Lion Optimization Research Articles

Cross-correlation stacking-based microseismic source location using three metaheuristic optimization algorithms

Microseismic location systems tend to be high-speed and precise. However, the requirement of high precision tends to slow down the calculation speed. Fortunately, metaheuristics are able to alleviate this problem. In this research, metaheuristic algorithms are used to improve the performance of cross-correlation stacking (CCS). CCS has able to provide excellent location accuracy as it uses more information in the entire waveform for location. However, this method often requires more calculation time due to its complex mathematical modeling. To overcome this problem, various metaheuristic algorithms (i.e. moth flame optimization (MFO), ant lion optimization (ALO) and grey wolf optimization (GWO)) have been used to improve CCS. It has been found that appropriate control parameters can improve the metaheuristic algorithm performance manyfold. So, these control parameters have been adjusted based on three different perspectives, i.e. success rate (SR), computational efficiency and convergence performance. The results show that these models are able to provide better location efficiency compared to the full grid search (FGS) and particle swarm optimization (PSO) based on ensuring good location accuracy. It is also found that MFO is significantly better than the other metaheuristic algorithms. In addition, the superiority of CCS over traditional location methods is verified through comprehensive tests, and the influence of the speed model and the number of sensors on the location performance of CCS was tested.

BSLnO: Multi‐agent based distributed intrusion detection system using Bat Sea Lion Optimization‐based hybrid deep learning approach

SummaryIntrusion detection system (IDS) is a robust model that plays an essential role in dealing with intrusion detection, especially in detecting abnormal anomalies and unknown attacks. The major challenges faced by IDS are the computation time required for analysis, and the exchange of a huge amount of data from one division of the network to another. For the sake of tackling such limitations, this probe proposes a multi‐agent enabled IDS for detecting intrusions using the Bat Sea Lion Optimization (BSLnO) algorithm. The proposed strategy consists of five phases, namely pre‐processor agent, reducer agent, augmentation agent, classifier agent, and decision agent. Initially, input data is subjected to pre‐processor agent, where pre‐processing is carried out using data normalization and missing value imputation. Thereafter, the pre‐processed result is fed up to the reducer agent, where dimension reduction is carried out using mutual information. The third step is data augmentation in which the dimensionality of data is enhanced. After that, the augmented result is subjected to classifier agent to classify intrusions or malicious activities present in the network based on hybrid deep learning strategies, namely deep maxout network and deep residual network. A developed BSLnO is implemented by incorporating Bat Algorithm (BA) and Sea Lion Optimization (SLnO) algorithm to train the hybrid classifier. The proposed scheme has acquired a higher precision of 0.936, recall of 0.904, and F‐measure of 0.920.

Mitigation of Low-Frequency Oscillation in Power Systems through Optimal Design of Power System Stabilizer Employing ALO

Low-frequency oscillations are an inevitable phenomenon of a power system. This paper proposes an Ant lion optimization approach to optimize the dual-input power system stabilizer (PSS2B) parameters to enhance the transfer capability of the 400 kV line in the North-West region of the Ethiopian electric network by the damping of low-frequency oscillation. Double-input Power system stabilizers (PSSs) are currently used in power systems to damp out low-frequency oscillations. The gained minimum damping ratio and eigenvalue results of the proposed Ant lion algorithm (ALO) approach are compared with the existing conventional system to get better efficiency at various loading conditions. Additionally, the proposed Ant lion optimization approach requires minimal time to estimate the key parameters of the power oscillation damper (POD). Consequently, the average time taken to optimally size the parameters of the PSS controller was 14.6 s, which is pretty small and indicates real-time implementation of an ALO developed model. The nonlinear equations that represent the system have been linearized and then placed in state-space form in order to study and analyze the dynamic performance of the system by damping out low-frequency oscillation problems. Finally, conventional fixed-gain PSS improves the maximum overshoot by 5.2% and settling time by 51.4%, but the proposed optimally sized PSS employed with the ALO method had improved the maximum overshoot by 16.86% and settling time by 78.7%.

Software defect prediction via optimal trained convolutional neural network

By creating an effective prediction model, SDP helps to find the possible problems in recent components of software earlier. The model's effectiveness was harmed by characteristics that were irrelevant or redundant. This article will present a new SDP (Software Defect Prediction) framework with several stages. Firstly, the input data proceeded through a preprocessing stage. Statistical features (variance, mean), raw features, higher-order statistical features, as well as suggested correlations are obtained from the preprocessed data. Weighted average & continuous probability distribution are among the statistical features of mean, whereas discrete random variables are among the statistical features of variance. Furthermore, to choose the necessary characteristics, improved PCA (Principle Component Analysis) is employed. Next, the chosen features are used to predict defects using an improved CNN (Convolutional Neural Network). The CNN weights are tuned optimally by a proposed hybrid SALO (Seagull Adopted Ant Lion Optimization) model for making the detection highly exact and precise. To analyze the performance of this work, certain performance measures have been used.

Intrusion detection framework for securing privacy attack in cloud computing environment using DCCGAN‐RFOA

AbstractCloud computing (CC) is vulnerable for the attacks of current information technology, because it prolongs and uses the information technology infrastructure, traditional operating system, and applications. Several security issues in detecting irregular network performances are confronted by the CC environment. With the intention of solving these security issues, dual‐channel capsule generation adversarial network optimized with red fox optimization algorithm fostered intrusion detection framework (IDS‐CC‐DCCGAN‐RFOA) is proposed in this article, for securing the privacy attacks in CC environment. Initially, the data are taken from NSL‐KDD benchmark dataset. Then the data are given to the preprocessing segment. When using the developed random forest and local least squares in preprocessing segment, the redundancy, and missing values are purged. Then the output of preprocessing is given to the feature selection stage. In feature selection, the optimal features are extracted using univariate ensemble feature selection (UEFS) technique. Based on the optimal features, the data are classified into secured data and privacy attack data based on dual‐channel capsule generative adversarial network. Here, the weight of dual‐channel capsule generative adversarial network is optimally tuned using the red fox optimization algorithm for generating an effective and optimal solution to detect the intruders. The proposed IDS‐CC‐DCCGAN‐RFOA method is implemented in MATLAB. Here, the performance metrics, like accuracy, specificity, sensitivity, F‐score, precision, false positive rate, AUC are analyzed. Then, the proposed IDS‐CC‐CCGAN‐WSOA method provides 13.9367%, 11.12%, 13.268%, and 13.739% higher accuracy and 24.54%, 29.76%, 12.87%, and 34.21% higher sensitivity compared with the existing methods, like intrusion detection framework in cloud computing using deep belief network with salp swarm algorithm (IDS‐CC‐DBN‐CSSA), intrusion detection framework in cloud computing using deep neural network with improved genetic algorithm and simulated annealing algorithm (IDS‐CC‐DNN‐IGASAA), intrusion detection for cloud computing using multilayer perceptron neural network and artificial bee colony optimization algorithm (IDS‐CC‐MLPNN‐ABC), and intrusion detection for cloud computing using recurrent convolutional neural network with ant lion optimization algorithm (IDS‐CC‐RCNN‐ALO).

Optimum design of combined footings using swarm intelligence-based algorithms

This study considers the optimum design of reinforced concrete combined footings. Analysis and design procedures are developed based on the American Concrete Institute (ACI 318-05) regulations and several geotechnical considerations for providing stability to the structure. Low-cost designs are generated using five swarm intelligence algorithms [i.e., particle swarm optimization (PSO), accelerated particle swarm optimization (APSO), whale optimization algorithm (WOA), ant lion optimizer (ALO), and moth flame optimization (MFO)]. The performance of these algorithms is examined through two numerical simulations. First, a combined footing design from the literature is optimized using the five swarm intelligence algorithms. The impacts of concrete compressive strength (fc) on the final cost are studied through a sensitivity analysis. Second, rectangular and trapezoidal combined footings are optimized using the best algorithm in the first example (i.e., PSO) to explore the effect of shape on the final design. Also, a sensitivity analysis is performed on the effects of the design parameters on the cost.

Impact of Grid Integrated Energy Storage Systems with Phasor Measuring Units for Secured Data Control Using Metaheuristic

In this article, malleable determinations have been made by integrating solar panels in the field of Power System State Estimation (PSSE) where the optimal placement using Phasor Measuring Units (PMUs) plays a major role in the integration process. This kind of integration provides a path for determining the importance of PMU and converting the grid to be smart. In preceding, methods of PSSE researchers were not able to demonstrate the smart grid process due to failure of integrating solar cells. Thus, the integrated solar cells in the proposed method can be able to detect the presence of false data and secure the identified data using a Data Management Systems (DMS). In addition, the DMS estimates a particular state during a certain period of time thus creating an external awareness about communicating devices. Moreover, the process of PSSE with solar cells and PMU placement mechanism functions effectively using a metaheuristic Ant Lion Optimization (ALO) where two fundamental scenarios are considered and simulated using MATLAB. The simulation results indicate that the proposed method provides satisfactory simulation results for about 67% when compared to existing method.

Passenger congestion alleviation in large hub airport ground-access system based on queueing theory

Airport public transport systems are plagued by passenger queue congestion, imposing a substandard travel experience and unexpected delays. To address this issue, this paper proposes a bi-level programming for optimizing queueing network in airport access. Firstly, we derive queueing network for airport public transport system, which include the taxi, bus, and subway. Then, we propose a bi-level programming model for optimizing queueing network. The lower level subprogram is designed to solve the passenger transport mode choice, while the upper level subprogram is designed to minimize the number of passengers waiting to be served with queue tolls. Finally, we develop the successive weighted averages (MSWA) method to solve the lower subprogram's passenger share rates and the ant lion optimization (ALO) method to solve the bi-level program's queue toll scheme. The simulation results highlight that our strategy can alleviate queue congestion for daytime and evening scenarios and effectively improve evacuation efficiency.

New Optimized Deep Learning Application for COVID-19 Detection in Chest X-ray Images

Due to false negative results of the real-time Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR) test, the complemental practices such as computed tomography (CT) and X-ray in combination with RT-PCR are discussed to achieve a more accurate diagnosis of COVID-19 in clinical practice. Since radiology includes visual understanding as well as decision making under limited conditions such as uncertainty, urgency, patient burden, and hospital facilities, mistakes are inevitable. Therefore, there is an immediate requirement to carry out further investigation and develop new accurate detection and identification methods to provide automatically quantitative evaluation of COVID-19. In this paper, we propose a new computer-aided diagnosis application for COVID-19 detection using deep learning techniques. A new technique, which receives symmetric X-ray data as the input, is presented in this study by combining Convolutional Neural Networks (CNN) with Ant Lion Optimization Algorithm (ALO) and Multiclass Naïve Bayes Classifier (NB). Moreover, several other classifiers such as Softmax, Support Vector Machines (SVM), K-Nearest Neighbors (KNN) and Decision Tree (DT) are combined with CNN. The promising results of these classifiers are evaluated and presented for accuracy, precision, and F1-score metrics. NB classifier with Ant Lion Optimization Algorithm and CNN produced the best results with 98.31% accuracy, 100% precision and 98.25% F1-score and with the lowest execution time.

An efficient ANFIS-EEBAT approach to estimate effort of Scrum projects

Software effort estimation is a significant part of software development and project management. The accuracy of effort estimation and scheduling results determines whether a project succeeds or fails. Many studies have focused on improving the accuracy of predicted results, yet accurate estimation of effort has proven to be a challenging task for researchers and practitioners, particularly when it comes to projects that use agile approaches. This work investigates the application of the adaptive neuro-fuzzy inference system (ANFIS) along with the novel Energy-Efficient BAT (EEBAT) technique for effort prediction in the Scrum environment. The proposed ANFIS-EEBAT approach is evaluated using real agile datasets. It provides the best results in all the evaluation criteria used. The proposed approach is also statistically validated using nonparametric tests, and it is found that ANFIS-EEBAT worked best as compared to various state-of-the-art meta-heuristic and machine learning (ML) algorithms such as fireworks, ant lion optimizer (ALO), bat, particle swarm optimization (PSO), and genetic algorithm (GA).

Multiobjective Optimization of a Hybrid PV/Wind/Battery/Diesel Generator System Integrated in Microgrid: A Case Study in Djelfa, Algeria

Hybrid Renewable Energy Sources (HRES) integrated into a microgrid (MG) are a cost-effective and convenient solution to supply energy to off-grid and rural areas in developing countries. This research paper focuses on the optimization of an HRES connected to a stand-alone microgrid system consisting of photovoltaics (PV), wind turbines (WT), batteries (BT), diesel generators (DG), and inverters to meet the energy demand of fifteen residential housing units in the city of Djelfa, Algeria. In this context, the multiobjective salp swarm algorithm (MOSSA), which is among the latest nature-inspired metaheuristic algorithms recently introduced for hybrid microgrid system (HMS) optimization, has been proposed in this paper for solving the optimization of an isolated HRES. The proposed multiobjective optimization problem takes into account the cost of energy (COE) and loss of power supply probability (LPSP) as objective functions. The proposed approach is applied to determine three design variables, which are the nominal power of photovoltaic, the number of wind turbines, and the number of battery autonomy days considering higher reliability and minimum COE. In order to perform the optimum size of HMG, MOSSA is combined with a rule-based energy management strategy (EMS). The role of EMS is the coordination of the energy flow between different system components. The effectiveness of using MOSSA in addressing the optimization issue is investigated by comparing its performance with that of the multiobjective dragonfly algorithm (MODA), multiobjective grasshopper optimization algorithm (MOGOA), and multiobjective ant lion optimizer (MOALO). The MATLAB environment is used to simulate HMS. Simulation results confirm that MOSSA achieves the optimum system size as it contributed 0.255 USD/kW h of COE and LPSP of 27.079% compared to MODA, MOGOA, and MOALO. In addition, the optimization results obtained using the proposed method provided a set of design solutions for the HMS, which will help designers select the optimal solution for the HMS.

Critical review of bio‐inspired data optimization techniques: An image steganalysis perspective

AbstractImage steganalysis involves the discovery of secret information embedded in an image. The common method is blind image steganalysis, which is a two‐class classification problem. Blind steganalysis extracts all possible feature variations in an image due to embedding, select the most appropriate feature data, and then classifies the image. The dimensionality of the extracted image features are high and demand data reduction to identify the most relevant features and to aid accurate classification of an image. The classification is under two classes namely, clean (cover) image and stego (image with embedded secret data) image. Since the classification accuracy depends on selection of most appropriate features, opting for the best data reduction or data optimization algorithms becomes a prime requisite. Research shows that most of the statistical optimization techniques converge to local minima and lead to less classification accuracy as compared to bio‐inspired methods. Bio‐inspired optimization methods obtain improved classification accuracy by reducing the high‐dimensional image features. These methods start with an initial population and then optimize them in steps till a global optimal point is reached. Examples of such methods include Ant Lion Optimization (ALO), Fire Fly Algorithm (FFA), and literature shows around 54 such algorithms. Bio‐inspired optimization has been applied in various fields of design optimization and is novel to image steganalysis. This article analyses the various bio‐inspired optimization techniques and their accuracy in image steganalysis pertaining to the discovery of embedded information in both JPEG and spatial domain steganalysis.This article is categorized under: Technologies > Classification Technologies > Computational Intelligence Technologies > Artificial Intelligence

An adaptive Grey-Markov model based on parameters Self-optimization with application to passenger flow volume prediction

It has been demonstrated that local prediction approaches show better prediction performance compared with global ones. The paper proposes a novel local prediction model named as the adaptive grey-Markov prediction model for incomplete and complex dynamic systems. This model displays favorable adaptability, flexibility and universality because of four advantages. Firstly, because of the randomness and non-repeatability of time series, the paper proposes adaptive exponential accumulated generating operators to describe different contribution degrees of historical information to future change trends of system characteristics. Secondly, this paper proposes a new paradigm of adaptive grey background value, which can automatically adjust parameters based on historical information to optimize this model and reduce prediction errors. Thirdly, the optimization model based on average absolute percentage error is constructed, and the corresponding adaptive parameters are searched by Ant Lion Optimizer algorithm. Fourthly, this model consists of a quadratic polynomial function describing nonlinearity and a periodic function generated by a Fourier series characterizing noise and period. Thereupon, the adaptive odd-period grey model is proposed. Its residual modified model, named as adaptive grey-Markov modified model, is given based on Markov chain (MC) and virtual linguistic trust degree (VLTD) to further improve prediction accuracy. The modified values of absolute errors are determined by the score functions produced from trust degrees of absolute errors to states on MC calculated by VLTD method. This modified way retains the influence degrees of absolute errors on the modified values and improves the prediction accuracy. Finally, the paper further demonstrates performance and practicability of the proposed prediction model through predicting passenger flow of Chengdu Metro Line1 and comparative analysis.

Rational Application of Electric Power Production Optimization through Metaheuristics Algorithm

The aim of this manuscript is to introduce solutions to optimize economic dispatch of loads and combined emissions (CEED) in thermal generators. We use metaheuristics, such as particle swarm optimization (PSO), ant lion optimization (ALO), dragonfly algorithm (DA), and differential evolution (DE), which are normally used for comparative simulations, and evaluation of CEED optimization, generated in MATLAB. For this study, we used a hybrid model composed of six (06) thermal units and thirteen (13) photovoltaic solar plants (PSP), considering emissions of contaminants into the air and the reduction in the total cost of combustibles. The implementation of a new method that identifies and turns off the least efficient thermal generators allows metaheuristic techniques to determine the value of the optimal power of the other generators, thereby reducing the level of pollutants in the atmosphere. The results are presented in comparative charts of the methods, where the power, emissions, and costs of the thermal plants are analyzed. Finally, the comparative results of the methods were analyzed to characterize the efficiency of the proposed algorithm.

Exploiting lion optimization algorithm for sustainable energy management system in industrial applications

Energy is the soul of each new invention on this Bio-sphere. The upturn in this advancement and technological growth, energy resources are getting scarce. As the energy resources are limited and can not be increased in the same proportion as with the exponential rising demand, that is why, we have to manage our energy consumption smartly. An optimal integration of the Renewable energy sources (RESs) for this purpose is the need of the day. This paper proposes a bio-inspired algorithm, namely, the Lion’s Algorithm (LA), for an efficient Energy Management System (EMS) in industrial areas, along with a beneficial utilization of RES and energy storing units (ESUs). Different objectives, like, Total Energy Cost (TEC), Peak to Average power Ratio (PAR), Hourly Load (HL) and maximization of end-user comfort (the reduction in waiting time) are analyzed and observed. LA algorithm is specially designed to achieve these objectives up-to maximum optimal limits. The MATLAB simulation results illustrate that, our proposed algorithm reduced the cost up to 42.66% and PAR 35.94% compared to un-scheduled and scheduled with other state-of-the-art algorithms, with an average waiting time of only 0.216 h (12.96 s).

A Mahalanobis Surrogate-Assisted Ant Lion Optimization and Its Application in 3D Coverage of Wireless Sensor Networks.

Metaheuristic algorithms are widely employed in modern engineering applications because they do not need to have the ability to study the objective function’s features. However, these algorithms may spend minutes to hours or even days to acquire one solution. This paper presents a novel efficient Mahalanobis sampling surrogate model assisting Ant Lion optimization algorithm to address this problem. For expensive calculation problems, the optimization effect goes even further by using MSAALO. This model includes three surrogate models: the global model, Mahalanobis sampling surrogate model, and local surrogate model. Mahalanobis distance can also exclude the interference correlations of variables. In the Mahalanobis distance sampling model, the distance between each ant and the others could be calculated. Additionally, the algorithm sorts the average length of all ants. Then, the algorithm selects some samples to train the model from these Mahalanobis distance samples. Seven benchmark functions with various characteristics are chosen to testify to the effectiveness of this algorithm. The validation results of seven benchmark functions demonstrate that the algorithm is more competitive than other algorithms. The simulation results based on different radii and nodes show that MSAALO improves the average coverage by 2.122% and 1.718%, respectively.

Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Alternating Current Networks

In this paper, we solve the optimal power flow problem in alternating current networks to reduce power losses. For that purpose, we propose a master–slave methodology that combines the multiverse optimization algorithm (master stage) and the power flow method for alternating current networks based on successive approximation (slave stage). The master stage determines the level of active power to be injected by each distributed generator in the network, and the slave stage evaluates the impact of the proposed solution on each distributed generator in terms of the objective function and the constraints. For the simulations, we used the 10-, 33-, and 69-node radial test systems and the 10-node mesh test system with three levels of distributed generation penetration: 20%, 40%, and 60% of the power provided by the slack generator in a scenario without DGs. In order to validate the robustness and convergence of the proposed optimization algorithm, we compared it with four other optimization methods that have been reported in the specialized literature to solve the problem addressed here: Particle Swarm Optimization, the Continuous Genetic Algorithm, the Black Hole Optimization algorithm, and the Ant Lion Optimization algorithm. The results obtained demonstrate that the proposed master–slave methodology can find the best solution (in terms of power loss reduction, repeatability, and technical conditions) for networks of any size while offering excellent performance in terms of computation time.

Suppression of Ambipolar Current in Enhanced Gate Based Schottky Barrier CNTFET Using Ant Lion Optimization

Suppression of Ambipolar Current in Enhanced Gate Based Schottky Barrier CNTFET Using Ant Lion Optimization

Hunger games search algorithm for global optimization of engineering design problems

Abstract The modernization in automobile industries has been booming in recent times, which has led to the development of lightweight and fuel-efficient design of different automobile components. Furthermore, metaheuristic algorithms play a significant role in obtaining superior optimized designs for different vehicle components. Hence, a hunger game search (HGS) algorithm is applied to optimize the automobile suspension arm (SA) by reduction of mass vis-à-vis volume. The performance of the HGS algorithm was accomplished by comparing the achieved results with the well-established metaheuristics (MHs), such as salp swarm optimizer, equilibrium optimizer, Harris Hawks optimizer (HHO), chaotic HHO, slime mould optimizer, marine predator optimizer, artificial bee colony optimizer, ant lion optimizer, and it was found that the HGS algorithm is able to pursue the best optimized solution subjecting to critical constraints. Moreover, the HGS algorithm can realize the least weight of the SA subjected to maximum stress values. Hence, the adopted algorithm can be found robust in terms of obtaining the best global optimum solution.

Economical-Environmental-Technical Operation of Power Networks with High Penetration of Renewable Energy Systems Using Multi-Objective Coronavirus Herd Immunity Algorithm

This paper proposes an economical-environmental-technical dispatch (EETD) model for adjusted IEEE 30-bus and IEEE 57-bus systems, including thermal and high penetration of renewable energy sources (RESs). Total fuel costs, emissions level, power losses, voltage deviation, and voltage stability are the five objectives addressed in this work. A large set of equality and inequality constraints are included in the problem formulation. Metaheuristic optimization approaches—Coronavirus herd immunity optimizer (CHIO), salp swarm algorithm (SSA), and ant lion optimizer (ALO)—are used to identify the optimal cost of generation, emissions, voltage deviation, losses, and voltage stability solutions. Several scenarios are reviewed to validate the problem-solving competency of the defined optimisation model. Numerous scenarios are studied to verify the proficiency of the optimisation model in problem-solving. The multi-objective problem is converted into a normalized one-objective issue through a weighted sum-approach utilizing the analytical hierarchy process (AHP). Additionally, the technique for order preference by similarity to ideal solution (TOPSIS) is presented for identifying the optimal value of Pareto alternatives. Ultimately, the results achieved reveal that the proposed CHIO performs the other approaches in the EETD problem-solving.