Manta Ray Research Articles

Design of Grid-Connected rooftop Photovoltaic system for leakage current reduction using optimization algorithms

Rooftop solar power systems refer to the organization of photovoltaic (PV) panels on the rooftop of a building. They are a feasible substitute for land-based solar arrays, and they are being used in different Asian countries. Thus regulations, grid connectivity, off-take, and land acquisition are the main obstacles that renewable energy plant owners face. At high penetration of rooftop PV, this voltage variability reduces the stability of the grid due to transient imbalance in load and generation and causes voltage and frequency to exceed set limits if not countered by power controls. To reduce this power loss in the transmission cable, a Manta Ray Foraging Optimization (MRFO) process is presented in this research. This method also enhances the PV system's performance. A Non-isolated Buck-Boost Converter is connected between the PV and the grid. For renewable energy applications, a new non-isolated Buck-Boost converter with high voltage gain and positive output voltage has been developed. This converter receives the voltage of input from the PV and gives the output as boosting or lowering the voltage. To improve the current tracking with high power conversion efficiency, a Finite Control Set Model Predictive Controller (FCS-MPC) is introduced for this converter. The proposed optimization algorithm is utilized to tune the controller and improves the dynamic process of the power plant system connected to the grid. This algorithm is simulated using the Matlab Simulink software. The voltage and power generated by the proposed rooftop PV are 560 V and 495.25 kW/sec. The voltage and electricity produced by the ground-mounted are very low compared to the proposed method, therefore, utilizing this rooftop solar PV model, that study generates more power than the ground-mounted PV. This study contributes to the field by improving the energy production of roof-top solar PV systems based on roof design.

Reliability-based assessment of reasonable mid-tower lateral stiffness for a three-tower suspension bridge

A reasonable lateral stiffness of the bridge mid-tower should ensure that the deflection-to-span ratio and sliding resistance coefficient satisfy the design requirements. However, if this stiffness is determined by the deterministic parametric analysis, the above two verification indices may exceed the threshold due to the randomness of the high-dimensional structure and load parameters. To satisfy the structural safety requirements of a three-tower suspension bridge during its design life, this study proposes a method to quickly and accurately evaluate the reliability indices of the deflection-to-span ratio and sliding resistance coefficient of the three-tower suspension bridge. Based on advanced first-order second-moment, the proposed method transforms the reliability problem into an optimization problem with constraint functions, uses the single-cable theory to establish the limit state function, and combines the manta ray foraging optimization algorithm and penalty function to derive the reliability index. The effectiveness of the proposed method is verified by performing Latin hypercube sampling for an examplary three-tower suspension bridge. These results prove that the new technique avoids tedious finite element simulation and surrogate model fitting, ensuring the accuracy of the reliability index while improving the calculation efficiency. The study further identifies critical structural parameters by parametric analysis. Based on the inverse reliability, the dimensionless stiffness range for the mid-tower with the randomness of the parameters is derived, and the preliminary design method of the lateral stiffness of the mid-tower based on the reliability is established.

Revolutionizing Vehicle Cruise Control: An Elite Opposition-Based Pattern Search Mechanism Augmented INFO Algorithm for Enhanced Controller Design

This paper presents a groundbreaking approach to enhance the performance of a vehicle cruise control system—a crucial aspect of road safety. The work offers two key contributions. Firstly, a state-of-the-art metaheuristic algorithm is proposed by augmenting the performance of the weighted mean of vectors (INFO) algorithm using pattern search and elite opposition-based learning mechanisms. The resulting boosted INFO (b-INFO) algorithm surpasses the original INFO, marine predators, and gravitational search algorithms in terms of performance on benchmark functions, including unimodal, multimodal, and fixed-dimensional multimodal functions. Secondly, a novel proportional, fractional order integral, derivative plus double derivative with filter (P{I}^{lambda }DN{D}^{2}{N}^{2}) controller is proposed as a more efficient control structure for vehicle cruise control systems. An objective function is utilized to determine the optimal values for the controller parameters, and the proposed method's performance is compared against a range of recent approaches. Results demonstrate that the b-INFO algorithm-based P{I}^{lambda }DN{D}^{2}{N}^{2} controller is the most efficient and superior method for controlling a vehicle cruise control system. Moreover, this work represents the first report of a P{I}^{lambda }DN{D}^{2}{N}^{2} controller’s implementation for vehicle cruise control systems, underscoring the novelty and significance of this research. The proposed method's exceptional ability is further confirmed by comparisons with the genetic algorithm, ant lion optimizer, atom search optimizer, arithmetic optimization algorithm, slime mold algorithm, Lévy flight distribution algorithm, manta ray foraging optimization, and hunger games search-based proportional–integral–derivative (PID), along with Harris hawks optimization-based PID and fractional order PID controllers. This work marks a remarkable milestone toward safer and more efficient vehicle cruise control systems.

Design and Realization of a Novel Robotic Manta Ray for Sea Cucumber Recognition, Location, and Approach.

Sea cucumber manual monitoring and fishing present various issues, including high expense and high risk. Meanwhile, compared to underwater bionic robots, employing autonomous underwater robots for sea cucumber monitoring and capture also has drawbacks, including low propulsion efficiency and significant noise. Therefore, this paper is concerned with the design of a robotic manta ray for sea cucumber recognition, localization, and approach. First, the developed robotic manta ray prototype and the system framework applied to real-time target search are elaborated. Second, by improved YOLOv5 object detection and binocular stereo-matching algorithms, precise recognition and localization of sea cucumbers are achieved. Thirdly, the motion controller is proposed for autonomous 3D monitoring tasks such as depth control, direction control, and target approach motion. Finally, the capabilities of the robot are validated through a series of measurements. Experimental results demonstrate that the improved YOLOv5 object detection algorithm achieves detection accuracies (mAP@0.5) of 88.4% and 94.5% on the URPC public dataset and self-collected dataset, respectively, effectively recognizing and localizing sea cucumbers. Control experiments were conducted, validating the effectiveness of the robotic manta ray's motion toward sea cucumbers. These results highlight the robot's capabilities in visual perception, target localization, and approach and lay the foundation to explore a novel solution for intelligent monitoring and harvesting in the aquaculture industry.

Design Optimization of Improved Fractional-Order Cascaded Frequency Controllers for Electric Vehicles and Electrical Power Grids Utilizing Renewable Energy Sources

Recent developments in electrical power grids have witnessed high utilization levels of renewable energy sources (RESs) and increased trends that benefit the batteries of electric vehicles (EVs). However, modern electrical power grids cause increased concerns due to their continuously reduced inertia resulting from RES characteristics. Therefore, this paper proposes an improved fractional-order frequency controller with a design optimization methodology. The proposed controller is represented by two cascaded control loops using the one-plus-proportional derivative (1 + PD) in the outer loop and a fractional-order proportional integral derivative (FOPID) in the inner loop, which form the proposed improved 1 + PD/FOPID. The main superior performance characteristics of the proposed 1 + PD/FOPID fractional-order frequency controller over existing methods include a faster response time with minimized overshoot/undershoot peaks, an ability for mitigating both high- and low-frequency disturbances, and coordination of EV participation in regulating electrical power grid frequency. Moreover, simultaneous determination of the proposed fractional-order frequency controller parameters is proposed using the recent manta ray foraging optimization (MRFO) algorithm. Performance comparisons of the proposed 1 + PD/FOPID fractional-order frequency controller with existing PID, FOPID, and PD/FOPID controllers are presented in the paper. The results show an improved response, and the disturbance mitigation is also obtained using the proposed MRFO-based 1 + PD/FOPID control and design optimization methodology.

A bio-inspired slippery coating with mechanochemical robustness for anti-ice and anti-corrosion

Slippery coatings with outstanding liquid repellency and drag reduction are essential for energy saving and economic society. However, their long-term durability is still limited by weak slippery capacity and mechanochemical stability. Here, a manta ray skin inspired coating (MSC) via straightforward spraying method is developed. The rational design of the hemispherical structures composed of silicon-containing materials endows a transparent and robust slippery surface with hardness of 0.94 GPa and an extremely low friction coefficient of 0.076. The MSC can be applied on diverse substrates with strong adhesion and exhibits an outstanding resistance to liquid with a wide range of surface tension and viscosity up to 1,499 mPa·s. The coating maintains transparency and sustains liquid repellency after 1,000 abrasion cycles under 250 g loads or 32 days treatment at extreme high/low temperatures. In addition, the distinct anti-icing/de-icing properties and good anti-corrosion performance along with a facile spraying process provide MSC a tremendous potential for achieving industrial applications.

Hierarchical Manta Ray Foraging Optimization with Weighted Fitness-Distance Balance Selection

Manta ray foraging optimization (MRFO) tends to get trapped in local optima as it relies on the direction provided by the previous individual and the best individual as guidance to search for the optimal solution. As enriching population diversity can effectively solve this problem, in this paper, we introduce a hierarchical structure and weighted fitness-distance balance selection to improve the population diversity of the algorithm. The hierarchical structure allows individuals in different groups of the population to search for optimal solutions in different places, expanding the diversity of solutions. In MRFO, greedy selection based solely on fitness can lead to local solutions. We innovatively incorporate a distance metric into the selection strategy to increase selection diversity and find better solutions. A hierarchical manta ray foraging optimization with weighted fitness-distance balance selection (HMRFO) is proposed. Experimental results on IEEE Congress on Evolutionary Computation 2017 (CEC2017) functions show the effectiveness of the proposed method compared to seven competitive algorithms, and the proposed method has little effect on the algorithm complexity of MRFO. The application of HMRFO to optimize real-world problems with large dimensions has also obtained good results, and the computational time is very short, making it a powerful alternative for very high-dimensional problems. Finally, the effectiveness of this method is further verified by analyzing the population diversity of HMRFO.

Design and implementation of multi-level linkage mechanism bionic pectoral fin for manta ray robot

Manta rays achieve propulsion through the undulation of their pectoral fins, making them an important subject of study in bionics due to their efficient and agile swimming characteristics. However, currently, most biomimetic pectoral fins achieve a flapping pattern similar to that of biological fins by using multiple driving mechanisms. This paper designs an actively controlled bionic pectoral fin, driven by a single motor through a multi-level linkage mechanism, for a larger size and fully functional manta ray robot. Firstly, a bionic pectoral fin is designed based on a multi-level linkage mechanism driven by a single motor, and non-linear programming methods are innovatively applied to optimize the fin mechanism, making its motion profile more similar to the sinusoidal waveform of biological manta ray pectoral fins. Secondly, a dynamic model of the manta ray robot is established using dynamic mesh technology to verify the propulsion capability of the bionic pectoral fins and the robot's theoretical speed. Finally, prototypes of the manta ray robot are developed, and experimental research is conducted in a pool and lake. The experimental results show that the robot can complete straight-line swimming, buoyancy control, and turning in water, with a stable and flexible motion posture, proving the feasibility of this method.

Manta ray foraging optimization based on mechanics game and progressive learning for multiple optimization problems

Metaheuristic algorithms are currently being studied in depth by many scholars, and it is an important task to improve the learning and adaptive capabilities of the algorithms so that they can be of great use in a wide range of optimization problems. The Manta ray foraging optimization (MRFO) has made certain achievements in optimization problems, but it still has shortcomings, such as its insufficient intra-population communication and poor learning ability, so the optimization capability of MRFO needs further improvement. To address these shortcomings, this paper proposes a manta ray foraging optimization based on mechanics game and progressive learning for multiple optimization problems, which is abbreviated as MGL-MRFO, introducing variable spiral factors and dynamic adjustment of internal parameters; then proposes progressive learning to enhance the learning ability of MRFO; and finally proposes a matching game-based update mechanism to eliminate intra-population repetition as a feasible solution. It is validated in terms of move step size and time complexity. The results are compared with variants of the algorithm proposed in recent years in 18 benchmark test functions and the CEC 2022 test set, as well as with the TOP algorithm in the CEC competition, and show that MGL-MRFO has strong advantages, validating the novelty and competitiveness of MGL-MRFO. At the end of the paper, the feasibility and practicality of MGL-MRFO is further validated by three engineering optimization problems.

Deep multi-scale three-dimensional convolutional neural network optimized with manta ray foraging optimization algorithm for classification of lung cancer on CT images

ABSTRACT Lung cancer starts in the lungs and spreads to other organs in the body. Premature identification can only help the doctor to make an exact diagnosis and it may save the life of patients. Numerous studies have been conducted in this area, but none of them attains the accuracy outcomes. To overcome this drawback, a deep multi-scale three dimensional convolutional neural network optimized with the manta ray foraging optimization algorithm is proposed in this article for lung cancer classification on CT images (LCCT-DMS3DCNN-MRFOA) which effectively classifies the lung cancer as benign or malignant. The simulation is executed in MATLAB. The ELCAP dataset is utilized to confirm the performance of the proposed LCCT-DMS3DCNN-MRFOA approach. The efficiency of the LCCT-DMS3DCNN-MRFOA approach attains 14.21%, 22.96% and 24.94% higher accuracy; 12.59%, 11.71% and 11.55% higher AUC and 59.83%, 53.05% and 61.41% lower computational time compared with existing methods.

Spatial Ecology of the Population of Reef Manta Rays, Mobula alfredi (Krefft, 1868), in New Caledonia Using Satellite Telemetry 1–Horizontal Behaviour

The reef manta ray, Mobula alfredi (Krefft, 1868), is a highly mobile and plankton-feeding species, classified vulnerable to extinction on the IUCN Red List for Threatened Species. Knowledge on their spatial ecology and the extent of their dispersal remain incomplete, especially within island-fragmented habitats as found in New Caledonia. Satellite telemetry was used to investigate the horizontal movement ecology of reef manta rays in New Caledonia. A total of 21 reef manta rays were tagged with pop-up satellite archival transmitting tags (21 Fastloc and 2 MiniPAT) that remained deployed for a duration ranging from 3 to 180 days (mean ± SE = 76.7 ± 50.3). Rays presented a strong site fidelity and an important affinity for coastal waters. Long-distance migrations (>300 km) were also observed, mainly through coastal and shallow water paths. Horizontal movements were compared to a home range area and classified into four distinct patterns: Fidelity, Excursion, Fidelity + Relocation and Relocation. The most dominant pattern was Fidelity, where manta rays remained within their home range for the whole duration of the tag deployment. Our findings may assist in the design of more appropriate management strategies for the species in New Caledonia and other regions worldwide.

Enhancing predictions of blast-induced ground vibration in open-pit mines: Comparing swarm-based optimization algorithms to optimize self-organizing neural networks

The objective of this paper is to present a method for predicting blast-induced ground vibration in open-pit mines that is based on the use of self-organizing neural networks (SONIA) and metaheuristic algorithms. In order to improve the accuracy of the SONIA model, several metaheuristic algorithms were employed, including the Manta Ray Foraging Optimization (MRFO), Hunger Games Search (HGS), Aquila Optimization (AO), and Naked Mole-Rat Algorithm (NMRA). Additionally, the k-fold cross-validation technique was used to identify the best parameters for the algorithms, which were then used to retrain the models for predicting blast-induced ground vibration. The effectiveness of the proposed method was evaluated using a case study of an open-pit coal mine in Vietnam, which had 288 blasting events. The study found that SONIA was a suitable neural network for predicting blast-induced ground vibration due to its self-organizing structure, even with a small dataset containing complex relationships. However, the SONIA model could be further optimized using the metaheuristic algorithms to improve its accuracy. The study found that the MRFO-SONIA model was the most reliable and accurate, with the lowest error (MAE = 0.379, RMSE = 0.453, MAPE = 0.08) and the highest reliability (R2 = 0.896). Meanwhile, the HGS-SONIA, AO-SONIA, and NMRA-SONIA models provided lower performance, with MAE values of 0.455, 0.500, and 0.492, RMSE values of 0.552, 0.603, and 0.580, MAPE values of 0.100, 0.112, and 0.111, and R2 values of 0.845, 0.815, and 0.829, respectively. The results of this study demonstrated the potential of using metaheuristic-based SONIA models to enhance predictions of blast-induced ground vibration in open-pit mines. This approach could prove useful in other operations in open-pit mines where there is a need to predict vibrations or other disturbances/negative effects resulting from specific mining activities.

Hydrodynamic performance study of manta ray gliding in groups

To investigate the effect of group gliding formation on the hydrodynamic performance of manta rays, numerical simulations were used to investigate the drag, the lift and pressure distribution of multiple manta rays in tandem, triangular and diamond-shaped arrangements. The simulation results show that the leader manta ray at the head of the group always suffers the least drag, and in most cases, the companion manta ray at the tail of the group suffers the most drag. The pressure distribution in the flow field shows that the drag reduction effect mainly comes from the distribution of high pressure and low pressure zones among the individual rays in the cluster, the high pressure zone is beneficial to the manta rays at the front of the group, and under certain circumstances, the presence of low pressure zone generates forward suction to reduce the drag of the manta rays at the end of the group. It is found that when manta rays glide in "two in front and one at the back", four-body diamond, six-body tandem and six-body diamond arrangement, the average drag of the system can be reduced, which provides theoretical guidance for the formation of bionic vehicle groups.

Research on swimming performance of fish in different species

Manta rays and tunas are outstanding representatives of propulsion by MPF (median and/or paired fin) and BCF (body and/or caudal fin), respectively, and it is an interesting topic to see what kind of fluid effects will be generated when they meet during the swimming process. In the present study, numerical simulations were performed for the individual swimming state of manta ray/tuna and the group swimming of manta ray and tuna in a tandem arrangement. In individual swimming conditions, increasing amplitude helps to improve manta ray thrust and increasing wave number helps to improve manta ray efficiency; increasing frequency, tuna thrust increases monotonically and efficiency increases first and then decreases. In the tandem group swimming state, the manta ray thrust and efficiency are enhanced at most spacings, the smaller the spacing, the greater the enhancement, as seen in the vortex structure, which benefits from the merging of the rear tuna wake. The thrust and efficiency curves of the tuna fluctuate greatly with spacing, which is related to the position of the manta ray wake field where the tuna are located.

Assessment and Enhancement of Uncertain Renewable Energy Hosting Capacity With/out Voltage Control Devices in Distribution Grids

Nowadays, with excessive integration of renewable energy sources (RESs), the distribution systems face several challenges due to exceeding the hosting capacity limit (HCL) of RESs. This article proposes a bi-stage approach to maximize the HCL considering RESs and load uncertainties. The first stage develops a multiobjective framework to adjust the system voltage profiles in a specific manner to avoid exceeding the voltage boundaries with high penetration of RESs. In the second stage, two processes are employed to maximize the HCL as the main objective function. In the first one, the photovoltaic (PV) and wind turbine (WT) plants are optimally allocated, while in the second process, a combination of three voltage control devices is proposed. A correlated multidimension probability uncertainty model is used to emulate the uncertainty of PV, WT, and load demand. Manta Ray foraging optimization (MRFO) algorithm is proposed to solve single-objective and multiobjective frameworks in the bi-stage approach considering the performance limits. The proposed approach is tested and validated on medium- and large-scale distribution systems. The effectiveness of the proposed MRFO is approved compared with two optimization algorithms. The proposed approach achieves up to 77.8% RESs integration from the total load demand for the tested systems.

BIN_MRFOA: İkili Optimizasyon İçin Yeni Bir Manta Vatozu Beslenme Optimizasyonu Algoritması

Optimization problems occur in three different structures: continuous, discrete, and hybrid. Metaheuristic algorithms, which are frequently preferred in the solution of optimization problems today, are mostly proposed for continuous problems and are discretized with subsequent modifications. In this study, a novel binary version (Bin_MRFOA) of the manta ray foraging optimization algorithm, which was frequently used in the solution of continuous optimization problems before, was proposed to be used in the solution of binary optimization problems. The Bin_MRFOA was first tested on ten classical benchmark functions, and the effect of the transfer function on performance was examined by comparing the variants obtained using eight different transfer functions. Then the most successful Bin_MRFOA variant was run on the eighteen CEC2005 benchmark functions. The results were compared with the algorithms in the literature and interpreted with Wilcoxon signed-rank and Friedman tests, which are nonparametric tests. The results revealed that Bin_MRFOA is a successful, competitive, and preferable algorithm compared to the literature.

Utilizing controlled plug-in electric vehicles to improve hybrid power grid frequency regulation considering high renewable energy penetration

This study proposes an incorporated strategy based on energy storage systems (ESSs) like plug-in electric vehicles (PEVs) with load frequency control (LFC) to enhance the frequency stability during high penetration of renewable energy sources (RESs). Furthermore, an innovative-cascaded controller is used in the LFC loop, and PEVs are considered in the two identical areas' multi-source power grids. The proposed control structure is based on using two terms, the first term is the fractional-order-proportional-derivative (FOPD), and the second cascaded term is a fractional-order-proportional-integral (FOPI) to form the cascaded (FO-(PD-PI)) controller. Additionally, the gains of the proposed controller are well designed, utilizing an improved version of the manta ray foraging optimization algorithm (MRFO) labeled as leader MRFO (LMRFO). The effectiveness of the proposed FO-(PD-PI) controller based on the LMFRO algorithm is validated by comparing its performance with that of other control approaches such as (i.e., the proportional-integral-derivative (PID) controller, the tilt-integral-derivative (TID) controller, and the fractional-order-PID (FOPID) controller) based on different optimizers. Different load perturbation patterns, a high penetration level of RESs, and the communication delay time are broadly implemented for ensuring the superiority of the proposed controller in penalizing the power grid perturbations. Furthermore, for validating the efficiency of the proposed controller, an IEEE 39 bus is used. As a result, the proposed integrated strategy is a viable and effective control scheme for modern power grids with high-RESs penetration.

Use of underwater contactless ultrasonography to elucidate the internal anatomy and reproductive activity of manta and devil rays (Fam. Mobulidae).

The ability to visualise the internal anatomical structures of fish provides important information on their reproductive status and body condition and has made important contributions to many areas of fish biology. Obtaining information about the internal anatomy of fish has traditionally required euthanasia and dissection. However, although ultrasonography is now increasingly used to study internal fish anatomy without the need for euthanasia, traditional techniques still require restraint and contact with the animal, both of which are known to cause stress. This has prompted the development of waterproof, contactless, and portable equipment to allow ultrasonographic examinations to be carried out in free-swimming individuals which also facilitates the application of this tool in wild populations of endangered species. This study reports the validation of this equipment using anatomical examinations of nine manta and devil ray (Mobulidae) specimens landed at fish markets in Sri Lanka. Species studied were Mobula kuhlii (n = 3), M. thurstoni (n = 1), M. mobular (n = 1), M. tarapacana (n = 1) and M. birostris (n = 3). The use of this equipment was further validated with ultrasonographic examinations in 55 free-swimming reef manta rays Mobula alfredi, which enabled maturity status to be quantified in 32 females. Structures successfully identified in free-swimming individuals were the liver, spleen, gallbladder, gastrointestinal tract, skeletal structures, developing follicles and uterus. The study demonstrated that ultrasonography provided a reliable method of determining both sexual maturity and gestational status in free-swimming M. alfredi. The methodology induced no detectable signs of disturbance to the animals involved and therefore offers a viable and practical alternative to invasive techniques currently used to study anatomical changes in both captive and wild marine organisms. This article is protected by copyright. All rights reserved.

A multimodal AI-based non-invasive COVID-19 grading framework powered by deep learning, manta ray, and fuzzy inference system from multimedia vital signs

The COVID-19 pandemic has presented unprecedented challenges to healthcare systems worldwide. One of the key challenges in controlling and managing the pandemic is accurate and rapid diagnosis of COVID-19 cases. Traditional diagnostic methods such as RT-PCR tests are time-consuming and require specialized equipment and trained personnel. Computer-aided diagnosis systems and artificial intelligence (AI) have emerged as promising tools for developing cost-effective and accurate diagnostic approaches. Most studies in this area have focused on diagnosing COVID-19 based on a single modality, such as chest X-rays or cough sounds. However, relying on a single modality may not accurately detect the virus, especially in its early stages. In this research, we propose a non-invasive diagnostic framework consisting of four cascaded layers that work together to accurately detect COVID-19 in patients. The first layer of the framework performs basic diagnostics such as patient temperature, blood oxygen level, and breathing profile, providing initial insights into the patient's condition. The second layer analyzes the coughing profile, while the third layer evaluates chest imaging data such as X-ray and CT scans. Finally, the fourth layer utilizes a fuzzy logic inference system based on the previous three layers to generate a reliable and accurate diagnosis. To evaluate the effectiveness of the proposed framework, we used two datasets: the Cough Dataset and the COVID-19 Radiography Database. The experimental results demonstrate that the proposed framework is effective and trustworthy in terms of accuracy, precision, sensitivity, specificity, F1-score, and balanced accuracy. The audio-based classification achieved an accuracy of 96.55%, while the CXR-based classification achieved an accuracy of 98.55%. The proposed framework has the potential to significantly improve the accuracy and speed of COVID-19 diagnosis, allowing for more effective control and management of the pandemic. Furthermore, the framework's non-invasive nature makes it a more attractive option for patients, reducing the risk of infection and discomfort associated with traditional diagnostic methods.

Clustering based EO with MRF technique for effective load balancing in cloud computing

PurposeCloud computing (CC) refers to the usage of virtualization technology to share computing resources through the internet. Task scheduling (TS) is used to assign computational resources to requests that have a high volume of pending processing. CC relies on load balancing to ensure that resources like servers and virtual machines (VMs) running on real servers share the same amount of load. VMs are an important part of virtualization, where physical servers are transformed into VM and act as physical servers during the process. It is possible that a user’s request or data transmission in a cloud data centre may be the reason for the VM to be under or overloaded with data.Design/methodology/approachVMs are an important part of virtualization, where physical servers are transformed into VM and act as physical servers during the process. It is possible that a user’s request or data transmission in a cloud data centre may be the reason for the VM to be under or overloaded with data. With a large number of VM or jobs, this method has a long makespan and is very difficult. A new idea to cloud loads without decreasing implementation time or resource consumption is therefore encouraged. Equilibrium optimization is used to cluster the VM into underloaded and overloaded VMs initially in this research. Underloading VMs is used to improve load balance and resource utilization in the second stage. The hybrid algorithm of BAT and the artificial bee colony (ABC) helps with TS using a multi-objective-based system. The VM manager performs VM migration decisions to provide load balance among physical machines (PMs). When a PM is overburdened and another PM is underburdened, the decision to migrate VMs is made based on the appropriate conditions. Balanced load and reduced energy usage in PMs are achieved in the former case. Manta ray foraging (MRF) is used to migrate VMs, and its decisions are based on a variety of factors.FindingsThe proposed approach provides the best possible scheduling for both VMs and PMs. To complete the task, improved whale optimization algorithm for Cloud TS has 42 s of completion time, enhanced multi-verse optimizer has 48 s, hybrid electro search with a genetic algorithm has 50 s, adaptive benefit factor-based symbiotic organisms search has 38 s and, finally, the proposed model has 30 s, which shows better performance of the proposed model.Originality/valueUser’s request or data transmission in a cloud data centre may cause the VMs to be under or overloaded with data. To identify the load on VM, initially EQ algorithm is used for clustering process. To figure out how well the proposed method works when the system is very busy by implementing hybrid algorithm called BAT–ABC. After the TS process, VM migration is occurred at the final stage, where optimal VM is identified by using MRF algorithm. The experimental analysis is carried out by using various metrics such as execution time, transmission time, makespan for various iterations, resource utilization and load fairness. With its system load, the metric gives load fairness. How load fairness is worked out depends on how long each task takes to do. It has been added that a cloud system may be able to achieve more load fairness if tasks take less time to finish.