Ant Lion Optimization Algorithm Research Articles

Optimized deep belief network and unsupervised deep learning methods for disease prediction

Due to the vast amount of patient health data, automated healthcare systems still struggle to classify and diagnose various ailments. Learning redundant data also reduces categorization accuracy. A Deep Belief Network (DBN) has been used to precisely extract the most important aspects from clinical data by ignoring irrelevant/redundant features. Due of many learning variables, training is complicated. Similarly, the hybrid model has been employed by ensemble Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) to categorize diseases. But, its efficiency depends on the proper choice of kernels and hyper-parameters. Therefore, this paper develops an efficient feature extraction and classification model for healthcare systems. First, several medical data related to the patient’s health are collected. Then, an Optimized DBN (ODBN) model is presented for maximizing the accurateness of DBN by optimizing the learning variables depends on the Ant Lion Optimization (ALO) algorithm. With learning ODBN, the most relevant features are extracted with reduced computational complexity. After that, the CNN-LSTM with Unsupervised Fine-tuned Deep Self-Organizing Map (UFDSOM)-based classifier model is designed to categorize the extracted features into categories of illnesses. In this novel classifier, dropout normalization and parameter tuning processes are applied to avoid overfitting and optimize the hyper-parameters, which results in a less training period. In the end, studies utilizing publically accessible datasets show that the ODBN with CNN-LSTM-UFDSOM system outperforms classical models by 98.23%.

Tetra Optimization Based Hybrid Parameters for OFDM Modulated Wireless Sensor Network

Orthogonal frequency-division multiplexing [OFDM] is an information transfer technique in which a single data flow is divided between several closely spaced narrowband subchannel frequency range rather than a single Wideband channel frequency. The information is sent to the relay node there is a delay and some data is lost in the relay node is the major issue in the existing system. To overcome these challenges, The objective of this study is to minimize the overall energy consumption and to maximize the network lifetime. In this paper, a novel Five Input Hybrid Optimization Relay Node Selection and Energy Efficient Routing (FIHORNSEER) technique has been proposed for choosing the best relay based on noises. Ant Lion Optimization (ALO) is initially utilized to select the relay node's elite position. Secondly, the Crow Search Optimization (CSO) Algorithm is used for the phenomenon of position and memory of each relay. Finally, the Memetic Algorithm (MA) was generated by integrating the Ant Lion and Crow search optimization algorithm for the best relay node selection. The proposed framework is compared with previous techniques like FRNSEER, LMMSE, and HABO-OFDM Methods in terms of performance analysis, such as average utility, Energy Consumption, and Network Life Time. The result shows that the proposed FIHORNSEER improves the energy consumption better than 22.01%, 16.4%, and 12.2% FRNSEER, LMMSE, and HABO-OFDM, respectively.

A Generalized Classification Framework with Simultaneous Feature Weighting and Selection Using Antlion Optimization Algorithm

ABSTRACT The use of machine-learning based algorithms on a large-scale nondestructive evaluation (NDE) data considerably advances the NDE techniques toward complete industrial automation. In this article, simultaneous feature selection and feature weighting are carried out on the magnetic Barkhausen emission (MBE) dataset to demonstrate the significance of optimization in NDE data. Antlion optimization is employed as a searching method to determine the optimum feature set that will maximize the classification performance. The proposed framework is validated for different magnetization frequencies separately and found to be frequency independent. The framework resulted in the selection of four significant features extracted from the MBE response thereby reducing the computational effort and improving the accuracy to 98.4% for AdaBoost classifier. The developed machine learning methodology is a potential strategy for processing industrial sensory data since material testing, property prediction, and categorization are frequent tasks in manufacturing and production engineering industries. Further, this research demonstrated the necessity of embedded intelligence in automation of NDE toward Industrial Revolution 4.0.

Automated Prediction of Osteoarthritis Level in Human Osteochondral Tissue Using Histopathological Images.

Osteoarthritis (OA) is the most common arthritis and the leading cause of lower extremity disability in older adults. Understanding OA progression is important in the development of patient-specific therapeutic techniques at the early stage of OA rather than at the end stage. Histopathology scoring systems are usually used to evaluate OA progress and the mechanisms involved in the development of OA. This study aims to classify the histopathological images of cartilage specimens automatically, using artificial intelligence algorithms. Hematoxylin and eosin (HE)- and safranin O and fast green (SafO)-stained images of human cartilage specimens were divided into early, mild, moderate, and severe OA. Five pre-trained convolutional networks (DarkNet-19, MobileNet, ResNet-101, NasNet) were utilized to extract the twenty features from the last fully connected layers for both scenarios of SafO and HE. Principal component analysis (PCA) and ant lion optimization (ALO) were utilized to obtain the best-weighted features. The support vector machine classifier was trained and tested based on the selected descriptors to achieve the highest accuracies of 98.04% and 97.03% in HE and SafO, respectively. Using the ALO algorithm, the F1 scores were 0.97, 0.991, 1, and 1 for the HE images and 1, 0.991, 0.97, and 1 for the SafO images for the early, mild, moderate, and severe classes, respectively. This algorithm may be a useful tool for researchers to evaluate the histopathological images of OA without the need for experts in histopathology scoring systems or the need to train new experts. Incorporating automated deep features could help to improve the characterization and understanding of OA progression and development.

Non-destructive detection of polysaccharides and moisture in Ganoderma lucidum using near-infrared spectroscopy and machine learning algorithm

Ganoderma lucidum, the fruiting body of the Poraceae fungi G. lucidum or Ganoderma sinense, has a long history of use in promoting health and longevity in Asian countries. However, traditional methods for detecting polysaccharides and moisture in G. lucidum are complicated, time-consuming, and damaging (to the sample). In this study, rapid and nondestructive near-infrared (NIR) spectroscopy (700–2500 nm) was uesd to directly scan the back of the G. lucidum cap without powdering. Thereafter, we used synergy interval partial least squares to select the performing band and the ant lion optimization (ALO) algorithm to optimize the least squares support vector machine (LSSVM) model for these two components. The results showed that the ALO-LSSVM model could predict the total polysaccharide and moisture content with high accuracy. The correlation coefficient for calibration were both >0.9 and their ratio of prediction to deviation (RPD) values of prediction were 2.6 and 3.6, respectively, indicating the non-destructive determination of polysaccharides and moisture in G. lucidum will provide great convenience for on-site testing by procurement personnel. This shows the combination of NIR spectroscopy and the ALO-LSSVM algorithm has potential applications for the rapid and nondestructive analysis of natural products.

Coverage Enhancement Strategy for WSNs Based on Multiobjective Ant Lion Optimizer

In order to improve the quality of service and prolong the network lifetime of wireless sensor networks (WSNs), optimizing the network coverage rate and the sensor nodes moving distance in the secondary deployment process has become a crucial research object. Aiming at these two goals, this paper improves the classic multi-objective ant lion optimization algorithm, and proposes an improved multi-objective ant lion optimization algorithm based on fast non-dominated sorting (NSIMOALO). Firstly, we use the idea of fast non-dominated sorting algorithm and elite strategy of NSGA-II, which avoids the algorithm from falling into the local optimal solution and improves the solution accuracy of the algorithm. Secondly, a more reasonable congestion calculation equation is proposed, which greatly increases the diversity of the population. Finally, we introduce Lévy flight to update the ant position by dynamically changing the weight coefficients among the antlion, elite antlion and Lévy flight, which improves the global optimization ability of the algorithm. The standard function simulation results show that NSIMOALO algorithm has higher convergence and coverage. The algorithm is applied to WSNs sensor nodes deployment, and 80 sensor nodes are deployed in a monitoring area of 200m×300m. Compared with MOALO algorithm, NSGA-II and NSMOFPA, the coverage rate of NSIMOALO algorithm is increased by 12.753%, 12.413% and 4.492% respectively, and the sensor nodes average moving distance is decreased by 2.551m, 2.316m and 4.457m respectively.

Secured wireless sensor networks using hybrid Rivest Shamir Adleman with ant lion optimization algorithm

Secured wireless sensor networks using hybrid Rivest Shamir Adleman with ant lion optimization algorithm

PR controller-based droop control strategy for AC microgrid using Ant Lion Optimization technique

Microgrids are now an integral part of the modern power system to maintain the stability of the power distribution process. Ensuring the frequency and voltage stability in the autonomous mode is quite a challenging task indeed. These have now reached the extent of utilizing evolutionary intelligence techniques leading to the concept of intelligent microgrids. In this work, a novel droop control strategy is implemented with a proportional resonant controller, whose proportional gain is optimized by the Ant Lion Optimization algorithm (A.L.O). The efficacy of the A.L.O algorithm for its application in the primary level droop control for voltage stability is investigated in this work. The superiority of the A.L.O optimized PR controller over the conventional PI controller is compared in terms of error indices, timed domain specifications, speed of response, voltage regulation and T.H.D levels. It could ensure a higher speed of response, better voltage stability and higher power quality as against its PI counterpart. The performance of the proposed A.L.O optimized PR controller is validated by comparing it with the performance achieved by controllers tuned with two other algorithms namely, particle swarm optimization (P.S.O) and Satin bower bird optimization (S.B.O). The proposed controller could ensure better steady-state stability compared to the other two.

COMBINING GREY WOLF OPTIMIZATION AND WHALE OPTIMIZATION ALGORITHM FOR BENCHMARK TEST FUNCTIONS

Many optimization problems have been successfully addressed using metaheuristic approaches. These approaches are frequently able to choose the best answer fast and effectively. Recently, the use of swarm-based optimization algorithms, a kind of metaheuristic approach, has become more common. In this study, a hybrid swarm-based optimization method called WOAGWO is proposed by combining the Whale Optimization Algorithm (WOA) and Grey Wolf Optimization (GWO). This method aims to realize a more effective hybrid algorithm by using the positive aspects of the two algorithms. 23 benchmark test functions were utilized to assess the WOAGWO. By running the proposed approach 30 times, the mean fitness and standard deviation values were computed. These results were compared to WOA, GWO, Ant Lion Optimization algorithm (ALO), Particle Swarm Optimization (PSO), and Improved ALO (IALO) in the literature. The WOAGWO algorithm, when compared to these algorithms in the literature, produced the optimal results in 5 of 7 unimodal benchmark functions, 4 of 6 multimodal benchmark functions, and 9 of 10 fixed-dimension multimodal benchmark functions. Therefore, the suggested approach generally outperforms the findings in the literature. The proposed WOAGWO seems to be promising and it has a wide range of uses.

A floating offshore platform motion forecasting approach based on EEMD hybrid ConvLSTM and chaotic quantum ALO

Accurate prediction of floating offshore platform motion (FOPM) is very important to control the movement of the platform and the normal operation of the equipment on the platform. However, the accurate prediction of FOPM is very difficult, due to the coupling effect of the mooring system, operation system, wind, wave, and current. Therefore, in order to obtain more accurate prediction results, firstly, the Convolutional LSTM (ConvLSTM) network is introduced to simulate FOPM nonlinear dynamical system, design input vector coding rules, considering the characteristics of complex time-varying nonlinear and space non-stationary of FOPM. The EEMD is applied for modal decomposition for time series of FOPM to reduce the nonlinearity of time series. Then the FOPM-EEMD-ConvLSTM forecasting model is proposed by designing the input sequence matrix and prediction architecture. Considering the defects of ALO algorithm, based on quantum computing and chaotic mapping, the quantum global search algorithm (QRS) and ant lion trap Chaos reconstruction mechanism (ALTCR)are designed, and chaotic quantum ant lion optimization algorithm (CQALO) is proposed and use to optimate the hyperparametric of FOPM-EEMD- ConvLSTM. Consequently, a hybrid forecasting approach of FOPM integrating FOPM-EEMD- ConvLSTM and CQALO was established, namely FOPM-EEMD-ConvLSTM-CQALO. Finally, the sway and heave data of a floating offshore platform serving in the ocean is used to carry out prediction experiments to test the performance of the proposed new prediction approach. The test results indicate that the prediction model established in this paper has higher prediction accuracy and stronger robustness than the comparison model selected in this paper and the CQALO obtains more appropriate hyperparameters than the comparison algorithm applied in selecting the hyperparameters of FOPM-EEMD- ConvLSTM.

Optimization of Power System Stabilizers Using Proportional-Integral-Derivative Controller-Based Antlion Algorithm: Experimental Validation via Electronics Environment

A robust, optimized power system stabilizer (PSS) is crucial for oscillation damping, and thus improving electrical network stability. Additionally, real-time testing methods are required to significantly reduce the likelihood of software failure in a real-world setting at the user location. This paper presents an Antlion-based proportional integral derivative (PID) PSS to improve power system stability during real-time constraints. The Antlion optimization (ALO) is developed with real-time testing methodology, using hardware-in-the-loop (HIL) that can communicate multiple digital control schemes with real-time signals. The dynamic power system model runs on the dSPACE DS1104, and the proposed PSS runs on the field programmable gate arrays (FPGA) (NI SbRIO-9636 board). The optimized PSS performance was compared with a modified particle swarm optimization (MPSO)-based PID-PSS, through different performance indices. The test cases include other step load perturbations and several short circuit faults at various locations. Twelve different test cases have been applied, through real-time constraints, to prove the robustness of the proposed PSS. These include 5 and 10% step changes through 3 different operating conditions and single, double, and triple lines to ground short circuits through 3 different operating conditions, and at various locations of the system transmission lines. The analysis demonstrates the effectiveness of ALO and MPSO in regaining the system’s stability under the three loading conditions. The integral square of the error (ISE), integral absolute of the error (IAE), integral time square of the error (ITSE), and integral time absolute of the error (ITAE) are used as performance indices in the analysis stage. The simulation results demonstrate the effectiveness of the proposed PSS, based on the ALO algorithm. It provides a robust performance, compared to the traditional PSS. Regarding the applied indices, the proposed PSS, based on the ALO algorithm, obtains significant improvement percentages in ISE, IAE, ITSE, and ITAE with 30.919%, 23.295%, 51.073%, and 53.624%, respectively.

Two-Tier Feature Extraction with Metaheuristics-Based Automated Forensic Speaker Verification Model

While speaker verification represents a critically important application of speaker recognition, it is also the most challenging and least well-understood application. Robust feature extraction plays an integral role in enhancing the efficiency of forensic speaker verification. Although the speech signal is a continuous one-dimensional time series, most recent models depend on recurrent neural network (RNN) or convolutional neural network (CNN) models, which are not able to exhaustively represent human speech, thus opening themselves up to speech forgery. As a result, to accurately simulate human speech and to further ensure speaker authenticity, we must establish a reliable technique. This research article presents a Two-Tier Feature Extraction with Metaheuristics-Based Automated Forensic Speaker Verification (TTFEM-AFSV) model, which aims to overcome the limitations of the previous models. The TTFEM-AFSV model focuses on verifying speakers in forensic applications by exploiting the average median filtering (AMF) technique to discard the noise in speech signals. Subsequently, the MFCC and spectrograms are considered as the inputs to the deep convolutional neural network-based Inception v3 model, and the Ant Lion Optimizer (ALO) algorithm is utilized to fine-tune the hyperparameters related to the Inception v3 model. Finally, a long short-term memory with a recurrent neural network (LSTM-RNN) mechanism is employed as a classifier for automated speaker recognition. The performance validation of the TTFEM-AFSV model was tested in a series of experiments. Comparative study revealed the significantly improved performance of the TTFEM-AFSV model over recent approaches.

Intelligent Temperature Control of Pressure Gauge Calibration Based on ANT-LION Neural Network

Aiming at the actual industrial requirements of high accuracy and high efficiency in the industrial process of pressure gauge calibration, this paper studies the intelligent temperature control of the pressure gauge calibration system. Based on the physical mechanism of the calibration system, the controlled object model is established. According to the characteristics of large time delay and nonlinearity of the calibration industrial process, a neural network PID control method based on ANT-LION optimization algorithm is proposed. The experimental results show that the method is effective and reliable, which improves the speed and stability of the temperature control of the pressure gauge calibration system, and realizes the intelligent control of the temperature of the calibration system.

Optimal power flow analysis including stochastic renewable energy sources using modified ant lion optimization algorithm

The optimal power flow is one of the major area in economic and efficient dispatch of electric power. This research article presents a swarm-based optimization algorithm, known as Modified Ant Lion Optimization (MALO) algorithm to solve optimal power flow (OPF) problems. A holistic approach has been used including thermal, wind, solar, and hydro power plants and results shows minimization of cost, losses, and voltage deviation that amounts for novelty of this paper. The MALO algorithm is validated on IEEE 30-bus and IEEE 57-bus systems, and the result are compared with the state-of-the-art algorithms. It is found that proposed algorithm provides better OPF solutions when compared with other mentioned algorithm namely Antlion Optimization, Graw Wolf Optimization, Salp Swarm Algorithm, and Grasshopper Optimization. The MALO algorithm is useful for electric utilities, researcher, and power system operation.

Modified Fractional Order PID Controller for Load Frequency Control of Four Area Thermal Power System

This paper presents the development of a modified Fractional Order Proportional Integral Derivative (FOPID) controller to mitigate frequency deviation in a four-area thermal power system. Change in load demand and noisy power system environment can cause frequency deviation. Reducing high-frequency deviation is very paramount in load frequency control. This is because large frequency deviation can cause the transmission line to be overloaded, which may damage transformers at the transmission level, damage mechanical devices at the generating stations and also damage consumer devices at the distribution level. The conventional PID has been widely used for this problem. However, the parameter values of the various generating units of the power system like generators, turbines and governors keep changing due to numerous on/off witching in the load side. As such, it is essential that the control strategy applied should have a good capability of handling uncertainties in the system parameters and good disturbance rejection. Fractional order PID controller is known to give a higher phase margin resulting in very good disturbance rejection, robustness to high-frequency noise and elimination of steady-state error. A four-area power system was designed, and FOPID was used as the supplementary controller to mitigate frequency deviation. Ant Lion Optimizer (ALO) algorithm was used to optimize the gains of the FOPID controller by minimizing Integral Square Error (ISE) as the objective function. Results obtained outperformed other designed methods available in the literature in terms of reducing frequency deviation, tie-line power deviation and area control error.

The Maximum Power Point Tracking (MPPT) of a Partially Shaded PV Array for Optimization Using the Antlion Algorithm

The antlion optimizer (ALO) algorithm is used in this article for maximum power point tracking (MPPT) of a solar array. The solar array consists of a single module, while there are 20 cells in the module. The voltage and current ratings of each cell are 2 V and 2.5 A, making a 100 W array in ideal condition. However, the voltage and current characteristics of the PV cell are unable to achieve maximum power. Therefore, the ALO was used for MPPT. The results of the ALO are compared with the traditional metaheuristic approaches, perturb and observe (P&O) and flower pollination (FP) algorithms. Comparison of the ALO with the stated algorithms is conducted for two cases: when solar irradiance is 1000 W/m2 and when it drops to 200 W/m2 at first then reaches 1000 W/m2. The change of irradiance is performed to simulate the partial shading condition. The simulation results depict that maximum power for the first case using the ALO reaches 91.3 W in just 0.05 s, while the P&O and PFA reach 90 W after 0.64 and 2 s, respectively. For the case of partial shading, maximum power using the ALO drops to 55 W when irradiance decreases to 200 W/m2 and then increases with the increase in irradiance reaching 91.3 W which clearly shows that the ALO outperforms the P&O and FPA.

Design of a novel robust adaptive cascade controller for DC‐DC buck‐boost converter optimized with neural network and fractional‐order PID strategies

AbstractA cascade technique with two control loops is designed for a DC\DC Buck‐Boost converter that is a right half‐plane zero (RHPZ) structure called a non‐minimum phase system. This concept presents several challenging constraints for designing well‐behaved control techniques. Cascade controllers can provide various benefits compared with single loop controllers such as higher safety, higher robustness, and higher stability. This strategy assumes the system as a black‐box structure without the need for a mathematical model of the system. This benefit can decrease the computational burden and provides faster dynamics along with ease of implementation. This technique consisted of an outer Fractional‐order PID voltage controller tuned with the Antlion Optimizer (ALO) algorithm, which provides a reference current for the inner control loop of the Neural Network‐based LQR (NN‐LQR) controller. The basic principle in cascade controllers is a more rapid performance of the inner loop that has been satisfied with the NN‐LQR strategy, which optimizes and tunes the gains of the LQR controller and shows faster dynamics and higher robustness. It should be mentioned that the number of neurons is limited to 2 and 4 in each layer to decrease the computational burden with lower complexity. Also, the ALO algorithm is a modern nature‐inspired algorithm used to tune the PID gains with better results under‐constrained problems with diverse search spaces. Considering the negative impacts of various disturbances on a power converter, a Fractional‐order‐based PID (FO‐PID) control technique is a proper alternative since it shows higher robustness in load uncertainties along with better dynamical responses based on its extra degree of freedom. Moreover, to evaluate the superiority of this controller, two other controllers are designed using the PSO algorithm for PID and FO‐PID controllers. Finally, the presented cascade controller has been tested in various working conditions through simulation and experiment results.

Guest Editorial: Special issue on machine learning and deep learning algorithms for complex networks

Guest Editorial: Special issue on machine learning and deep learning algorithms for complex networks

Optimal allocation of distributed generators in radial distribution networks based on ant-lion optimizer

This paper presents the use of a recently developed algorithm inspired by the hunting mechanism of ant-lions in nature, called ant lion optimizer (ALO). The ALO algorithm is used for solving the voltage instability problem in radial distribution networks. The proposed method ensures system operation within voltage standard limits and with minimum power losses. The proposed method implies the following main tasks; load flow analysis with forwarding/backward sweep method, voltage stability index calculation and optimizing the distributed generators (size, location, and power factor) by applying the ALO. The brilliant performance of hybridizing these tasks will be able to give an overall view of voltage stability for radial distribution system; calculate the voltage stability index; detect the most sensitive node to collapse and presents a solution (to mitigate voltage instability problem and reduce system power losses). The performance and feasibility of the proposed method are demonstrated through simulation case on IEEE 69-bus. The obtained results provide the optimal solution.

CUDA implementation of the antlion optimization algorithm

A parallel version of Ant Lion Optimizer algorithm using the CUDA platform is introduced in this paper. Efficient kernel, memory and thread management approaches have been developed to maximize its performance. The new algorithm was tested against the canonical algorithm on 15 scalable problems of different sizes, with a total of 172 experiments. The solution costs and the execution times were compared using relative percentage difference and significance tests. The results showed the CUDA antlion algorithm significantly improves upon the execution time, while retaining the same solution quality.