Sunflower Optimization Algorithm Research Articles

Optimizing cluster head selection for energy efficiency in wireless sensor networks: A hybrid algorithm combining grey wolf and enhanced sunflower optimization

AbstractIn this letter, we introduce a novel cluster head selection algorithm namely mixed grey wolf and improved sunflower optimization algorithm (MGWISFO). This algorithm leverages both energy requirements and inter‐node distances to select cluster heads (CH). Within this algorithm, the Grey Wolf Optimizer facilitates exploration, offering a broader search, while the improved Sunflower Optimization focuses on exploitation, delivering a narrower search. This balance between exploration and exploitation leads to the identification of the optimal CH node, thereby enhancing network performance. To validate its effectiveness, the proposed algorithm is benchmarked against existing strategies such as particle swarm optimization (PSO), genetic algorithm (GA), grey wolf optimization (GWO), and sunflower optimization (SFO) across various performance parameters including throughput, the number of live and dead nodes, and residual energy. Simulation results unequivocally establish the unparalleled performance of our proposed algorithm, surpassing the capabilities of existing algorithms.

Grid-Connected Single-Stage DC–AC Converter for Solar PV Applications

Photovoltaic (PV) has become an important alternative because of the increased demand for electricity and the limited supply of traditional energy sources. Harmonic distortion (HD) on the grid-connected PV can lead to several undesirable outcomes, including overheating of connected equipment, frequent grid interruptions, reduced accuracy of electrical meters, and an increase in the required current. This manuscript proposes the novel use of the Sunflower Optimization (SFO) Algorithm in grid-connected single-stage DC–AC converter with minimizing Total HD (THD) and enhancing the efficiency of the system as its primary objective. The SFO algorithm is used to maximize the system's features and control the switched inductor boost converter. The proposed technique is then simulated using MATLAB and its performance is contrasted with other existing technologies Recurrent Neural Network (RNN), Garra-Rufa Fish Optimization-Student Psychology Optimization Algorithm (GRFO-SPOA), and Fuzzy Logic Control (FLC). The proposed method attains an efficiency of 97% with a low THD of 3.1%. The findings suggest that the proposed method outperforms the existing approaches.

Economic Energy Scheduling of electrical micro grid considering optimal participation of the electric vehicles

This research presents a strategy for managing energy scheduling within an electrical microgrid, with a specific focus on enhancing the integration of electric vehicles (EVs). By incorporating Monte Carlo simulation to address uncertainties related to EV charging power and demand-side variables, the study aims to ensure precise outcomes. The economic energy scheduling is conducted on a day-ahead basis, taking these uncertainties into consideration to assess the efficiency of the recommended approach. The primary objective is to reduce the overall system costs, encompassing operational expenditures and EV charging power. To tackle the intricacies of the operational framework, the study utilizes the modified sunflower optimization (MSFO) algorithm to resolve the outlined issue. The simulation findings highlight the superior performance of the proposed optimization algorithms compared to others. The proposed approach leads to minimize cost of microgrid by 4.31%, 3.82% and 1.87% than genetic algorithm (GA), Particle swarm optimization (PSO) algorithm and Teaching learning-based optimization (TLBO) algorithm, respectively.

Dual Attention Graph Convolutional Network optimized with the Sun Flower Optimization for Differential Privacy Protection Mechanism for Smart Grid against Security Attacks

A smart grid combines information and communication technology with the conventional power system to provide efficient and dependable electricity generation, transmission, distribution, and control. Utilities and users share information and electricity in a smart grid. This manuscript presents a Dual Attention Graph Convolutional Network (DAGCN) optimized with the Sun Flower Optimization Algorithm (SFO) predicting the differential privacy protection mechanism for smart grid (DPSG-DAGCN-SFO). Initially, the data is collected from NSL-KDD datasets. Afterward, the data is fed to an Adaptive Robust Cubature Kalman Filter (ARCKF) based preprocessing process. Then the preprocessed data’s are fed to Adaptive and Concise Empirical Wavelet Transform (ACEWT)for extracting features such as authentication, integrity and trusted authority. The extracted features are fed to Dual Attention Graph Convolutional Network (DAGCN)to classify the privacy protection in smart grid such as stealthy attack and no attack. The weight parameters of DAGCN are optimized using SFO. The proposed DPSG-DAGCN-SFO is implemented in python, effectiveness assessed by several performance metrics such as accuracy, error rate, precision, sensitivity, F1-score and recall. The proposed method error rate attains 2% stealthy attack and4% no attack of the of the privacy protection in smart grid. The proposed method shows better results in all existing systems like Bayesian Clustering Algorithm (BCA), Self-Adaptive Grid-Partitioning Algorithm (SGNA) and Elliptic Curve Digital Signature Algorithm (ECDSA).From the result it is concludes that the proposed DPSG-DAGCN-SFO method based error rate lower than the existing methods.

An improved sunflower optimization algorithm for cluster selection with secured data transmission using cryptographic techniques in WSNS-IoT

An improved sunflower optimization algorithm for cluster selection with secured data transmission using cryptographic techniques in WSNS-IoT

Improved Sunflower Optimization Algorithm Based Encryption with Public Auditing Scheme in Secure Cloud Computing

Improved Sunflower Optimization Algorithm Based Encryption with Public Auditing Scheme in Secure Cloud Computing

Improving Kinnow Fruit Classification with Feature Weighting using Modified Levy Flight Distribution-Inspired Sunflower Optimization Technique

In this paper, Feature weighting is used to create an intelligent and effective classification method for Kinnow fruits. Feature weighting approach is used because it improves classification performance more than feature selection methods. The modified sunflower optimization algorithm (SFO) is proposed to search the optimal feature weights and parametric values of k-Nearest Neighbour (kNN).The levy flight distribution operator has been utilised to enhance the convergence speed of the sunflower optimization algorithm by improving the local and global search ability of the optimization algorithm. Also, the algorithmic parameter of the SFO algorithm has been adaptively selected using the linear time varying adaption method. In addition, tanh normalization technique is used for the data pre-processing to reduce the influence of outliers and dominating features before the feature weighting method. The findings suggest that the proposed wrapper based approach feature weighting technique is more capable of achieving higher accuracy than the existing strategies.

Projection of monthly surface flows by an optimized SWAT–MLP: a case study

Abstract Recent investigations have noted that using a hybrid arrangement of Soil and Water Assessment Tool (SWAT) and multi-layer perceptron (MLP) has high efficiency in runoff prediction. In this research, in addition to using the SWAT and MLP models, an optimized algorithm called Mutated SunFlower Optimization (MSFO) algorithm has been proposed to predict better runoff, which improves the results of prediction runoff by decreasing the error percentage in the MLP model. For this purpose, first, runoff modeling is used to assess the efficiency of the SWAT system. The model's verification and calibration have been performed using data from the previous 30 years of statistics. Then, the flow stream simulated by the SWAT method is evaluated with the observational data and applied as the inputs to the MLP model, and finally, runoff is predicted through the MLP model, and MSFO is used in the MLP model to obtain better results for runoff prediction. The results show that the values of statistical indices R2, RMSE, NSE, and RE give satisfying agreement for runoff forecast in the SWAT–MLP/MSFO model with values of 0.83, 1.68, 0.51, and −0.1.

Reliable detection of blast disease in rice plant using optimized artificial neural network

Early plant disease diagnosis is necessary for several purposes, including reducing yield losses, monitoring and predicting infections, detecting host resistance, and studying basic host–pathogen biological processes. However, early detection has been limited by a trained workforce and the ability to identify the problem early in the growing season. Artificial intelligence/machine learning algorithms can help fill this gap. Automatic leaf detection using machine learning is proposed in this study. The presented approach consists of three stages: pre‐processing, feature extraction, and classification. Initially, the input image is transformed into the red, green, and blue formatand the noise in the green band is removed using a median filter. Then important features of the green band are extracted. After feature extraction, the extracted features are fed to the optimized artificial neural network classifier to classify an image as normal or diseased. To improve artificial neural network (ANN) performance, the ANN parameters are chosen optimally using the adaptive sunflower optimization (ASFO) algorithm. Then, the infected region is separated using a level set segmentation algorithm. The efficiency of our work is analyzed based on accuracy, sensitivity, and specificity; the proposed method reached the maximum accuracy of 97.94% for plant disease prediction.

A Hybrid Many-Objective Optimization Algorithm for Job Scheduling in Cloud Computing Based on Merge-and-Split Theory

Scheduling jobs within a cloud environment is a critical area of research that necessitates meticulous analysis. It entails the challenge of optimally assigning jobs to various cloud servers, each with different capabilities, and is classified as a non-deterministic polynomial (NP) problem. Many conventional methods have been suggested to tackle this difficulty, but they often struggle to find nearly perfect solutions within a reasonable timeframe. As a result, researchers have turned to evolutionary algorithms to tackle this problem. However, relying on a single metaheuristic approach can be problematic as it may become trapped in local optima, resulting in slow convergence. Therefore, combining different metaheuristic strategies to improve the overall system enactment is essential. This paper presents a novel approach that integrates three methods to enhance exploration and exploitation, increasing search process efficiency and optimizing many-objective functions. In the initial phase, we adopt cooperative game theory with merge-and-split techniques to train computing hosts at different utilization load levels, determining the ideal utilization for each server. This approach ensures that servers operate at their highest utilization range, maximizing their profitability. In the second stage, we incorporate the mean variation of the grey wolf optimization algorithm, making significant adjustments to the encircling and hunting phases to enhance the exploitation of the search space. In the final phase, we introduce an innovative pollination operator inspired by the sunflower optimization algorithm to enrich the exploration of the search domain. By skillfully balancing exploration and exploitation, we effectively address many-objective optimization problems. To validate the performance of our proposed method, we conducted experiments using both real-world and synthesized datasets, employing CloudSim software version 5.0. The evaluation involved two sets of experiments to measure different evaluation metrics. In the first experiment, we focused on minimizing factors such as energy costs, completion time, latency, and SLA violations. The second experiment, in contrast, aimed at maximizing metrics such as service quality, bandwidth utilization, asset utilization ratio, and service provider outcomes. The results from these experiments unequivocally demonstrate the outstanding performance of our algorithm, surpassing existing state-of-the-art approaches.

A new intelligent sunflower optimization based explainable artificial intelligence approach for early‐age concrete compressive strength classification and mixture design of RAC

AbstractThis study aims to develop a new artificial intelligence model that can produce explainable rules to predict the mix design and early‐age concrete compressive strength classes of recycled aggregate concrete (RAC). Unlike other black‐box machine learning methods and rule‐based algorithms, the study relies on a metaheuristic mechanism for explainability. This metaheuristic mechanism is not used for a traditional parameter optimization, but to automatically extract interpretable and interpretable rules from the experimental data. In the study, 30 series of RACs are produced, and the samples’ 1‐ and 3‐day early‐age concrete compressive strength values are determined. Concretes produced using these strength values are classified. The labels defined for each concrete class are Class A (C 8/10), Class B (C 12/15), Class C (C 16/20), and Class D (C 20/25). The proposed intelligent classification model that consists of rule set automatically produces interpretable and comprehensible rules from data to determine the early‐age concrete compressive strength class and RAC mix amounts. In addition, the proposed method eliminates the black‐box disadvantages of classical machine learning methods with its explainability and interpretability feature. The sunflower optimization algorithm is adapted as the metaheuristic mechanism and a special fitness function and representative solution form are developed for automatic extraction of high‐quality comprehensible rules by simultaneously optimizing many different metrics. This paper is the first interpretable and comprehensible artificial intelligence model attempt used for early‐age compressive strength classification and mixture design of recycled aggregate concrete by balancing and optimizing both the accuracy and explainability. Proposed explainable intelligent classification model is tested against both well‐known state‐of‐the‐art machine learning algorithms and standard rule‐based methods on the produced real data. Promising results in terms of accuracy, precision, recall are obtained along with the explainability feature.

Optimization of functionally graded plates under Thermo-elastic free vibration using nature-based algorithms

This article presents the parametric optimization of the free vibration characteristics of a functionally graded material (FGM) plate exposed to nonlinear thermal loading using the finite element method and nature-based algorithms. The one-dimensional (1D) Fourier heat conduction equation is used to calculate temperature distributions over the thickness of the plate. Using Lagrange’s equation, we get the dynamic equation of motion for the plate. An eight-noded iso-parametric plate element with five degrees of freedom per node is used in the finite element formulation based on first-order shear deformation theory. Rectangular plates have temperature-dependent material characteristics; the thickness of the plates is scaled using a straightforward power law distribution. Here, the investigation of the FGM plate is conducted with two different boundary conditions, such as simple support and fully clamped. Additionally, new hybrid optimization approaches, namely RSM-Composite Desirability Optimization (RSM-CDO), Whale Optimization Algorithm (WOA), Corrected Moth Search Optimization (CMSO), Lichtenberg Algorithm Optimization (LAO), Sunflower Optimization Algorithm (SFO), and Forensic-Based Investigation Algorithm (FBI), are utilised to determine the best optimal solution, and the obtained findings are validated using confirmatory tests.

Sunflower optimization algorithm for multi-strategy-assist parameter identification of solar cell models

A novel optimization method, namely, the elite opposition learning and polynomial steps-based sunflower optimization (EOPSFO) algorithm, has been proposed to solve engineering problems. To speed up the convergence, the elite opposition-based learning and polynomial steps strategy is applied to automatically determine the search step adapted in each iteration. To verify the performance of EOPSFO, the feasibility of EOPSFO is first verified using various benchmarking and some standard optimization problems. In addition, EOPSFO is used to determine the parameters of the single diode model and double diode model. Results show that EOPSFO can be regarded as a competitive algorithm in optimization problems.

Hybrid optimization enabled deep learning model for Parkinson's disease classification

ABSTRACT The analysis of Parkinson's disease (PD) is an inspiring task that necessitates the analysis of numerous motor and non-motor indications. During analysis, some abnormalities are considered as important symptoms to analyze the disease. Hence, this research introduced the proposed chronological smart sunflower optimization Algorithm (CSSFOA) for classifying the PD from voice data and voice signal samples. For voice signal, the input signals are pre-processed by the Gaussian filter, and then the significant features are extracted from it. The selection of optimal features is done by the chronological smart flower optimization Algorithm (CSFOA). The proposed CSFOA-based feature selection method considered the features by the Bray–Curtis distance. The PD classification is done by the ZF-Net which is trained by proposed CSSFOA to increase the performance of classification. The experimental result reveals that the proposed CSSFOA_ZF-Net algorithm got a better testing accuracy of 0.945, a sensitivity of 0.919, and a specificity of 0.957.

Efficient Prediction of Blast Disease in Paddy Plant using Optimized Support Vector Machine

Diagnostics of plant diseases are essential to predicting yield loss, detecting infections, studying host–pathogen interactions, and detecting host resistance. Early detection of plant leaf disease can increase yields. This is possible if there are automated systems to help farmers diagnose rice diseases from the pictures of plant leaves. Therefore, automatic leaf detection using machine learning is proposed in this study. The proposed approach has three stages, namely, pre-processing, feature extraction, and classification. Initially, the input image is converted into red, green and blue and the noise in the green band is removed using a median filter. Initially, the input image is converted into red, green and blue and the noise in the green band is removed using a median filter. Then, important features of the green band are extracted. An Optimized Support Vector Machine (OSVM) classifier uses the extracted features to classify an image as normal or pathological. To improve SVM performance, the SVM parameters are chosen optimally using the Adaptive Sunflower Optimization (ASFO) algorithm. Then, the infected region is separated using a level-set segmentation algorithm. The efficiency of our work is analyzed based on accuracy, sensitivity, and specificity, the proposed method reached the maximum accuracy of 97.54% for plant disease prediction.

Hybrid K-Medoids with Energy-Efficient Sunflower Optimization Algorithm for Wireless Sensor Networks

Wireless sensor network (WSN) sensor nodes should have adequate energy. Reduced energy usage is essential to maximize the endurance of WSNs. Combining WSN with a more significant energy source, a cluster head (CH), is another effective strategy for extending WSN durability. A CH is dependent on the communication inside and between clusters. A CH’s energy level extends the cluster’s life for the complete WSN. Determining the energy required in WSNs while developing clustering algorithms is challenging. For maintaining energy efficiency in WSNs, this research offers K-medoids with sunflower-based clustering and a cross-layer-based optimal routing approach. An efficient fitness function generated from diverse objectives is used to choose the CH. After CH selection, sunflower optimization (SFO) indicates the best data transmission line to the sink node. The proposed protocol, SFO-CORP, increased the network lifetime by 19.6%, 13.63%, 11.13%, and 4.163% compared to the LEACH, EECRP, FEEC-IIR, and CL-IoT protocols, respectively. The experimental results showed that it performed better for packet delivery ratio, energy consumption, end-to-end delay, network lifetime, and computation efficiency.

Optimized levy flight model for heart disease prediction using CNN framework in big data application

Cardiac disease is one of the most complex diseases globally. It affects the lives of humans critically. It is essential for accurate and timely diagnosis to treat heart failure and prevent the disease. In most aspects, it was not so successful with the traditional method, which uses past medical history. Many existing models had several types of the loss function in traditional CNN can lead to misidentification of the model. To solve this problem, so many scholars have used the swarm intelligence algorithm, but most of these techniques are stuck in the local minima and suffer from premature convergence. In the proposed method, we build up the Levy Flight – Convolutional Neural Network (LV-CNN) depending on the diagnostic system using heart disease image data set for heart disease assessment. Initially, the input Big Data images are resized to reduce the computational complexity of the system. Then, those resized images are subject to the proposed LV-CNN model. Therefore, the LV approach is integrated with the Sunflower Optimization Algorithm (SFO) to reduce loss function occurring in the CNN architecture. Such a combination helps the SFO algorithm avoid trapping in local minima due to the random walk of the levy flight. The proposed algorithm will be simulated using the MATLAB tool and tested experimentally in terms of accuracy is 95.74%, specificity is 0.96%, the error rate is 0.35, and time consumption is 9.71 s. This comparative analysis revealed that the excellence of the proposed model.

Reliable cluster based data aggregation scheme for IoT network using hybrid deep learning techniques

BackgroundSeveral IoT nodes are deployed in the monitoring environment to ensure reliability. In both sensor and sink nodes, the same data is sensed and forwarded. While redundant data maintains reliability, sink nodes waste energy processing the redundant data. ObjectiveIn order to upholdthe compromiseamong energy ingesting and reliability, necessary to eliminate the redundancies in sensed data up to an appropriate level. Data aggregation algorithms currently assign time slots based on data sensing period and program rate, disregarding packet loss and latency. MethodologyIn this paper, we suggest a cluster based reliable data aggregation (CRDA) scheme for IoTnetwork which ensures data collection and aggregation in energy efficient manner and transfer to another end very effectively. We first introduce a monarch and sine-cosine (MSC) algorithm to form clusters by grouping the IoT sensors which ensures the effective data transferring. In data aggregation phase, we utilize the multiple design metrics to compute the trust degree of each IoT sensors and design an improved sunflower optimization (ISFO) algorithm to optimize the design constraints. The highest trust degree owned swelling is act as cluster head (CH) of the cluster which ensures data aggregation. A reformative optimal–learning-based deep neural network (ROL-DNN) is then used to compute routes between IoT sensors which ensures reliabledata aggregation and transferring. Resultsand analysis: Finally, we validate our proposed routing with the different simulation scenario and their results are compared with the existing routing protocols to prove the effectiveness.

Internet of things based smart application for rice leaf disease classification using optimization integrated deep maxout network

SummaryRice is the major crop in India. Early prevention and timely identification of plant leaf diseases are important for increasing production. Hence, an effective sunflower earthworm algorithm and student psychology based optimization (SEWA‐SPBO) based deep maxout network is developed to classify different types of diseases in rice plant leaf. The SEWA is the combination of sunflower optimization (SFO) and earthworm algorithm (EWA). Initially, the network nodes simulated in the environment capture the plant leaf images and are routed to the sink node for disease classification. After receiving the plant images at the sink node, the image is preprocessed using a Gaussian filter. Next to preprocessing, segmentation using the black hole entropic fuzzy clustering (BHEFC) mechanism is performed. Then, data augmentation is applied to segmented image results and disease classification is done by a deep maxout network. The training of the deep maxout network is done using the proposed SEWA‐SPBO algorithm. The proposed method detects the leaf disease more accurately with limited time and shows higher accuracy. Moreover, the proposed method attains higher performance with metrics, like accuracy, sensitivity, and specificity as 93.626%, 94.626%, and 90.431%, respectively.

Intelligent Intrusion Detection System for Industrial Internet of Things Environment

Rapid increase in the large quantity of industrial data, Industry 4.0/5.0 poses several challenging issues such as heterogeneous data generation, data sensing and collection, real-time data processing, and high request arrival rates. The classical intrusion detection system (IDS) is not a practical solution to the Industry 4.0 environment owing to the resource limitations and complexity. To resolve these issues, this paper designs a new Chaotic Cuckoo Search Optimization Algorithm (CCSOA) with optimal wavelet kernel extreme learning machine (OWKELM) named CCSOA-OWKELM technique for IDS on the Industry 4.0 platform. The CCSOA-OWKELM technique focuses on the design of feature selection with classification approach to achieve minimum computation complexity and maximum detection accuracy. The CCSOA-OWKELM technique involves the design of CCSOA based feature selection technique, which incorporates the concepts of chaotic maps with CSOA. Besides, the OWKELM technique is applied for the intrusion detection and classification process. In addition, the OWKELM technique is derived by the hyperparameter tuning of the WKELM technique by the use of sunflower optimization (SFO) algorithm. The utilization of CCSOA for feature subset selection and SFO algorithm based hyperparameter tuning leads to better performance. In order to guarantee the supreme performance of the CCSOA-OWKELM technique, a wide range of experiments take place on two benchmark datasets and the experimental outcomes demonstrate the promising performance of the CCSOA-OWKELM technique over the recent state of art techniques.