Ant Lion Optimization Approach for Load Frequency Control of Multi-Area Interconnected Power Systems

This work proposes a novel nature-inspired algorithm called Ant Lion Optimizer (ALO). The ALO algorithm mimics the search mechanism of antlions in nature. A time domain based objective function is established to tune the parameters of the PI controller based LFC, which is solved by the proposed ALO algorithm to reach the most convenient solutions. A three-area interconnected power system is investigated as a test system under various loading conditions to confirm the effectiveness of the suggested algorithm. Simulation results are given to show the enhanced performance of the developed ALO algorithm based controllers in comparison with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Bat Algorithm (BAT) and conventional PI controller. These results represent that the proposed BAT algorithm tuned PI controller offers better performance over other soft computing algorithms in conditions of settling times and several performance indices.

MOALG: A Metaheuristic Hybrid of Multi-Objective Ant Lion Optimizer and Genetic Algorithm for Solving Design Problems

This study proposes a hybridization of two efficient algorithm’s Multi-objective Ant Lion Optimizer Algorithm(MOALO)which is a multi-objective enhanced version of the Ant Lion Optimizer Algorithm(ALO)and the Genetic Algorithm(GA).MOALO version has been employed to address those problems containing many objectives and an archive has been employed for retaining the non-dominated solutions.The uniqueness of the hybrid is that the operators like mutation and crossover of GA are employed in the archive to update the solutions and later those solutions go through the process of MOALO.A first-time hybrid of these algorithms is employed to solve multi-objective problems.The hybrid algorithm overcomes the limitation of ALO of getting caught in the local optimum and the requirement of more computational effort to converge GA.To evaluate the hybridized algorithm’s performance,a set of constrained,unconstrained test problems and engineering design problems were employed and compared with five well-known computational algorithms-MOALO,Multi-objective Crystal Structure Algorithm(MOCryStAl),Multi-objective Particle Swarm Optimization(MOPSO),Multi-objective Multiverse Optimization Algorithm(MOMVO),Multi-objective Salp Swarm Algorithm(MSSA).The outcomes of five performance metrics are statistically analyzed and the most efficient Pareto fronts comparison has been obtained.The proposed hybrid surpasses MOALO based on the results of hypervolume(HV),Spread,and Spacing.So primary objective of developing this hybrid approach has been achieved successfully.The proposed approach demonstrates superior performance on the test functions,showcasing robust convergence and comprehensive coverage that surpasses other existing algorithms.

Hybridization of Fuzzy and Hard Semi-Supervised Clustering Algorithms Tuned with Ant Lion Optimizer Applied to Higgs Boson Search

This paper focuses on the unsupervised detection of the Higgs boson particle using the most informative features and variables which characterize the“Higgs machine learning challenge 2014”data set.This unsupervised detection goes in this paper analysis through 4 steps:(1)selection of the most informative features from the considered data;(2)definition of the number of clusters based on the elbow criterion.The experimental results showed that the optimal number of clusters that group the considered data in an unsupervised manner corresponds to 2 clusters;(3)proposition of a new approach for hybridization of both hard and fuzzy clustering tuned with Ant Lion Optimization(ALO);(4)comparison with some existing metaheuristic optimizations such as Genetic Algorithm(GA)and Particle Swarm Optimization(PSO).By employing a multi-angle analysis based on the cluster validation indices,the confusion matrix,the efficiencies and purities rates,the average cost variation,the computational time and the Sammon mapping visualization,the results highlight the effectiveness of the improved Gustafson-Kessel algorithm optimized withALO(ALOGK)to validate the proposed approach.Even if the paper gives a complete clustering analysis,its novel contribution concerns only the Steps(1)and(3)considered above.The first contribution lies in the method used for Step(1)to select the most informative features and variables.We used the t-Statistic technique to rank them.Afterwards,a feature mapping is applied using Self-Organizing Map(SOM)to identify the level of correlation between them.Then,Particle Swarm Optimization(PSO),a metaheuristic optimization technique,is used to reduce the data set dimension.The second contribution of thiswork concern the third step,where each one of the clustering algorithms as K-means(KM),Global K-means(GlobalKM),Partitioning AroundMedoids(PAM),Fuzzy C-means(FCM),Gustafson-Kessel(GK)and Gath-Geva(GG)is optimized and tuned with ALO.

Swarm Optimization and Machine Learning for Android Malware Detection

Malware Security Intelligence constitutes the analysis of applications and their associated metadata for possible security threats.Application Programming Interfaces(API)calls contain valuable information that can help with malware identification.The malware analysis with reduced feature space helps for the efficient identification of malware.The goal of this research is to find the most informative features of API calls to improve the android malware detection accuracy.Three swarm optimization methods,viz.,Ant Lion Optimization(ALO),Cuckoo Search Optimization(CSO),and Firefly Optimization(FO)are applied to API calls using auto-encoders for identification of most influential features.The nature-inspired wrapperbased algorithms are evaluated using well-known Machine Learning(ML)classifiers such as Linear Regression(LR),Decision Tree(DT),Random Forest(RF),K-Nearest Neighbor(KNN)&SupportVector Machine(SVM).A hybrid Artificial Neuronal Classifier(ANC)is proposed for improving the classification of android malware.The experimental results yielded an accuracy of 98.87%with just seven features out of hundred API call features,i.e.,a massive 93%of data optimization.

Optimal Deep Belief Network Based Lung Cancer Detection and Survival Rate Prediction

The combination of machine learning(ML)approaches in healthcare is a massive advantage designed at curing illness of millions of persons.Several efforts are used by researchers for detecting and providing primary phase insights as to cancer analysis.Lung cancer remained the essential source of disease connected mortality for both men as well as women and their frequency was increasing around the world.Lung disease is the unrestrained progress of irregular cells which begin off in one or both Lungs.The previous detection of cancer is not simpler procedure however if it can be detected,it can be curable,also finding the survival rate is a major challenging task.This study develops an Ant lion Optimization(ALO)with Deep Belief Network(DBN)for Lung Cancer Detection and Classification with survival rate prediction.The proposed model aims to identify and classify the presence of lung cancer.Initially,the proposed model undergoes min-max data normalization approach to preprocess the input data.Besides,the ALO algorithm gets executed to choose an optimal subset of features.In addition,the DBN model receives the chosen features and performs lung cancer classification.Finally,the optimizer is utilized for hyperparameter optimization of the DBN model.In order to report the enhanced performance of the proposed model,a wide-ranging experimental analysis is performed and the results reported the supremacy of the proposed model.

Ant Lion Algorithm for Optimized Controller Gains for Power Quality Enrichment of Off-grid Wind Power Harnessing Units

The proposed system uses an algorithm that works on the admittance of the system,for estimating the reference values of generated currents for an off-grid wind power harnessing unit(WPHU).The controller controls the voltage and maintains the frequency within the limits while working with both linear and nonlinear loads for varying wind speeds.The admittance algorithm is simple and easy to implement and works very efficiently to generate the triggering signals for the controller of the WPHU.The wind power harnessing unit comprising of a squirrel cage induction generator,a star-delta transformer,a battery storage system and the control unit are modeled using Matlab/Simulink R2019.An isolated transformer with a star-delta configuration connects the load and the generator circuit with the controller to reduce the dc bus voltage and mitigate current in the neutral line.The response of the system during the dynamic loading depends on the best possible compensator proportional-integral(PI)gains.The antlion optimization algorithm is compared with particle swarm optimization and grey wolf optimization and is found to have the advantages of good convergence,high efficiency and fast calculating speed.It is therefore used to extract the optimal values of frequency and voltage PI gains.The simulation results of the control algorithm for the WPHU are validated in a real-time environment in a dSpace1104 laboratory set up.This algorithm is proven to have a quick response,maintain the required frequency,suppress the current harmonics,regulate voltage,help in balancing the load and compensating for the neutral current.

An Automatic Threshold Selection Using ALO for Healthcare Duplicate Record Detection with Reciprocal Neuro-Fuzzy Inference System

ESystems based on EHRs(Electronic health records)have been in use for many years and their amplified realizations have been felt recently.They still have been pioneering collections of massive volumes of health data.Duplicate detections involve discovering records referring to the same practical components,indicating tasks,which are generally dependent on several input parameters that experts yield.Record linkage specifies the issue of finding identical records across various data sources.The similarity existing between two records is characterized based on domain-based similarity functions over different features.De-duplication of one dataset or the linkage of multiple data sets has become a highly significant operation in the data processing stages of different data mining programmes.The objective is to match all the records associated with the same entity.Various measures have been in use for representing the quality and complexity about data linkage algorithms,and many other novel metrics have been introduced.An outline of the problem existing in themeasurement of data linkage and de-duplication quality and complexity is presented.This article focuses on the reprocessing of health data that is horizontally divided among data custodians,with the purpose of custodians giving similar features to sets of patients.The first step in this technique is about an automatic selection of training examples with superior quality from the compared record pairs and the second step involves training the reciprocal neuro-fuzzy inference system(RANFIS)classifier.Using the Optimal Threshold classifier,it is presumed that there is information about the original match status for all compared record pairs(i.e.,Ant Lion Optimization),and therefore an optimal threshold can be computed based on the respective RANFIS.Febrl,Clinical Decision(CD),and Cork Open Research Archive(CORA)data repository help analyze the proposed method with evaluated benchmarks with current techniques.

Incremental Learning Framework for Mining Big Data Stream

At this current time,data stream classification plays a key role in big data analytics due to its enormous growth.Most of the existing classification methods used ensemble learning,which is trustworthy but these methods are not effective to face the issues of learning from imbalanced big data,it also supposes that all data are pre-classified.Another weakness of current methods is that it takes a long evaluation time when the target data stream contains a high number of features.The main objective of this research is to develop a new method for incremental learning based on the proposed ant lion fuzzy-generative adversarial network model.The proposed model is implemented in spark architecture.For each data stream,the class output is computed at slave nodes by training a generative adversarial network with the back propagation error based on fuzzy bound computation.This method overcomes the limitations of existing methods as it can classify data streams that are slightly or completely unlabeled data and providing high scalability and efficiency.The results show that the proposed model outperforms stateof-the-art performance in terms of accuracy(0.861)precision(0.9328)and minimal MSE(0.0416).

Moving Multi-Object Detection and Tracking Using MRNN and PS-KM Models

On grounds of the advent of real-time applications,like autonomous driving,visual surveillance,and sports analysis,there is an augmenting focus of attention towards Multiple-Object Tracking(MOT).The tracking-by-detection paradigm,a commonly utilized approach,connects the existing recognition hypotheses to the formerly assessed object trajectories by comparing the simila-rities of the appearance or the motion between them.For an efficient detection and tracking of the numerous objects in a complex environment,a Pearson Simi-larity-centred Kuhn-Munkres(PS-KM)algorithm was proposed in the present study.In this light,the input videos were,initially,gathered from the MOT dataset and converted into frames.The background subtraction occurred whichfiltered the inappropriate data concerning the frames after the frame conversion stage.Then,the extraction of features from the frames was executed.Afterwards,the higher dimensional features were transformed into lower-dimensional features,and feature reduction process was performed with the aid of Information Gain-centred Singular Value Decomposition(IG-SVD).Next,using the Modified Recurrent Neural Network(MRNN)method,classification was executed which identified the categories of the objects additionally.The PS-KM algorithm identi-fied that the recognized objects were tracked.Finally,the experimental outcomes exhibited that numerous targets were precisely tracked by the proposed system with 97%accuracy with a low false positive rate(FPR)of 2.3%.It was also proved that the present techniques viz.RNN,CNN,and KNN,were effective with regard to the existing models.

Sensitive Integration of Multilevel Optimization Model in Human Activity Recognition for Smartphone and Smartwatch Applications

This study proposes an intelligent data analysis model for finding optimal patterns in human activities on the basis of biometric features obtained from four sensors installed on smartphone and smartwatch devices. The proposed model, referred to as Scheduling Activities of smartphone and smartwatch based on Optimal Pattern Model(SA-OPM), consists of four main stages. The first stage relates to the collection of data from four sensors in real time(i.e., two smartphone sensors called accelerometer and gyroscope and two smartwatch sensors of the same name).The second stage involves the preprocessing of the data by converting them into graphs. As graphs are difficult to deal with directly, a deterministic selection algorithm is proposed as a new method to find the optimal root to split the graphs into multiple subgraphs. The third stage entails determining the number of samples related to each subgraph by using the optimization technique called the lion optimization algorithm. The final stage involves the generation of patterns from the optimal subgraph by using the association pattern algorithm called g Span. The pattern finder based on Forward-Backward Rules(FBR) generates the optimal patterns and thus aids humans in organizing their activities. Results indicate that the proposed SA-OPM model generates robust and authentic patterns of human activities.

An M-ALO-SVR Model to Predict Output of Industry-University-Research Collaboration Network in China

The output prediction of industry-university-research cooperation network is a prerequisite for optimization of network resource allocation and improvement of network innovation performance.Accurate prediction of network output can provide data for feedback systems,offermethods and reference to government macro-level control,and avoid resource wastes caused by improper input of capital and humans.In this paper,a prediction model based on Ant Lion Optimizer and Support Vector Regression is proposed.First,the M-ALO-SVR model was built.Then,Pareto function and regulatory factors were applied to accelerate the convergence of ALO-SVR optimization,improving the global search ability of the algorithm.Finally,the empirical research of the industry-university-research cooperation network was implemented,and simulation experiments were conducted with samples of China Statistical Yearbooks.The results show that the M-ALO-SVR model performs well in the innovation network output prediction.The predictive goodness of fit of the model reaches 99.7%,improved by 0.02%and 3.3%respectively compared with that of ALO-SVR model and SVM.The running time of the model is two seconds fewer than that of the ALO-SVR model.In addition,the optimizing function of the model converges at higher speed and its MSE is optimal.