Sunflower Optimization Algorithm Research Articles

Hybrid Optimisation with Black Hole Algorithm for Improving Network Lifespan

Wireless sensor networks (WSNs) are projected to have a wide range of applications in the future. The fundamental problem with WSN is that it has a finite lifespan. Clustering a network is a common strategy for increasing the lifetime of WSNs and, as a result, allowing for faster data transmission. The clustering algorithm’s goal is to select the best cluster head (CH). In the existing system, Hybrid grey wolf sunflower optimization algorithm (HGWSFO)and optimal cluster head selection method is used. It does not provide better competence and output in the network. Therefore, the proposed Hybrid Grey Wolf Ant Colony Optimisation (HGWACO) algorithm is used for reducing the energy utilization and enhances the lifespan of the network. Black hole method is used for selecting the cluster heads (CHs). The ant colony optimization (ACO) technique is used to find the route among origin CH and destination. The open cache of nodes, transmission power, and proximity are used to improve the CH selection. The grey wolf optimisation (GWO) technique is the most recent and well-known optimiser module which deals with grey wolves’ hunting activity (GWs). These GWs have the ability to track down and encircle food. The GWO method was inspired by this hunting habit. The proposed HGWACO improves the duration of the network, minimizes the power consumption, also it works with the large-scale networks.The HGWACO method achieves 25.64% of residual energy, 25.64% of alive nodes, 40.65% of dead nodes also it enhances the lifetime of the network.

Web Intelligence with Enhanced Sunflower Optimization Algorithm for Sentiment Analysis

Web Intelligence with Enhanced Sunflower Optimization Algorithm for Sentiment Analysis

ExpSFROA-Based DRN: Exponential Sunflower Rider Optimization Algorithm-Driven Deep Residual Network for the Intrusion Detection in IOT-Based Plant Disease Monitoring

Internet of Things (IoT) significantly gained attraction nowadays, as it assists in numerous purposes. Several computerized technologies are employed for monitoring the plant disease in the IoT paradigm, and the major challenging lies in detecting the intrusions while monitoring the plant disease. This paper proposes an Exponential Sun Flower Rider Optimization Algorithm-driven Deep Residual Network (ExpSFROA-based DRN) for achieving effective intrusion detection results in the IoT. The proposed ExpSFROA is devised by incorporating Exponential Sun Flower Optimization (Exponential-SFO) and Rider Optimization Algorithm (ROA). Meanwhile, Exponential-SFO is designed by combining the Exponential Weighted Moving Average (EWMA) and Sunflower Optimization (SFO) algorithm. Here, the information is collected from the simulated IoT nodes based on the Cluster Head (CH), and performs the process of routing in order to predict the leaf disease more effectively. Based on the disease prediction process, the intrusion detection process is achieved by the devised ExpSFROA-based DRN. The performance of the newly developed ExpSFROA-based DRN is evaluated using four metrics such as accuracy, throughput, energy, and True Positive Rate (TPR). The developed method attained better results than the existing methods, such as SecTrust-RPL+DRN, OSEAP+IBFO+DRN, LASeR++DRN with a maximum accuracy of 0.950, higher throughput of 7533350 bps, minimal energy of 49.74 J, and higher TPR of 0.956. The proposed method is used in the agricultural land, which helps the farmers for monitoring the conditions of the fields anywhere and anytime, thereby minimizing the manpower and time.

Deep learning enabled cross‐lingual search with metaheuristic web based query optimization model for multi‐document summarization

SummaryDue to the exponential increase in the generation of digital documents and in the online search user diversity, multilingual information is highly available on the Internet. However, the huge amount of multilingual data cannot be analyzed manually. Therefore, cross lingual multi‐document summarization (CLMDS) model is introduced to generate a summary of several documents in which the summary language is different from the source document language. This paper presents a Deep Learning Enabled Cross‐lingual Search with Metaheuristic based Query Optimization (DLCLS‐MQO) model for Multi‐document summarization. The DLCLS‐MQO model allows to offer a query in Tamil, summarize several English documents, and lastly translate the summary into Tamil. The DLCLS‐MQO model encompasses four stages of operation such as multilingual search, query optimization, automatic sematic lexicon builder, and document summarization. Firstly, bidirectional long short‐term memory (BiLSTM) model is applied to perform multilingual searching process. Followed by, sunflower optimization (SFO) algorithm based query optimization process is carried out. Moreover, global vectors (GloVe) method is used for the construction of domain oriented sentiment lexicons. Finally, extreme gradient boosting (XGBoost) model is applied for the CLMDS. A detailed simulation analysis takes place to highlight the betterment of the DLCLS‐MQO model. The resultant experimental values portrayed the superior performance of the DLCLS‐MQO model over the compared methods.

Lung disease detection using Self-Attention Generative Adversarial Capsule network optimized with sun flower Optimization Algorithm

Lung cancer is the uncontrolled growth of abnormal cells in one or both lungs. This is one of the dangerous diseases. A lot of feature extraction with classification methods were discussed previously regarding this disease, but none of the methods give sufficient results, not only that, those methods have high over fitting problem, as a result, the detection accuracy was minimizing. Therefore, to overcome these issues, a Lung Disease Detection using Self-Attention Generative Adversarial Capsule Network optimized with Sun flower Optimization Algorithm (SA-Caps GAN-SFOA-LDC) is proposed in this manuscript. Initially, NIH chest X-ray image dataset is gathered through Kaggle repository to diagnose the lung disease. Then, the chests X-ray images are pre-processed by using the contrast limited adaptive histogram equalization (CLAHE) filtering method to eliminate the noise and to enhance the image quality. These pre-processed outputs are fed to feature extraction process. In the feature extraction process, the empirical wavelet transform method is used. These extracted features are given into Self-Attention based Generative Adversarial Capsule classifier for detecting the lung disease. The hyper parameters of SA-Caps GAN classifier is optimized using Sun flower Optimization Algorithm. The simulation is implemented in MATLAB. The proposed SA-Caps GAN-SFOA-LDC method attains higher accuracy 21.05%, 33.28%, 30.27%, 29.68%, 32.57% and 44.28%, Higher Precision 30.24%, 35.68%, 32.08%, 41.27%, 28.57% and 34.20%, Higher F-Score 32.05%, 31.05%, 36.24%, 30.27%, 37.59% and 22.05% analyzed with the existing methods, SVM-SMO-LDC, CNN-MOSHO-LDC, XGboost-PSO-LDC respectively.

Enhanced radial basis function neural network for tomato plant disease leaf image segmentation

Primary crop losses in agriculture are due to leaf diseases, which farmers cannot identify early. If the diseases are not detected early and correctly, then the farmer will have to undergo huge losses. Therefore, in the field of agriculture, the detection of leaf diseases in tomato crops plays a vital role. Recent advances in computer vision and deep learning techniques have made disease prediction easy in agriculture. Tomato crop front side leaf images are considered for research due to their high exposure to diseases. The image segmentation process assumes a significant role in identifying disease affected areas on tomato leaf images. Therefore, this paper develops an efficient tomato crop leaf disease segmentation model using an enhanced radial basis function neural network (ERBFNN). The proposed ERBFNN is enhanced using the modified sunflower optimization (MSFO) algorithm. Initially, the noise present in the images is removed by a Gaussian filter followed by CLAHE (contrast-limited adaptive histogram equalization) based on contrast enhancement and un-sharp masking. Then, color features are extracted from each leaf image and given to the segmentation stage to segment the disease portion of the input image. The performance of the proposed ERBFNN approach is estimated using different metrics such as accuracy, Jaccard coefficient (JC), Dice's coefficient (DC), precision, recall, F-Measure, sensitivity, specificity, and mean intersection over union (MIoU) and are compared with existing state-of-the-art methods of radial basis function (RBF), fuzzy c-means (FCM), and region growing (RG). The experimental results show that the proposed ERBFNN segmentation model outperformed with an accuracy of 98.92% compared to existing state-of-the-art methods like RBFNN, FCM, and RG, as well as previous research work.

Economic-emission load dispatch for power system operation using enhanced sunflower optimization

Conventional thermal power plant uses limited sources of gas, fuel or coal which contributes to the rise of air pollution. Thus, it is crucial to efficiently use the natural sources and minimize the emissions of greenhouse gases and other pollutants. This paper presents an optimal economic dispatch considering three factors which are cost of generation, loss of power transmission and amount of emission for an efficient operation of power generation. Enhanced sunflower optimization (ESFO) algorithm is applied to determine the solution for three different cases: economic load dispatch, emission load dispatch and economic-emission load dispatch. The optimal solution based on the minimum generation cost and emission is obtained for the IEEE 6-unit test system using MATLAB software

Drop-off Location Optimization in Hybrid CFRP/GFRP Composite Tubes Using Design of Experiments and SunFlower Optimization Algorithm

Drop-off Location Optimization in Hybrid CFRP/GFRP Composite Tubes Using Design of Experiments and SunFlower Optimization Algorithm

Real-time state of charge estimation for electric vehicle power batteries using optimized filter

The main focus of the battery management system is the estimation of the battery's State of Charge (SOC), which is an indicator to determine the driving range of an electric vehicle. The Extended Kalman Filter (EKF) algorithm is the most promising for SOC estimation when the system is running. The EKF state estimation algorithm is sensitive to the process noise covariance matrix Q and measurement noise covariance matrix R. Inappropriate noise covariance matrices reduce the accuracy and cause divergence in state estimation. This paper uses the Sunflower Optimization algorithm (SFO) to find the optimal values of the noise covariance matrices before applying EKF for online SOC estimation. It is clearly stated that the iterative SFO does not affect the instantaneous response of EKF in the online estimate because the SFO is only performed once to determine the optimal values. The effectiveness of the proposed identification is examined through the constant discharge rate test and dynamic driving profile. The proposed algorithm's performance indices, such as maximum error, Mean Absolute Error, Mean Square Error, and Root Mean Square Error of SOC estimation, are low compared to the trial-error method, genetic algorithm, and adaptive extended Kalman filter. Besides accuracy, the proposed method quickly converges even when the initial SOC is inaccurate. The feasibility and benefits of the proposed SFO-EKF algorithm are demonstrated by qualitative and quantitative findings on five challenging datasets. To assess the performance of the SFO-EKF for SOC estimation in constant-current discharge and extreme drive cycle situations, three public datasets and two datasheets are employed. The simulation results show that the proposed method has high accuracy and a better convergence rate in estimating SOC under static and dynamic operating conditions.

Design and Optimizing of Compact Ultra-Wide Band Printed Patch Antenna Employing Different Optimization Algorithms Based on Plant Inspiration

In this paper, a compact ultra-wide band (UWB) printed patch antenna is designed and optimized using four biologically and plant inspired optimization algorithms. These algorithms are the newly adopted Moss Rose Optimization Algorithm (MROA), Runner Root Algorithm (RRA), Sunflower Optimization Algorithm (SFOA) and Particle Swarm Optimization (PSO). These algorithms are modified in an optimizer software, which merges the attributes of the design of electromagnetic environment of CST Microwave Studio with those of the technical programming environment of MATLAB. A compact (12 × 21.5) mm2 printed patch antenna has been proposed and simulated over the whole UWB frequency range using these four optimization algorithms. The simulation results show the superiority of the antenna design using MROA, which has the widest covered frequency range, the lowest reflection coefficient and the lowest standing wave ratio.

Clustering by Hybrid K-Means-Based Rider Sunflower Optimization Algorithm for Medical Data

Currently, medical data clustering is a very active and effective part of the research area to take proper decisions at the medical field from medical data sets. But medical data clustering is a very challenging issue due to limitless receiving data, vast size, and high frequencies. To achieve this and improve the performance with fast and effective clustering, this paper proposes a hybrid optimization technique, namely, the K-means-based rider sunflower optimization (RSFO) algorithm for medical data. In this research, initially, the data preprocessing phase has been carried out to clean the current input medical data, and then in the second phase, important features are chosen with the help of the Tversky index with holoentropy. Finally, medical data clustering has been carried out by using hybrid K-means-based rider sunflower optimization (RSFO) algorithm. RSFO is designed to produce optimum clustering centroid, which is the combination of two optimization techniques, such as rider optimization algorithm (ROA) and sunflower optimization (SFO). This hybrid algorithm can get the advantages of both K-means and RSFO technique and avoid premature convergence of K-means algorithm and high computation cost of optimization technique. K-Means clustering algorithm is used to cluster the medical data by using an optimum centroid. The efficiency of the proposed K-means-based rider sunflower optimization algorithm is examined with a heart disease data set and analyzed based on three different performance metrics.

Cloud task scheduling using enhanced sunflower optimization algorithm

The objective of cloud task scheduling is to partition tasks on shared resources to minimize energy consumption and makespan. Recently, several meta-heuristics for task scheduling were proposed and achieved encouraging results. However, their performance is far from the ideal state and needs more improvement. This paper introduces an enhanced sunflower optimization (ESFO) algorithm for improving the performance of existing task scheduling. It finds optimal scheduling in a polynomial time. The experiments show that ESFO outperformed its counterparts. The amount of improvement in comparison with the best counterpart is 0.73% and 2.24% respectively in terms of makespan and energy consumption.

An efficient chaotic system based hybrid radiation heat transfer sunflower optimization algorithm for securing digital image transmission

AbstractAt the pixel level, several chaos‐based image encryption and decryption models have been developed throughout the years. However, due to long‐haul, privacy, and security considerations, it has a number of drawbacks, including increased network complexity and low scalability, and actual text cracking avoidance is more challenging. In this study, we have proposed a novel approach for image encryption and decryption. The confusion and diffusion process is performed using fractional‐order chaotic maps with the hybrid radiation heat transfer algorithm based sunflower optimization (HRHT‐SO) algorithm. The proposed method works for both color and grayscale images. The HRHT‐SO algorithm is the combination of both heat transfer search (HTS) and sunflower optimization (SO) algorithms. During the search process, the SO algorithm improves the radiation stage of the HTS model. Further, the DNA sequence improves the performance of image encryption and decryption using fractional‐order chaotic maps with the HRHT‐SO algorithm. The experimental investigations are carried out by using various parameters such as key sensitivity analysis, keyspace analysis, histogram analysis, correlation coefficient analysis, entropy analysis, PSNR, NPCR, and UACI analysis thereby analyzing the efficiency and complexity of the encryption scheme. Hence, the proposed method outperforms superior performances in all the experiments.

Battery total capacity estimation based on the sunflower algorithm

Battery total capacity estimation is crucial for battery management systems (BMSs). Accurate knowledge allows BMSs to anticipate potential failures in advance, through indicators such as the state of health. In this paper, we suggest a new capacity estimation framework based on the sunflower optimization algorithm (SFO). The technique reported accounts for capacity error sources, including measurement and estimation noises, in addition of being recursive. SFO is applied to find the best candidate that has the minimum objective function value in a population of sunflowers. A reduction strategy of the search space is used to enhance the solution accuracy. The best candidate is then used through a forgetting factor to update battery capacity. The algorithm's accuracy was verified using NASA Prognostic Data Repository, in addition to three scenarios of a battery in electric and plug-in hybrid electric vehicle applications. As far as we can tell, this is one of the first attempts to use SFO to estimate battery capacity. SFO showed great performance: SFO recorded a maximum error of 1.26% in the worst-case scenario. Moreover, all predictive performance indicators (root mean square error RMSE, mean absolute error MAE, and mean absolute percentage error MAPE) did not exceed 0.4% in all tests.

Taylor Sun Flower Optimization-Based Compressive Sensing for Image Compression and Recovery

Abstract The most prominent challenges in compressive sensing are seeking the domain where an image is represented sparsely and hence be faithfully recovered to obtain high-quality results. This paper introduces an approach for image compression and recovery. The proposed approach involves two phases: the initial step is the compression phase, and the second step is the recovery phase. Initially, the medical image is subjected to the compression module wherein the self-similarity and the 3-dimensional (3D) transform are adapted for compressing the image. Then, in the recovery phase, the compressive sensing recovery is performed based on structural similarity index measure (SSIM)-based collaborative sparsity measure (S-CoSM), and the novel optimization algorithm, named Taylor-based Sunflower optimization (Taylor-SFO) algorithm. An effective S-CoSM measure is designed by modifying the CoSM using the SSIM metric. The proposed Taylor-SFO will be designed by integrating the Taylor series with the sunflower optimization (SFO) algorithm. The performance of the proposed Taylor-SFO approach is evaluated for matrices SSIM of 0.9412 and peak signal to noise ratio of 57.57 dB.

Coot Bird Algorithms-Based Tuning PI Controller for Optimal Microgrid Autonomous Operation

This paper develops a novel methodology for optimal control of islanded microgrids (MGs) based on the coot bird metaheuristic optimizer (CBMO). To this end, the optimum gains for the PI controller are found using the CBMO under a multi-objective optimization framework. The Response Surface Methodology (RSM) is incorporated into the developed procedure to achieve a compromise solution among the different objectives. To prove the effectiveness of the new proposal, a benchmark MG is tested under various scenarios, 1) isolate the system from the grid (autonomous mode), 2) islanded system exposure to load changes, and 3) islanded system exposure to a 3 phase fault. Extensive simulations are performed to validate the new method taking conventional data from PSCAD/EMTDC software. The validity of the suggested optimizer is proved by comparing its results with that achieved using the LMSRE-based adaptive control, sunflower optimization algorithm (SFO), Ziegler-Nichols method and the particle swarm optimization (PSO) techniques. The article shows the superiority of the suggested CBMO over the LMSRE-based adaptive control, SFO, Ziegler-Nichols and the PSO techniques in the transient responses of the system.

Opposition based sunflower optimization algorithm using cloud computing environments

Computer!! The one everyone uses almost every day in this busy world, a device capable of handling tasks from simple addition to complex tasks like launching rockets, but to sustain these tasks, and be a part of the huge networks like the internet and serve as an integrated part of IoT (internet of things) many different devices like cloud computing, servers are developed which can handle the request and return the appropriate response to the device. However, IoT device face certain issues among which the obtainability of on-going energy heads in one of the biggest issue and reason of this is because of the enormous amount of information that is created by using IoT devices and handling this information is one of the biggest constraints. To reduce these issues, many different schedulers were introduced like the optimized schedule task requests which enhances system productivity and performance, and the task scheduler whose objective is to divide tasks based on the available resources so the it reduces make span and time consumption. Hence to improve cloud computing many different meta-heuristics systems for task scheduling were introduced, but their throughput was far from the optimal state and needed upgrading. Keeping that in mind, this paper proposes an Opposition based sunflower optimization (OSFO) algorithm for enhancing the throughput of already available task schedulers in terms of cost, energy and makespan.

Controller Placement in Software Defined Internet of Things Using Optimization Algorithm

The current and future status of the internet is represented by the upcoming Internet of Things (IoT). The internet can connect the huge amount of data, which contains lot of processing operations and efforts to transfer the pieces of information. The emerging IoT technology in which the smart ecosystem is enabled by the physical object fixed with software electronics, sensors and network connectivity. Nowadays, there are two trending technologies that take the platform i.e., Software Defined Network (SDN) and IoT (SD-IoT). The main aim of the IoT network is to connect and organize different objects with the internet, which is managed with the control panel and data panel in the SD network. The main issue and the challenging factors in this network are the increase in the delay and latency problem between the controllers. It is more significant for wide area networks, because of the large packet propagation latency and the controller placement problem is more important in every network. In the proposed work, IoT is implementing with adaptive fuzzy controller placement using the enhanced sunflower optimization (ESFO) algorithm and Pareto Optimal Controller placement tool (POCO) for the placement problem of the controller. In order to prove the efficiency of the proposed system, it is compared with other existing methods like PASIN, hybrid SD and PSO in terms of load balance, reduced number of controllers and average latency and delay. With 2 controllers, the proposed method obtains 400 miles as average latency, which is 22.2% smaller than PSO, 76.9% lesser than hybrid SD and 91.89% lesser than PASIN.

Caviar-Sunflower Optimization Algorithm-Based Deep Learning Classifier for Multi-Document Summarization

AbstractThis paper proposes a multi-document summarization model using an optimization algorithm named CAVIAR Sun Flower Optimization (CAV-SFO). In this method, two classifiers, namely: Generative Adversarial Network (GAN) classifier and Deep Recurrent Neural Network (Deep RNN), are utilized to generate a score for summarizing multi-documents. Initially, the simHash method is applied for removing the duplicate/real duplicate contents from sentences. Then, the result is given to the proposed CAV-SFO based GAN classifier to determine the score for individual sentences. The CAV-SFO is newly designed by incorporating CAVIAR with Sun Flower Optimization Algorithm (SFO). On the other hand, the pre-processing step is done for duplicate-removed sentences from input multi-document based on stop word removal and stemming. Afterward, text-based features are extracted from pre-processed documents, and then CAV-SFO based Deep RNN is introduced for generating a score; thereby, the internal model parameters are optimally tuned. Finally, the score generated by CAV-SFO based GAN and CAV-SFO based Deep RNN is hybridized, and the final score is obtained using a multi-document compression ratio. The proposed TaylorALO-based GAN showed improved results with maximal precision of 0.989, maximal recall of 0.986, maximal F-Measure of 0.823, maximal Rouge-Precision of 0.930, and maximal Rouge-recall of 0.870.

Rider and Sunflower optimization-driven neural network for image classification

Image classification is the classical issue in computer vision, machine learning, and image processing. The image classification is measured by differentiating the image into the prescribed category based on the content of the vision. In this paper, a novel classifier named RideSFO-NN is developed for image classification. The proposed method performs the image classification by undergoing two steps, namely feature extraction and classification. Initially, the images from various sources are provided to the proposed Weighted Shape-Size Pattern Spectra for pattern analysis. From the pattern analysis, the significant features are obtained for the classification. Here, the proposed Weighted Shape-Size Pattern Spectra is designed by modifying the gray-scale decomposition with Weight-Shape decomposition. Then, the classification is done based on Neural Network (NN) classifier, which is trained using an optimization approach. The optimization will be done by the proposed Ride Sunflower optimization (RideSFO) algorithm, which is the integration of Rider optimization algorithm (ROA), and Sunflower optimization algorithm (SFO). Finally, the image classification performance is evaluated using RideSFO-NN based on sensitivity, specificity, and accuracy. The developed RideSFO-NN method achieves the maximal accuracy of 94%, maximal sensitivity of 93.87%, and maximal specificity of 90.52% based on K-Fold.