that the VMD-iCHOA-GRU model has the best prediction effect on the trend of water inflow, with the evaluation index values of 0.00862, 0.01059, 0.02189%, 0.87079, respectively, and with the smallest MAE, RMSE, MAPE, and the largest R2, and the highest prediction accuracy of the VMD-iCHOA-GRU model.

A Multi-Objective Nutcracker Optimization Algorithm Based on Cubic Chaotic Map for Numerical Association Rule Mining

Traditional numerical association rule mining optimization algorithms have limitations in handling discrete attributes, and they are susceptible to becoming trapped in local optima, uneven population distribution, and poor convergence. To address these challenges, we propose a multi-objective nutcracker optimization algorithm based on a cubic chaotic map (C-MONOA), specifically designed for mining association rules from mixed data (continuous and discrete). Unlike existing models, C-MONOA leverages a chaotic map for population initialization, alongside Michigan rule encoding, to dynamically optimize feature intervals during the optimization process. This algorithm integrates continuous and discrete data more effectively and efficiently. This article uses support, confidence, Kulc metric, and comprehensibility as evaluation indicators for multi-objective optimization. The experimental results show that C-MONOA performs well in rule scoring and can generate frequent, simple, and accurate rule sets. This study extends the association rule mining method for mixed data, demonstrating high performance and robustness and providing new technical tools for application fields such as market analysis and disease prediction.

VM-YOLO: YOLO with VMamba for Strawberry Flowers Detection

Computer vision technology is widely used in smart agriculture, primarily because of its non-invasive nature, which avoids causing damage to delicate crops. Nevertheless, the deployment of computer vision algorithms on agricultural machinery with limited computing resources represents a significant challenge. Algorithm optimization with the aim of achieving an equilibrium between accuracy and computational power represents a pivotal research topic and is the core focus of our work. In this paper, we put forward a lightweight hybrid network, named VM-YOLO, for the purpose of detecting strawberry flowers. Firstly, a multi-branch architecture-based fast convolutional sampling module, designated as Light C2f, is proposed to replace the C2f module in the backbone of YOLOv8, in order to enhance the network’s capacity to perceive multi-scale features. Secondly, a state space model-based lightweight neck with a global sensitivity field, designated as VMambaNeck, is proposed to replace the original neck of YOLOv8. After the training and testing of the improved algorithm on a self-constructed strawberry flower dataset, a series of experiments is conducted to evaluate the performance of the model, including ablation experiments, multi-dataset comparative experiments, and comparative experiments against state-of-the-art algorithms. The results show that the VM-YOLO network exhibits superior performance in object detection tasks across diverse datasets compared to the baseline. Furthermore, the results also demonstrate that VM-YOLO has better performances in the mAP, inference speed, and the number of parameters compared to the YOLOv6, Faster R-CNN, FCOS, and RetinaNet.

Semantic-based topic multicast aggregated routing algorithm for resource optimization of pub/sub systems in wireless sensor network

Semantic-based topic multicast aggregated routing algorithm for resource optimization of pub/sub systems in wireless sensor network

Hermit Crab Optimizer Approach: Harmonics Reduction of Hybrid Renewable Energy Sources

Micro Grids (MGs) have gained a lot of popularity recently because of their benefits in terms of high transmission efficiency and efficient power conversion. However, the nonlinear properties of renewable energy sources (RESs), the increased usage of power electronic devices, and unanticipated fluctuations in load are what lead to the formation of stability issues in the MG. This manuscript presents the optimization approach to improve the performance of microgrid with renewable energy sources (RES). The proposed method is the Hermit Crab Optimizer (HCO) Algorithm. Here, Solar PV and Wind Turbine is utilized as power sources, supported by DC/DC and AC/DC converters for efficient energy conversion. The main objective of proposed technique is to minimize the THD and maximize the efficiency of system. The HCO algorithm is used to optimize the duty cycle of the DC to DC converters within the system. By then, the proposed method's performance is put into practice using the MATLAB platform, and it is contrasted with a number of existing methods, including Salp Swarm Algorithm (SSA), Ant Lion Optimizer (ALO), and Particle Swarm Optimization (PSO). From the result, the THD of proposed method is -17.75. The THD of existing PSO is 17.81465, ALO is -17.81469 and SSA is -17.81545.

Alzheimer’s Prediction Methods with Harris Hawks Optimization (HHO) and Deep Learning-Based Approach Using an MLP-LSTM Hybrid Network

Background/Objective: Alzheimer’s disease is a progressive brain syndrome causing cognitive decline and, ultimately, death. Early diagnosis is essential for timely medical intervention, with MRI medical imaging serving as a primary diagnostic tool. Machine learning (ML) and deep learning (DL) methods are increasingly utilized to analyze these images, but accurately distinguishing between healthy and diseased states remains a challenge. This study aims to address these limitations by developing an integrated approach combining swarm intelligence with ML and DL techniques for Alzheimer’s disease classification. Method: This proposal methodology involves sourcing Alzheimer’s disease-related MRI images and extracting features using convolutional neural networks (CNNs) and the Gray Level Co-occurrence Matrix (GLCM). The Harris Hawks Optimization (HHO) algorithm is applied to select the most significant features. The selected features are used to train a multi-layer perceptron (MLP) neural network and further processed using a long short-term (LSTM) memory network in order to classify tumors as malignant or benign. The Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset is utilized for assessment. Results: The proposed method achieved a classification accuracy of 97.59%, sensitivity of 97.41%, and precision of 97.25%, outperforming other models, including VGG16, GLCM, and ResNet-50, in diagnosing Alzheimer’s disease. Conclusions: The results demonstrate the efficacy of the proposed approach in enhancing Alzheimer’s disease diagnosis through improved feature extraction and selection techniques. These findings highlight the potential for advanced ML and DL integration to improve diagnostic tools in medical imaging applications.

Gray relational analysis and SBOA-BP for predicting settlement intervals of high-speed railway subgrade

PurposeThe deformation of the roadbed is easily influenced by the external environment to improve the accuracy of high-speed railway subgrade settlement prediction.Design/methodology/approachA high-speed railway subgrade settlement interval prediction method using the secretary bird optimization (SBOA) algorithm to optimize the BP neural network under the premise of gray relational analysis is proposed.FindingsUsing the SBOA algorithm to optimize the BP neural network, the optimal weights and thresholds are obtained, and the best parameter prediction model is combined. The data were collected from the sensors deployed through the subgrade settlement monitoring system, and the gray relational analysis is used to verify that all four influencing factors had a great correlation to the subgrade settlement, and the collected data are verified using the model.Originality/valueThe experimental results show that the SBOA-BP model has higher prediction accuracy than the BP model, and the SBOA-BP model has a wider range of prediction intervals for a given confidence level, which can provide higher guiding value for practical engineering applications.