Optimization Algorithm Research Articles

Novel application of sinh cosh optimizer for robust controller design in hybrid photovoltaic-thermal power systems

Load frequency control (LFC) is critical for maintaining stability in interconnected power systems, addressing frequency deviations and tie-line power fluctuations due to system disturbances. Existing methods often face challenges, including limited robustness, poor adaptability to dynamic conditions, and early convergence in optimization. This paper introduces a novel application of the sinh cosh optimizer (SCHO) to design proportional–integral (PI) controllers for a hybrid photovoltaic (PV) and thermal generator-based two-area power system. The SCHO algorithm’s balanced exploration and exploitation mechanisms enable effective tuning of PI controllers, overcoming challenges such as local minima entrapment and limited convergence speeds observed in conventional metaheuristics. Comprehensive simulations validate the proposed approach, demonstrating superior performance across various metrics. The SCHO-based PI controller achieves faster settling times (e.g., 1.6231 s and 2.4615 s for frequency deviations in Area 1 and Area 2, respectively) and enhanced robustness under parameter variations and solar radiation fluctuations. Additionally, comparisons with the controllers based on the salp swarm algorithm, whale optimization algorithm, and firefly algorithm confirm its significant advantages, including a 25–50% improvement in integral error indices (IAE, ITAE, ISE, ITSE). These results highlight the SCHO-based PI controller’s effectiveness and reliability in modern power systems with hybrid and renewable energy sources.

CDCG-UNet: Chaotic Optimization Assisted Brain Tumor Segmentation Based on Dilated Channel Gate Attention U-Net Model.

Brain tumours are one of the most deadly and noticeable types of cancer, affecting both children and adults. One of the major drawbacks in brain tumour identification is the late diagnosis and high cost of brain tumour-detecting devices. Most existing approaches use ML algorithms to address problems, but they have drawbacks such as low accuracy, high loss, and high computing cost. To address these challenges, a novel U-Net model for tumour segmentation in magnetic resonance images(MRI) is proposed. Initially, images are claimed from the dataset and pre-processed with the Probabilistic Hybrid Wiener filter (PHWF) to remove unwanted noise and improve image quality. To reduce model complexity, the pre-processed images are submitted to a feature extraction procedure known as 3D Convolutional Vision Transformer (3D-VT). To perform the segmentation approach usingchaotic optimization assisted Dilated Channel Gate attention U-Net (CDCG-UNet)model to segment brain tumour regions effectively. The proposed approach segments tumour portions as whole tumour (WT), tumour Core (TC), and Enhancing Tumour (ET) positions. The optimization loss function can be performed using the Chaotic Harris Shrinking Spiral optimization algorithm (CHSOA). The proposed CDCG-UNet model is evaluated with three datasets: BRATS 2021, BRATS 2020, and BRATS 2023. For the BRATS 2021 dataset, the proposed CDCG-UNet model obtained a dice score of 0.972 for ET, 0.987 for CT, and 0.98 for WT. For the BRATS 2020 dataset, the proposed CDCG-UNet model produced a dice score of 98.87% for ET, 98.67% for CT, and 99.1% for WT. The CDCG-UNet model is further evaluated using the BRATS 2023 dataset, which yields 98.42% for ET, 98.08% for CT, and 99.3% for WT.

Path planning for unmanned surface vehicles in anchorage areas based on the risk-aware path optimization algorithm

Path planning for unmanned surface vehicles in anchorage areas based on the risk-aware path optimization algorithm

Health state assessment method for complex system based on multiexpert joint belief rule base

The health of complex systems continues to decline as they operate over long periods of time, so it is important to assess the health state of complex systems. Belief rule base (BRB) is widely used in the field of health state assessment of complex systems as a semi-quantitative method that can address uncertainty effectively and with interpretability. In practical engineering, BRB still has problems: the incompleteness of expert knowledge and the inconsistency of the cognitive abilities of each expert have an effect on the construction of the model and interpretability. To address this problem, a complex system health state assessment method is proposed based on a joint multiexpert belief rule base (BRB-ME). Experts first build their own models, and a new multiexpert knowledge fusion algorithm is designed for the fusion of different expert models. The ER is used as the inference machine for the model. Next, a multi-population evolution whale optimization algorithm with multiexpert knowledge constraints (C-MEWOA) is used to optimize the BRB-ME model. Finally, the effectiveness of the BRB-ME model in health state assessment is verified through case studies of lithium-ion batteries and flywheels. Comparative studies have shown that the BRB-ME model can fuse multiexpert knowledge and has advantages in terms of the stability and accuracy of assessment results.

Voltage profile enhancement of an AVR system using Ant Lion Optimization algorithm

Voltage profile enhancement of an AVR system using Ant Lion Optimization algorithm

Coati optimization algorithm for brain tumor identification based on MRI with utilizing phase-aware composite deep neural network

ABSTRACT Brain tumors can cause difficulties in normal brain function and are capable of developing in various regions of the brain. Malignant tumours can develop quickly, pass through neighboring tissues, and extend to further brain regions or the central nervous system. In contrast, healthy tumors typically develop slowly and do not invade surrounding tissues. Individuals frequently struggle with sensory abnormalities, motor deficiencies affecting coordination, and cognitive impairments affecting memory and focus. In this research, Utilizing Phase-aware Composite Deep Neural Network Optimized with Coati Optimized Algorithm for Brain Tumor Identification Based on Magnetic resonance imaging (PACDNN-COA-BTI-MRI) is proposed. First, input images are taken from the brain tumour Dataset. To execute this, the input image is pre-processed using Multivariate Fast Iterative Filtering (MFIF) and it reduces the occurrence of over-fitting from the collected dataset; then feature extraction using Self-Supervised Nonlinear Transform (SSNT) to extract essential features like model, shape, and intensity. Then, the proposed PACDNN-COA-BTI-MRI is implemented in Matlab and the performance metrics Recall, Accuracy, F1-Score, Precision Specificity and ROC are analysed. Performance of the PACDNN-COA-BTI-MRI approach attains 16.7%, 20.6% and 30.5% higher accuracy; 19.9%, 22.2% and 30.1% higher recall and 16.7%, 21.9% and 30.8% higher precision when analysed through existing techniques brain tumor identification using MRI-Based Deep Learning Approach for Efficient Classification of Brain Tumor (MRI-DLA-ECBT), MRI-Based Brain Tumor Detection using Convolutional Deep Learning Methods and Chosen Machine Learning Techniques (MRI-BTD-CDMLT) and MRI-Based Brain Tumor Image Detection using CNN-Based Deep Learning Method (MRI-BTID-CNN) methods, respectively.

An Improved Whale Optimization Algorithm for the Integrated Scheduling of Automated Guided Vehicles and Yard Cranes

An Improved Whale Optimization Algorithm for the Integrated Scheduling of Automated Guided Vehicles and Yard Cranes

Multi-Strategy Hybrid Dung Beetle Optimization Algorithm for Parameter Identification in Photovoltaic Systems

Multi-Strategy Hybrid Dung Beetle Optimization Algorithm for Parameter Identification in Photovoltaic Systems

Direct torque tolerant control of five-phase permanent magnet synchronous motor based on super-twisting sliding mode tuned by ISSA

ABSTRACT This paper proposes a fault-tolerant direct torque control strategy based on an improved sparrow search algorithm (ISSA) and super-twisting sliding mode control to address torque ripple and speed response issues in direct torque control (DTC) of five-phase permanent magnet synchronous motor (PMSM). By minimising torque ripple and optimising speed response under load conditions, the traditional dual-hysteresis DTC method is improved, reducing steady-state torque ripple and overshoot in transient speed response. To handle complete rotor position sensor failures, a fault-tolerant control scheme is proposed. A super-twisting sliding mode controller is designed for the flux and torque loops of the DTC, and the improved sparrow search algorithm optimises three controller parameters for the inner and outer loops. The impact of controller complexity on the computational burden is discussed. This ensures that both speed and torque can quickly recover to normal operation after significant disturbances, effectively eliminating chattering. Simulation results demonstrate that the proposed method significantly reduces steady-state torque ripple, achieves rapid speed response, and provides fault tolerance in the event of rotor position sensor failure.

A new approach for fire and non-fire aerosols discrimination based on multilayer perceptron trained by modified bonobo optimizer

Fire smoke detection plays a pivotal role in our life. For the optical fire smoke detector, it is easy to produce false alarms due to the misidentification of non-fire aerosols, such as dust and water mist. In this research, four multilayer perceptron (MLP) models are established and applied to discriminate the fire smokes and non-fire aerosols. A new hybrid algorithm (HBOBES) is proposed to optimize the parameters of MLP, which incorporates three search phases, including space selecting, promiscuous and restrictive mating, and consortship and extra-group mating, which are derived from the basic bonobo optimizer (BO) and bald eagle search algorithm (BES). Moreover, a adaptive tent chaos mapping technique is introduced into the first two phases to increase the population diversity. In the experiments, eight standard classification datasets and sixteen self-established aerosol classification datasets are applied to evaluate HBOBES’s optimization performance on training the MLP models. The results show that the proposed HBOBES ranks first overall, showing the merits in training MLP models compared to eight other algorithms. For the aerosol classification problem, it is found that both the optimized 3-7-2 and 3-7-3 MLP models have achieved the highest classification accuracy of approximately 95%. Black smokes can be identified with 100% classification accuracy. Therefore, this paper provides an effective and feasible approach to distinguish fire and non-fire aerosols by using the MLP classifier optimized by optimization algorithm, which has practical significance for the development of intelligent optical smoke detector.

A Novel Approach for Evaluating Web Page Performance Based on Machine Learning Algorithms and Optimization Algorithms

A Novel Approach for Evaluating Web Page Performance Based on Machine Learning Algorithms and Optimization Algorithms

Critical vector based evolutionary algorithm for large-scale multi-objective optimization

Critical vector based evolutionary algorithm for large-scale multi-objective optimization

Newton-type algorithms for inverse optimization: weighted bottleneck Hamming distance and $$\ell _\infty$$-norm objectives

Newton-type algorithms for inverse optimization: weighted bottleneck Hamming distance and $$\ell _\infty$$-norm objectives

Zoning design of cemented sand and gravel dam materials based on DeepFit–memory gradient search–particle swarm optimization fitting optimization

Cemented sand and gravel (CSG) dams are preferred for their cost-effectiveness and adaptability. This study introduces a zoning fitting optimization design method aimed at improving material strength utilization and reducing construction costs. Using Latin sampling and reduced strength safety calculations, an implicit function relationship is established between zoning parameters and safety through an improved deep neural network (DNN). Subsequent dimensionality reduction facilitates optimization using the memory gradient search–particle swarm optimization (MGS-PSO) algorithm. The method's efficacy is demonstrated by optimizing zoning parameters for Japan's Tobetsu Dam, achieving significant improvements in safety and material use. This approach increases safety by 22% and reduces cementing dosage by 36% compared to traditional methods, underscoring its potential in enhancing dam stability and efficiency.

V-shaped and S-shaped binary artificial protozoa optimizer (APO) algorithm for wrapper feature selection on biological data

V-shaped and S-shaped binary artificial protozoa optimizer (APO) algorithm for wrapper feature selection on biological data

Optimal integration of photovoltaic sources and capacitor banks considering irradiance, temperature, and load changes in electric distribution system

This paper introduces the Efficient Metaheuristic BitTorrent (EM-BT) algorithm, aimed at optimizing the placement and sizing of photovoltaic renewable energy sources (PVRES) and capacitor banks (CBs) in electric distribution networks. The main goal is to minimize energy losses and enhance voltage stability over 24 h, taking into account varying load profiles, solar irradiance, and temperature effects. The algorithm is rigorously tested on standard distribution networks, including the IEEE 33, IEEE 69, and ZB-ALG-Hassi Sida 157-bus systems. The results reveal that EM-BT outperforms established methods like Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA), demonstrating its effectiveness in reducing energy losses and maintaining stable voltage profiles. By effectively combining PVRES and CBs, this research highlights a robust approach to enhancing both technical performance and operational reliability in distribution systems. Additionally, the consideration of temperature effects on PVRES efficiency adds depth to the study, making it a valuable contribution to the field of power system optimization.

Multiconfigurational short-range on-top pair-density functional theory.

We present the theory and implementation of a fully variational wave function-density functional theory (DFT) hybrid model, which is applicable to many cases of strong correlation. We denote this model as the multiconfigurational self-consistent on-top pair-density functional theory (MC-srPDFT) model. We have previously shown how the multiconfigurational short-range DFT (MC-srDFT) hybrid model can describe many multiconfigurational cases of any spin symmetry and also state-specific calculations on excited states [Hedegård et al., J. Chem. Phys. 148(21), 214103 (2018)]. However, the srDFT part of the MC-srDFT has some deficiencies that it shares with Kohn-Sham DFT; in particular, (1) self-interaction errors (albeit reduced because of the range separation), (2) that different MS states incorrectly become non-degenerate, and (3) that singlet and non-singlet states dissociating to the same open-shell fragments incorrectly lead to different electronic energies at dissociation. The model that we present in this paper corrects these deficiencies by introducing the on-top pair density as an auxiliary variable replacing the spin density. Unlike other models in the literature, our model is fully variational and employs a long-range version of the on-top pair density. The implementation is a second-order optimization algorithm ensuring robust convergence to both ground and excited states. We show how MC-srPDFT solves the mentioned challenges by sample calculations on the ground state singlet curve of H2, N2, and Cr2 and the lowest triplet curves for N2 and Cr2. Furthermore, the rotational barrier for ethene is investigated for the S0 and T1 states. The calculations show correct degeneracy between the singlet and triplet curves at dissociation and the results are invariant to the choice of the MS value for the triplet curves.

A Combined Calibration Method for Workpiece Positioning in Robotic Machining Systems and a Hybrid Optimization Algorithm for Improving Tool Center Point Calibration Accuracy

A Combined Calibration Method for Workpiece Positioning in Robotic Machining Systems and a Hybrid Optimization Algorithm for Improving Tool Center Point Calibration Accuracy

Solving single- and multi-objective optimal power flow problems using the spider wasp optimization algorithm

Solving single- and multi-objective optimal power flow problems using the spider wasp optimization algorithm

Finite element-based nonlinear dynamic optimization of nanomechanical resonators

Nonlinear dynamic simulations of mechanical resonators have been facilitated by the advent of computational techniques that generate nonlinear reduced order models (ROMs) using the finite element (FE) method. However, designing devices with specific nonlinear characteristics remains inefficient since it requires manual adjustment of the design parameters and can result in suboptimal designs. Here, we integrate an FE-based nonlinear ROM technique with a derivative-free optimization algorithm to enable the design of nonlinear mechanical resonators. The resulting methodology is used to optimize the support design of high-stress nanomechanical Si3N4 string resonators, in the presence of conflicting objectives such as simultaneous enhancement of Q-factor and nonlinear Duffing constant. To that end, we generate Pareto frontiers that highlight the trade-offs between optimization objectives and validate the results both numerically and experimentally. To further demonstrate the capability of multi-objective optimization for practical design challenges, we simultaneously optimize the design of nanoresonators for three key figure-of-merits in resonant sensing: power consumption, sensitivity and response time. The presented methodology can facilitate and accelerate designing (nano) mechanical resonators with optimized performance for a wide variety of applications.