Cuckoo Search Algorithm Research Articles

A Social Group Optimization Algorithm Using the Laplace Operator for the Economic Dispatch Problem

The economic dispatch (ED) problem focuses on the optimal scheduling of thermal generating units in a power system to minimize fuel costs while satisfying operational constraints. This article proposes a modified version of the social group optimization (SGO) algorithm to address the ED problem with various practical characteristics (such as valve-point effects, transmission losses, prohibited operating zones, and multi-fuel sources). SGO is a population-based metaheuristic algorithm with strong exploration capabilities, but for certain types of problems, it may stagnate in a local optimum due to a potential imbalance between exploration and exploitation. The new version, named SGO-L, retains the structure of the SGO but incorporates a Laplace operator derived from the Laplace distribution into all the iterative solution update equations. This adjustment generates more effective search steps in the solution space, improving the exploration–exploitation balance and overall performance in terms of solution stability and quality. SGO-L is validated on four power systems of small (six-unit), medium (10-unit), and large (40-unit and 110-unit) sizes with diverse characteristics. The efficiency of SGO-L is compared with SGO and other metaheuristic algorithms. The experimental results demonstrate that the proposed SGO-L algorithm is more robust than well-known algorithms (such as particle swarm optimization, genetic algorithms, differential evolution, and cuckoo search algorithms) and other competitor algorithms mentioned in the study. Moreover, the non-parametric Wilcoxon statistical test indicates that the new SGO-L version is more promising than the original SGO in terms of solution stability and quality. For example, the standard deviation obtained by SGO-L shows significantly lower values (6.02 × 10−9 USD/h for the six-unit system, 7.56 × 10−5 USD/h for the 10-unit system, 75.89 USD/h for the 40-unit system, and 4.80 × 10−3 USD/h for the 110-unit system) compared to SGO (0.44 USD/h for the six-unit system, 50.80 USD/h for the 10-unit system, 274.91 USD/h for the 40-unit system, and 1.04 USD/h for the 110-unit system).

Optimal Allocation and Sizing of Battery Energy Storage System in Distribution Network Using Mountain Gazelle Optimization Algorithm

This paper addresses the problem of finding the optimal position and sizing of battery energy storage (BES) devices using a two-stage optimization technique. The primary stage uses mixed integer linear programming (MILP) to find the optimal positions along with their sizes. In the secondary stage, a relatively new algorithm called mountain gazelle optimizer (MGO) is implemented to find the technical feasibility of the solution, such as voltage regulation, energy loss reduction, etc., provided by the primary stage. The main objective of the proposed bi-level optimization technique is to improve the voltage profile and minimize the power loss. During the daily operation of the distribution grid, the charging and discharging behaviour is controlled by minimizing the voltage at each bus. The energy storage dispatch curve along with the locations and sizes are given as inputs to MGO to improve the voltage profile and reduce the line loss. Simulations are carried out in the MATLAB programming environment using an Australian radial distribution feeder, with results showing a reduction in system losses by 8.473%, which outperforms Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and Cuckoo Search Algorithm (CSA) by 1.059%, 1.144%, and 1.056%, respectively. During the peak solar generation period, MGO manages to contain the voltages within the upper boundary, effectively reducing reverse power flow and enhancing voltage regulation. The voltage profile is also improved, with MGO achieving a 0.348% improvement in voltage during peak load periods, compared to improvements of 0.221%, 0.105%, and 0.253% by GWO, WOA, and CSA, respectively. Furthermore, MGO’s optimization achieves a reduction in the fitness value to 47.260 after 47 iterations, demonstrating faster and more consistent convergence compared to GWO (47.302 after 60 iterations), WOA (47.322 after 20 iterations), and CSA (47.352 after 79 iterations). This comparative analysis highlights the effectiveness of the proposed two-stage optimization approach in enhancing voltage stability, reducing power loss, and ensuring better performance over existing methods.

DCTCS stack classifier-an integrated framework leveraging discrete cosine transformation, cuckoo search algorithm and stacked machine learning models for EEG-based eye state classification

DCTCS stack classifier-an integrated framework leveraging discrete cosine transformation, cuckoo search algorithm and stacked machine learning models for EEG-based eye state classification

Drag polar modelling for jet aircraft using 6-DOF model data via cuckoo search algorithm

Drag polar modelling for jet aircraft using 6-DOF model data via cuckoo search algorithm

Wind energy assessment and hybrid micro-grid optimization for selected regions of Saudi Arabia

This study investigates the optimization of wind energy integration in hybrid micro grids (MGs) to address the rising demand for renewable energy, particularly in regions with limited wind potential. A comprehensive assessment of wind energy potential was conducted, and optimal sizing of standalone MGs incorporating photovoltaic (PV) systems, wind turbines (WT), and battery storage (BS) systems was performed for six regions in the Kingdom Saudi Arabia. Wind resource analysis utilizing the Weibull distribution function shows that all regions exhibited Class 1 wind energy characteristics, with average annual wind power densities ranging from 36.74 W/m² to 149.56 W/m², thereby rendering them suitable for small-scale hybrid applications. A multi-strategy serial cuckoo search algorithm was employed to evaluate three distinct configurations, and the results indicated that the integration of PV, WT, and BS yielded the most cost-effective solution for the majority of regions, achieving a levelized cost of energy of 0.148$/kWh and a loss of power supply probability below 0.05%. Notably, alternative configurations demonstrated superior reliability in locations such as Al-Baha, Taif, and Tabuk. The rseults of this study provide valuable insights into the design of scalable, sustainable, and cost-efficient hybrid MGs tailored to regions with low wind potential, thereby contributing to enhanced energy access and economic development in remote locations.

Interval-valued optimal control strategies for vaccine distribution and infection awareness investment in preservation technology

Purpose This study aims to explore the global threat of diseases that affect people, such as diarrheal, Hepatitis B, Rotavirus, Measles diseases, emphasizing the integration of disease vaccines into immunization programs globally as recommended by the World Health Organization, resulting in significant case reductions. Design/methodology/approach Notably, it stresses the necessity of raising awareness about diseases and vaccines through promotional efforts alongside effective inventory management because of vaccine perishability, highlighting preservation techniques and cold storage. Addressing environmental concerns, including carbon emissions from vaccine deterioration, the study proposes green technology investments aligned with Sustainable Development Goals to mitigate these impacts. Additionally, advanced optimization algorithms, including ant colony, modified flower pollination, cuckoo search and particle swarm optimization algorithms, are used to optimize pricing, preservation strategies, green investments and replenishment schedules. The research also uses the concept of interval values to enhance the robustness of the optimization framework. Through numerical experiments, the study demonstrates the effectiveness of this dynamic investment approach, providing empirical validation. Findings Furthermore, sensitivity analysis on critical parameters yields valuable insights for decision-makers, underscoring the importance of dynamically managing vaccine inventory. The study offers practical solutions and managerial insights that can inform policy decisions and strategic planning in disease response efforts. Originality/value This study concludes by emphasizing how creative green technology approaches can help decision-makers manage the social and environmental effects of vaccine inventories in the health care of people.

Optimal tuning of multi-stage PID controller for dynamic frequency control of microgrid system under climate change scenarios

Introduction. In recent years, the use of renewable energy has become essential to preserve the climate from pollution and global warming. To utilize renewable energy more effectively, the microgrid system has emerged, which is a combination of renewable energies such as wind and solar power. However, due to sudden and random climate fluctuations, energy deviation and instability problems have arisen. To address this, storage systems and diesel engines have been incorporated. Nevertheless, this approach has led to another issue: frequency deviation in the microgrid system. Therefore, most recent studies have focused on finding ways to reduce frequency deviation. The goal of this work is to study and compare various improvement methods in terms of frequency deviation. Methodology. We first simulated the microgrid system using the PID controller based on the following algorithms: krill herd algorithm (KHA) and cuckoo search algorithm (CSA). In the second phase, we replaced the PID controller with the multi-stage PID controller and optimized its parameters using the KHA and the CSA. In the final phase, we tested the response of the microgrid system to these methods under a range of influencing factors. Results. The results initially showed the superiority of the KHA over the other algorithms in improving the parameters of the PID controller. In the second phase, the results showed a significant advantage of the multi-stage PID controller in terms of speed and stabilization time, as well as in reducing the frequency deviation compared to the PID controller. Practical value. Based on the tests conducted on the microgrid system, we can conclude that the multi-stage PID controller based on the KHA can be relied upon to solve these types of problems within the microgrid system. References 36, tables 4, figures 10.

Optimized KNN Algorithm for Diabetic Retinopathy Classification with PCA-Based Data Fusion and Cuckoo Search Optimization

Diabetes is a disease that occurs when the body is unable to use the insulin it produces effectively or the body fails to produce enough insulin. One of the most important complications of this disease is diabetic retinopathy (DR), which is considered the main cause of severe visual impairment and blindness. Previous studies have proven that the KNN algorithm is an effective algorithm for solving classification and prediction problems, as the performance of this algorithm rely on determining the value of the K parameter because the inappropriate choice of this value can negatively affect the accuracy of classification. On the other hand, adjusting this value manually is very difficult because this value depends on the state of determining the solution to the problem each time. Therefore, there is still an urgent need to use smart algorithms to adjust this value and obtain an ideal value that ultimately leads to obtaining a very high classification accuracy. In this paper, the Cuckoo Search algorithm was used, which is considered one of the smart and modern algorithms in the field of diagnosis, in addition to applying more than one technique and algorithm to build an integrated system to enhance the accuracy of diagnosis and obtain competitive diagnostic accuracy. The proposed work was implemented using the Debrecen diabetic retinopathy dataset and competitive results were obtained for recall, sensitivity, precision, F1 score, accuracy and specificity (98.05%), (97.30%), (99.01%), (98.70%), (99.70%), and (99.08%), respectively. Our results demonstrate that the Cuckoo Search algorithm is an effective and suitable choice for optimizing the parameters in the KNN algorithm, in addition to enhancing this algorithm to diagnose the disease early and support direct intervention and treatment, and this method lays the foundation for diagnosing other diseases and thus improving patient care in most related fields.

Thermo-Elastic Vibration Analysis of Acoustic Liners Using Polymer-Metal Nanocomposites: Prediction and Optimization of Hybrid Deep Neural Network and Cuckoo Search Algorithm

Thermo-Elastic Vibration Analysis of Acoustic Liners Using Polymer-Metal Nanocomposites: Prediction and Optimization of Hybrid Deep Neural Network and Cuckoo Search Algorithm

An Enhanced Semisteady‐State Jaya Algorithm With a Control Coefficient and a Self‐Adaptive Multipopulation Strategy

This paper introduces the enhanced semisteady‐state Jaya (ESJaya) algorithm, an improved version of the semisteady‐state Jaya (SJaya) algorithm. The ESJaya algorithm uses a control coefficient to regulate the influence of the best solution, achieving a better balance between exploration and exploitation. It also features a self‐adaptive multipopulation strategy and removes absolute value operators in its update equation for improved performance. Using 20 benchmark functions, the ESJaya algorithm is compared with the traditional Jaya, SJaya, and elitism‐based self‐adaptive multipopulation Jaya (SAMPEJaya) algorithms. The ESJaya algorithm ranks first in obtaining optimum values, mean accuracy, and stability, showing better overall performance than the others. The ESJaya algorithm ranks first with an average overall rank of 1.750, followed by Jaya, SAMPEJaya, and SJaya with average overall ranks of 2.650, 2.700, and 2.900, respectively. The Wilcoxon signed‐rank test confirms the statistical significance of the overall rankings. When compared with the Snow Geese Algorithm (SGA), Big Bang–Big Crunch Algorithm (BBBCA), firefly algorithm (FA), bat algorithm (BA), cuckoo search algorithm (CSA), and flower pollination algorithm (FPA), the ESJaya algorithm gives competitive results in terms of obtaining optimum values, mean accuracy, and stability. Using four real‐world engineering problems, the ESJaya algorithm is compared with the recently proposed piranha predation optimization algorithm (PPOA) and four state‐of‐the‐art algorithms, namely, the COLSHADE algorithm, the sCMAgES algorithm, the SASS algorithm, and the EnMODE algorithm. The ESJaya algorithm finds optimum solutions with good accuracy, consistency, and stability. Overall, it has a slower convergence and longer run time due to its multipopulation strategy and its search for the best solution after each solution update. However, it consistently finds more optimal solutions than other improved algorithms.

A Building Carbon Emission Prediction Model Based on Optimized Machine Learning Model

To improve the performance of traditional machine learning model, this study employs the chaotic particle swarm optimization (CPSO) and the fuzzy cuckoo search (FCS) algorithm to enhance the BP neural network and support vector machine (SVM), leading to CPSO-BP and FCS-SVM model, respectively. Simulation results indicate that both optimization algorithms significantly enhance the predictive accuracy of their respective models. The relative error of the FCS-SVM decreases by 2.68%, while the CPSO-BP model reduces by 9.75% compared to their pre-benchmark model. Although the CPSO-BP model demonstrates a more substantial improvement in the simulation, the performance of FCS-SVM remains superior to that of CPSO-BP model. Subsequently, the FCS-SVM model was selected to forecast carbon dioxide emissions in China's construction industry from 2022 to 2025, and the results were thoroughly analyzed. The findings indicate that total carbon emissions are expected to rise over the next four years; however, the growth rate is projected to slow significantly. Additionally, carbon emission intensity is anticipated to continue declining until 2023, although the rate of decline will be minimal.

Solving the maximum cut problem using Harris Hawk Optimization algorithm.

The objective of the max-cut problem is to cut any graph in such a way that the total weight of the edges that are cut off is maximum in both subsets of vertices that are divided due to the cut of the edges. Although it is an elementary graph partitioning problem, it is one of the most challenging combinatorial optimization-based problems, and tons of application areas make this problem highly admissible. Due to its admissibility, the problem is solved using the Harris Hawk Optimization algorithm (HHO). Though HHO effectively solved some engineering optimization problems, is sensitive to parameter settings and may converge slowly, potentially getting trapped in local optima. Thus, HHO and some additional operators are used to solve the max-cut problem. Crossover and refinement operators are used to modify the fitness of the hawk in such a way that they can provide precise results. A mutation mechanism along with an adjustment operator has improvised the outcome obtained from the updated hawk. To accept the potential result, the acceptance criterion has been used, and then the repair operator is applied in the proposed approach. The proposed system provided comparatively better outcomes on the G-set dataset than other state-of-the-art algorithms. It obtained 533 cuts more than the discrete cuckoo search algorithm in 9 instances, 1036 cuts more than PSO-EDA in 14 instances, and 1021 cuts more than TSHEA in 9 instances. But for four instances, the cuts are lower than PSO-EDA and TSHEA. Besides, the statistical significance has also been tested using the Wilcoxon signed rank test to provide proof of the superior performance of the proposed method. In terms of solution quality, MC-HHO can produce outcomes that are quite competitive when compared to other related state-of-the-art algorithms.

Advancements in nutty quality: Segmentation for enhanced monitoring and determination

Segmentation of nut images plays a vital role in computer vision and agricultural applications. Precise segmentation enables the extraction and analysis of essential information about the nuts, supporting quality evaluation, yield estimation, and automated sorting processes. This study explores nuts image segmentation utilizing the cuckoo search algorithm. The cuckoo search algorithm, a nature-inspired optimization technique, is introduced to enhance the segmentation process, potentially optimizing parameters or guiding the segmentation algorithms. Performance evaluation emphasizes metrics such as MSE, IoU, and dice coefficient. CSA (cuckoo search algorithm) demonstrates superior results, showcasing its effectiveness in automated nuts segmentation. This research contributes to the advancement of nut image analysis, providing insights into segmentation methodologies that can enhance automated processes in agriculture and food industry applications. The findings underscore the significance of employing advanced algorithms like CSA for accurate and efficient segmentation of nuts in images.

Process Optimization Using Cuckoo Search Algorithm for the Manufacturing of Resin Transfer Moulded Composite Parts

An in-house coded cuckoo search (CS) optimization algorithm integrated with a finite element simulation model was developed for resin transfer moulding (RTM) process optimization. At first, the mould filling and curing phases, the vital stages of the RTM process were modelled in the COMSOL multi-physics simulator for RTM6 resin – carbon fibre reinforced composite part. Then, the model was imported to the CS optimization algorithm scripted in MATLAB using the COMSOL live link for MATLAB. The CS algorithm was developed for the minimization of dry spot content by finding the optimal positions of gates and vents during the mould-filling stage and the minimization of the thermal gradient by finding the optimal mould temperature during the curing stage. From the optimization results, there was a decrease in dry spot content and thermal gradient with an increase in generations. With the generations, converged-stable-optimal solutions were obtained. A dry spot content of 3% for one gate and one vent and 0.56% for two gates and one vent was obtained for two-dimensional and three-dimensional composite plates, respectively. From the curing phase optimization results, a minimum thermal gradient of 0.86 K with 98.1% degree of cure was obtained for the optimal mould temperature of 433 K. The developed CS algorithm predicted the better-automated mould-fill and cure phase optimal solutions for the studied composite part.

Enhancing Intrusion Detection with Cuckoo Search Optimized XGBoost Classifier for Network Traffic Analysis

An Intrusion Detection System (IDS) is crucial for ensuring network security by identifying and mitigating malicious activities. With the rise in interconnected systems, vulnerabilities have become a significant concern, requiring advanced detection mechanisms. Machine learning techniques, with their ability to identify patterns and anomalies, offer a promising approach for anomaly based IDS. This paper introduces a novel intrusion detection model combining the Cuckoo Search Algorithm (CSA) and the XGBoost classifier. This hybrid approach effectively analyzes network traffic and detects intrusions by leveraging the strengths of both methods. The model is trained and evaluated using the UNSW NB15 dataset, achieving a commendable accuracy of 92.06%. Key contributions include handling missing values, outlier analysis, one hot encoding, and feature scaling to enhance detection performance. The proposed model demonstrates superior results compared to existing techniques, offering an efficient solution for securing network infrastructures.

Optimized Hybrid Deep Learning Framework for Early Detection of Alzheimer's Disease Using Adaptive Weight Selection.

Alzheimer's disease (AD) is a progressive neurological disorder that significantly affects middle-aged and elderly adults, leading to cognitive deterioration and hindering daily activities. Notwithstanding progress, conventional diagnostic techniques continue to be susceptible to inaccuracies and inefficiencies. Timely and precise diagnosis is essential for early intervention. We present an enhanced hybrid deep learning framework that amalgamates the EfficientNetV2B3 with Inception-ResNetV2 models. The models were integrated using an adaptive weight selection process informed by the Cuckoo Search optimization algorithm. The procedure commences with the pre-processing of neuroimaging data to guarantee quality and uniformity. Features are subsequently retrieved from the neuroimaging data by utilizing the EfficientNetV2B3 and Inception-ResNetV2 models. The Cuckoo Search algorithm allocates weights to various models dynamically, contingent upon their efficacy in particular diagnostic tasks. The framework achieves balanced usage of the distinct characteristics of both models through the iterative optimization of the weight configuration. This method improves classification accuracy, especially for early-stage Alzheimer's disease. A thorough assessment was conducted on extensive neuroimaging datasets to verify the framework's efficacy. The framework attained a Scott's Pi agreement score of 0.9907, indicating exceptional diagnostic accuracy and dependability, especially in identifying the early stages of Alzheimer's disease. The results show its superiority over current state-of-the-art techniques. The results indicate the substantial potential of the proposed framework as a reliable and scalable instrument for the identification of Alzheimer's disease. This method effectively mitigates the shortcomings of conventional diagnostic techniques and current deep learning algorithms by utilizing the complementing capabilities of EfficientNetV2B3 and Inception-ResNetV2 by using an optimized weight selection mechanism. The adaptive characteristics of the Cuckoo Search optimization facilitate its application across many diagnostic circumstances, hence extending its utility to a wider array of neuroimaging datasets. The capacity to accurately identify early-stage Alzheimer's disease is essential for facilitating prompt therapies, which are crucial for decelerating disease development and enhancing patient outcomes.

Particle swarm optimization with local search for height-map surface reconstruction from point clouds in reverse engineering

AbstractSurface reconstruction is a classical process in industrial engineering and manufacturing, particularly in reverse engineering, where the goal is to obtain a digital model from a physical object. For that purpose, the real object is typically scanned or digitized and the resulting point cloud is then fitted to mathematical surfaces through numerical optimization. The choice of the approximating functions is crucial for the accuracy of the process. Real-world objects such as manufactured workpieces often require complex nonlinear approximating functions, which are not well suited for standard numerical methods. In a previous paper presented at the ISMSI 2023 conference, we addressed this issue by using manually selected approximation functions via optimization through the cuckoo search algorithm with Lévy flights. Building upon that work, this paper presents an enhanced and extended method for surface reconstruction by using height-map surfaces obtained through a combination of exponential, polynomial and logarithmic functions. A feasible approach for this goal is to consider continuous bivariate distribution functions, which ensures consistent models along with good mathematical properties for the output shapes, such as smoothness and integrability. However, this approach leads to a difficult multivariate, constrained, multimodal continuous nonlinear optimization problem. To tackle this issue, we apply particle swarm optimization, a popular swarm intelligence technique for continuous optimization. The method is hybridized with a local search procedure for further improvement of the solutions and applied to a benchmark of 15 illustrative examples of point clouds fitted to different surface models. The performance of the method is analyzed through several computational experiments. The numerical and graphical results show that the method is able to recover the shape of the point clouds accurately and automatically. Furthermore, our approach outperforms other alternative methods significantly in terms of the numerical errors. We conclude that our method can be successfully applied to the reconstruction of manufactured workpieces in real industrial settings.

Accurate Range-Free Localization Using Cuckoo Search Optimization in IoT and Wireless Sensor Networks

Precise positioning of sensors is critical for the performance of various applications in the Internet of Things and wireless sensor networks. The efficiency of these networks heavily depends on the precision of sensor node locations. Among various localization approaches, DV-Hop is highly recommended for its simplicity and robustness. However, despite its popularity, DV-Hop suffers from significant accuracy issues, primarily due to its reliance on average hop size for distance estimation. This limitation often results in substantial localization errors, compromising the overall network effectiveness. To address this gap, we developed an enhanced DV-Hop approach that integrates the cuckoo search algorithm (CS). Our solution improves the accuracy of node localization by introducing a normalized average hop size calculation and leveraging the optimization capabilities of CS. This hybrid approach refines the distance estimation process, significantly reducing the errors inherent in traditional DV-Hop. Findings from simulations reveal that the developed approach surpasses the accuracy of both the original DV-Hop and multiple other current localization methods, providing a more precise and reliable localization method for IoT and WSN applications.

Optimization of MQL-Turning Process Parameters to Produce Environmentally-Benign AISI 4340 Alloy with Nano-Lubricants using Cuckoo Search Algorithm

The current research consists of a machining process involving AISI steel where the input parameters are the cutting depth, feed rate and cutting speed while the responses include the cutting force, surface roughness and tool wear. Usually, heat is generated during the turning process and various machining processes, and to reduce it, coolants are considered. In this work, CuO and Al2O3 were used as nano lubricants (MQL). Data obtained from the machining process were inserted into Minitab 18 software where quadratic objective functions were formulated as related to each output concerning the input parameters. Objective functions were optimized with the aid of C++ programming code. The cuckoo search algorithm was used for the optimization process of the work. This work clearly shows a reduction of the output parameters that is, cutting force from 243N to 127.20N, surface roughness from 0.66µm to 0.368µm and tool wear from 0.069mm to 0.0046mm using CuO as the nano lubricant. While using Al2O3, cutting force was lowered from 363N to 197.63N, surface roughness from 1.98µm to 0.148µm and tool wear from 0.219mm to 0.063mm. This clearly shows that using CuO helps to obtain a better cutting force coupled with elongation of the tool life but Al2O3 best gives a better surface finish.

Optimisation of laser surface ablation of alumina/GO coating on AZ31D magnesium alloy using Cuckoo Search Algorithm method

ABSTRACT Graphene oxide (GO) and alumina (Al2O3) work in concert to give magnesium alloys advantageous surface qualities and protection from adverse environments. This paper aims to investigate the effect of laser surface treatment on plasma sprayed alumina-graphene oxide (GO) coating on AZ31D magnesium alloy. Key ablation quality responses; HAZ width, surface roughness and ablation rate were correlated with laser parameters using the Response Surface Methodology and optimised using the metaheuristic Cuckoo Search Algorithm Method. Laser power significantly influenced all ablation characteristics of the composite coating. The addition of 1.5 wt% GO in the alumina coating caused a significant reduction in HAZ width (43.32%), surface roughness (25.11%) and ablation rate (76.58%) when treated with optimal combinations of laser power (8 W), scanning speed (950 mm/s) and frequency (78.2 kHz). The surface quality was characterised by smooth textures, and resolidified splats along with deep pits when ablated with lower and intermediate power whereas surfaces ablated with higher power showed debonded splats with peripheral surface destruction. While a prominent 2D peak at 16 W confirmed the presence of GO layers during high-power laser contacts, the ID/IG ratio below unity across all specimens indicated minimal defect concentrations.