Ant Lion Optimization Algorithm Research Articles

Optimal power flow solution with current injection model of generalized interline power flow controller using ameliorated ant lion optimization

<span lang="EN-US">Optimal power flow (OPF) solutions with generalized interline power flow controller (GIPFC) devices play an imperative role in enhancing the power system’s performance. This paper used a novel ant lion optimization (ALO) algorithm which is amalgamated with Lévy flight operator, and an effectual algorithm is proposed named as, ameliorated ant lion optimization (AALO) algorithm. It is being implemented to solve single objective OPF problem with the latest flexible alternating current transmission system (FACTS) controller named as GIPFC. GIPFC can control a couple of transmission lines concurrently and it also helps to control the sending end voltage. In this paper, current injection modeling of GIPFC is being incorporated in conventional Newton-Raphson (NR) load flow to improve voltage of the buses and focuses on minimizing the considered objectives such as generation fuel cost, emissions, and total power losses by fulfilling equality, in-equality. For optimal allocation of GIPFC, a novel Lehmann-Symanzik-Zimmermann (LSZ) approach is considered. The proposed algorithm is validated on single benchmark test functions such as Sphere, Rastrigin function then the proposed algorithm with GIPFC has been testified on standard IEEE-30 bus system.</span>

Impact of Electric Vehicle on Residential Power Distribution Considering Energy Management Strategy and Stochastic Monte Carlo Algorithm

The area of a Microgrid (μG) is a very fast-growing and promising system for overcoming power barriers. This paper examines the impacts of a microgrid system considering Electric Vehicle Grid Integration (EVGI) based on stochastic metaheuristic methods. One of the biggest challenges to slowing down global climate change is the transition to sustainable mobility. Renewable Energy Sources (RESs) integrated with Evs are considered a solution for the power and environmental issues needed to achieve Sustainable Development Goal Seven (SDG7) and Climate Action Goal 13 (CAG13). The aforementioned goals can be achieved by coupling Evs with the utility grid and other RESs using Vehicle-to-Grid (V2G) technology to form a hybrid system. Overloading is a challenge due to the unknown number of loads (unknown number of Evs). Thus, this study helps to establish the system impact of the uncertainties (arrival and departure Evs) by proposing Stochastic Monte Carlo Method (SMCM) to be addressed. The main objective of this research is to size the system configurations using a metaheuristic algorithm and analyze the impact of an uncertain number of Evs on the distribution of residential power in Tripoli-Libya to gain a cost-effective, reliable, and renewable system. The Improved Antlion Optimization (IALO) algorithm is an optimization technique used for determining the optimal number of configurations of the hybrid system considering multiple sources, while the Rule-Based Energy Management Strategy (RB-EMS) controlling algorithm is used to control the flow of power in the electric power system. The sensitivity analysis of the effect parameters has been taken into account to assess the expected impact in the future. The results obtained from the sizing, controlling, and sensitivity analyses are discussed.

Advanced optimizer for maximum power point tracking of photovoltaic systems in smart grid: A roadmap towards clean energy technologies

With the increased load demand and generation cost of electricity, renewable energy sources (RES) provide cost effective and environment-friendly solution. Integration of solar energy includes the challenges of finding the Maximum Power Point (MPP) to achieve the maximum power from the solar. Combining RESs with distributed generation (DG) comes with the challenges of voltage regulation and frequency mismatch. In this work, a novel approach of Robust Antlion Optimizer (ALO) algorithm is implemented for MPP tracking in solar photovoltaic (PV) system and charge controllers are designed for storage system while the DC to AC converters have been designed to match RESs’ frequency with that of DG. Power scheduling is done to decrease operational cost while keeping the voltage and frequency in the allowable range. Research findings provide useful policy implications for all stakeholders to meet energy needs in a consistent and coherent manner.

Fractional-Ant Lion Optimization Algorithm for Privacy Protection in Cloud with MapReduce Framework

Cloud computing environment offers huge computation power and enormous storage capacity. The cloud framework enables users to organize the data-intensive applications without the investment of substructure and it acts as a key role in the digital era, as it simultaneously handles the data from the number of users. Besides the useful characteristic features, providing security to data that is stored in cloud platform offers a great challenge in cloud storage. Using the proposed F-ALO technique, a privacy protection model is developed to encrypt the data before sharing it in the cloud. To achieve effective data protection, the proposed F-ALO algorithm was developed by combining the Fractional Calculus (FC) and Ant Lion Optimizer (ALO) algorithms. By incorporating the privacy protection and data sharing phases, employing the MapReduce framework’s mappers and reducers, the shared data is safeguarded. Using the proposed algorithm based on the factors, such as markedness and sensitivity index, the optimal coefficient is calculated. The proposed F-ALO algorithm achieved higher performance with measures, like sensitivity index, markedness, accuracy, Database Difference Ratio (DBDR), computational overhead, time complexity and space complexity with the values of 0.8422, 0.9366, 0.9339, 0.8192, 0.7357, 0.4678, and 0.3679 using the Hungarian database.

A novel MOALO-MODA ensemble approach for multi-objective optimization of machining parameters for metal matrix composites

Machining of metal matrix composites (MMCs) has now become an essential task in shaping them to their final usable forms for various industrial applications. These machining operations may range from drilling of simple through holes to cutting and shaping of the composites into complex shapes. Determination of the optimal process parameters during their machining presents a combinatorial optimization problem, which may turn out to be more complex due to involvement of various material dependent parameters of the MMCs, like particle size, reinforcement percentage etc. The importance of optimization increases manifold due to conflicting settings of different process parameters for attaining the desired quality characteristics. In this paper, two nature-inspired optimization algorithms, i.e. multi-objective antlion optimization (MOALO) and multi-objective dragonfly algorithm (MODA) are applied for optimization of various process parameters during machining of MMCs. To mitigate uncertainties in global optimality of the predicted Pareto optimal fronts arising due to stochastic nature of the metaheuristics, an ensemble approach integrating MOALO and MODA techniques is proposed here. It is demonstrated with the help of two case studies that MOALO-MODA ensemble is far superior to its individual counterparts and thus, can be employed for development of robust and reliable Pareto optimal fronts.

New hybrid support vector regression methods for predicting fresh and hardened properties of self-compacting concrete

Most of the published literature on concrete containing fly ash was limited to predicting the hardened properties of concrete. It is understood that exist so restricted studies focusing on forecasting both hardened and fresh features of self-compacting concrete (SCC). Hence, it is goaled for developing models for predicting the fresh and hardened properties of SCC by the support vector regression method (SVR). This study aims to specify SVR method key parameters using Ant lion optimization (ALO) and Biogeography-based optimization (BBO) algorithms. The considered properties of SCC in the fresh phase are the L-box test, V-funnel test, slump flow, and in the hardened phase is CS. Results demonstrate powerful potential in the learning section for all considered properties as well as approximating in the testing phase. It can be seen that the proposed models have R2 incredible value in the learning and testing phase. It means that the correlation between observed and predicted properties of SCC from hybrid models is acceptable so that it represents high accuracy in the training and approximating process. All in all, in most of the cases, the SVR model developed by ALO outperforms BBO-SVR, which depicts the capability of the ALO algorithm for determining the optimal parameters of the considered method.

Taylor ant lion optimization‐based generative adversarial networks for forecasting electricity consumption

Taylor ant lion optimization‐based generative adversarial networks for forecasting electricity consumption

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.

Optimizing cloud resource scheduling for enhanced energy efficiency

Energy consumption in cloud computing plays a vital role in operating costs for both the service provider and the cloud user. The cloud is scalable and can provide access as per demand. Due to this, resource access requests are increasing and submitted to the server. To manage all requests, scheduling is the solution to assign requests with the quality of service. To avoid high operating costs, resource scheduling needs to be energy-aware. In this paper, energy-aware resource scheduling in the cloud is proposed. The total resource utilization of each resource has been calculated, and energy is optimized through the antlion optimization algorithm to avoid high power consumption. The resource is identified with its best utilization value and assigned to submitted workloads on a priority basis. The experimental results of the proposed work are analyzed with existing autonomic frameworks, and it is found that the proposed work performs exceptionally well. In addition to addressing the increasing resource access requests, the proposed energy-aware resource scheduling in cloud computing also focuses on optimizing overall system performance. The antlion optimization algorithm is employed to efficiently manage energy consumption, ensuring that power usage is minimized without compromising the quality of service. The algorithm calculates the total resource utilization for each component in the cloud infrastructure, allowing for a thorough understanding of system dynamics. The paper optimizes cloud computing by prioritizing efficient and capacity-based resource assignment, improving responsiveness, user experience, and reducing costs. The approach also promotes environmental sustainability. Experimental results validate its effectiveness in energy efficiency and system responsiveness, contributing to advanced resource management in cloud computing for sustainable and cost-effective services.

EECHS-ARO: Energy-efficient cluster head selection mechanism for livestock industry using artificial rabbits optimization and wireless sensor networks

<abstract> <p>In the livestock industry, wireless sensor networks (WSNs) play a significant role in monitoring many fauna health statuses and behaviors. Energy preservation in WSNs is considered one of the critical, complicated tasks since the sensors are coupled to constrained resources. Therefore, the clustering approach has proved its efficacy in preserving energy in WSNs. In recent studies, various clustering approaches have been introduced that use optimization techniques to improve the network lifespan by decreasing energy depletion. Yet, they take longer to converge and choose the optimal cluster heads in the network. In addition, the energy is exhausted quickly in the network. This paper introduces a novel optimization technique, i.e., an artificial rabbits optimization algorithm-based energy efficient cluster formation (EECHS-ARO) approach in a WSN, to extend the network lifetime by minimizing the energy consumption rate. The EECHS-ARO technique balances the search process in terms of enriched exploration and exploitation while selecting the optimal cluster heads. The experimentation was carried out on a MATLAB 2021a platform with varying sensor nodes. The obtained results of EECHS-ARO are contrasted with other existing approaches via teaching–learning based optimization algorithm (TLBO), ant lion optimizer (ALO) and quasi oppositional butterfly optimization algorithm (QOBOA). The proposed EECHS-ARO enriches the network lifespan by ~15% and improves the packet delivery ratio by ~5%.</p> </abstract>

Frequency regulation analysis for renewable bio generated autonomous multi-microgrid using moth flame optimized fractional order controller

Microgrid is considered as an active cell for grid control and management in the modern power system environment. The load frequency control of multi-micro-grid consisting of two micro-grid connected through a tie-line is discussed in these paper. In order to achieve this, a mathematical model is proposed based on renewable energy resources with multi-load perturbations. The proposed microgrid-1 consists of Hydro, variable load, Solar Photo Voltaic (SPV), Bio Gas Turbine Generator (BGTG), Battery Energy Storage System (BESS) and while, the proposed microgrid-2 consist of Battery Energy Storage System (BESS), Solar Photo Voltaic (SPV), Hydro, Biodiesel Engine Generator (BDEG) and variable Load. During a disturbance in the system frequency the instant load sources is tackle by BESS, BDEG and BGTB. Fractional-Proportional-Integral-Derivative (FPID), Proportional-Integral-Derivative (PID) and Proportional-Integral (PI) where use to controls the load frequency of the proposed multi-micro-grid systems. The variable values (gain) of the controllers where considered as a control variable and integral square error (ISE) considered as being a fitness function of the optimization algorithm. The performance in relations of the dynamic response of the controllers are deliberate using different optimization techniques namely, Firefly algorithm (FA), Ant Lion Optimization (ALO) and Moth Flame Optimization (MFO) algorithm.

Improved Supervised and Unsupervised Metaheuristic-Based Approaches to Detect Intrusion in Various Datasets

Due to the increasing number of cyber-attacks, the necessity to develop efficient intrusion detection systems (IDS) is more imperative than ever. In IDS research, the most effectively used methodology is based on supervised Neural Networks (NN) and unsupervised clustering, but there are few works dedicated to their hybridization with metaheuristic algorithms. As intrusion detection data usually contains several features, it is essential to select the best ones appropriately. Linear Discriminant Analysis (LDA) and t-statistic are considered as efficient conventional techniques to select the best features, but they have been little exploited in IDS design. Thus, the research proposed in this paper can be summarized as follows. a) The proposed approach aims to use hybridized unsupervised and hybridized supervised detection processes of all the attack categories in the CICIDS2017 Dataset. Nevertheless, owing to the large size of the CICIDS2017 Dataset, only 25% of the data was used. b) As a feature selection method, the LDA performance measure is chosen and combined with the t-statistic. c) For intrusion detection, unsupervised Fuzzy C-means (FCM) clustering and supervised Back-propagation NN are adopted. d) In addition and in order to enhance the suggested classifiers, FCM and NN are hybridized with the seven most known metaheuristic algorithms, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Cultural Algorithm (CA), Harmony Search (HS), Ant-Lion Optimizer (ALO) and Black Hole (BH) Algorithm. Performance metrics extracted from confusion matrices, such as accuracy, precision, sensitivity and F₁-score are exploited. The experimental result for the proposed intrusion detection, based on training and test CICIDS2017 datasets, indicated that PSO, GA and ALO-based NNs can achieve promising results. PSO-NN produces a tested accuracy, global sensitivity and F₁-score of 99.97%, 99.95% and 99.96%, respectively, outperforming performance concluded in several related works. Furthermore, the best-proposed approaches are valued in the most recent intrusion detection datasets: CSE-CICIDS2018 and LUFlow2020. The evaluation fallouts consolidate the previous results and confirm their correctness.

Optimal Allocation of UPFC for Congestion Management using ANTLION Algorithm

To obtain optimum the power market the deregulation process is implemented for the entire electrical market and further classified as transmission and distribution units. Each firm focuses on delivering their best output which lead to congestion issues on transmission lines as there is in-adequate transmission capability to satisfy the load demand, transmission congestion occurs. Transmission cable congestion results from a lack of coordination between the utilities that handle generating, transmission, and distribution lead to generator failure, an abrupt increase in load demand, or the miscarriage of equipment, all of which can be fixed by FACTS devices. In this work, transmission line congestion is reduced using FACTS devices like UPFC and IPFC. The primary goal is to determine where FACTS devices should be placed. The main goal is to use the ANTLION optimization algorithm to locate the optimal position and size for FACTS devices. In an IEEE-14 bus system, UPFC and IPFC are individually connected and parameters are analyzed and formulated for better transmission among energy sectors.

Diameter Prediction of Silicon Ingots in the Czochralski Process Based on a Hybrid Deep Learning Model

The diameter prediction of silicon ingots in the Czochralski process is a complex problem because the process is highly nonlinear, time-varying, and time-delay. To address this problem, this paper presents a novel hybrid deep learning model, which combines the deep belief network (DBN), support vector regression (SVR), and the ant lion optimizer (ALO). Continuous restricted Boltzmann machines (CRBMs) are used in DBN for working with continuous industrial data. The feature aggregates the outputs from various DBNs through an SVR model. Additionally, the ALO algorithm is used for the parameter’s optimization of SVR. The newly developed model is verified with the actual production data and compared with the back propagation neural network (BPNN) and the SVR model. The simulation results demonstrate the availability and accuracy of the CRBM-DBN-ALO-SVR hybrid deep learning model.

Sizing Optimization Algorithm for Vehicle-to-Grid System Considering Cost and Reliability Based on Rule-Based Scheme

The most widely two Renewable Energy Sources (RESs) used are solar and wind as naturally found sources due to the provided merits as clean, free of charge, and environmentally friendly. However, they are facing limitations in intermittency. This article aims to utilize natural resources integrated with the utility grid and Electric Vehicle (EV) to provide a hybrid system with a minimum of two objectives namely Cost of Electricity (COE) and reliability using the Losses Power Supply Probability (LPSP) method. The two mentioned objectives are considered to satisfy the residential load demand with EV in terms of Vehicle-to-Grid (V2G) as this article considered. The mentioned objective has been addressed by the Improved Antlion Optimization Algorithm (IALO) and coupled with a high supervisory control method called Rule-Based Energy Management Strategy (RB-EMS) to guarantee to spread the power among the system component. Optimization results show the hybrid integration of the utilized RESs with the Battery (BT) gives the most economic and reliable system for the study area integrated with the EV battery. The gained result was validated with a natural-inspired metaheuristic algorithm called Particle Swarm Optimization (PSO). This article assesses the effect on the RESs generators to achieve an economic and reliable system.

Novel Mathematical Design of Triple-Tuned Filters for Harmonics Distortion Mitigation

The design of AC filters must meet the criteria of harmonic distortion mitigation and reactive power support in various operating modes. The stringent reactive power-sharing requirements currently lead to sophisticated filter schemes with high component ratings. In this regard, triple-tuned filters (TTFs) have good potential in harmonic mitigation of a broad range of harmonics. In the literature, the TTF design has been presented using a parametric method, assuming that the TTF is equivalent to a three-arm single-tuned filter (TASTF). However, no direct methods of designing it or finding its optimal parameters have been provided. This paper presents novel mathematical designs of TTFs. Three different design methods are considered—the direct triple-tuned filter (DTTF) design method, as a TASTF, and a method based on the equivalence between the two design methods called the equivalence hypothesis method to design the triple-tuned filter (EHF). The parameters of the three proposed design methods are optimized based on the minimization of a proposed multi-objective function using a recent metaheuristic algorithm called artificial rabbits optimization (ARO) to mitigate harmonics, improve power quality, and minimize power losses in an exemplary system presented in IEEE STD-519. Further, the system’s performance has been compared to the system optimized by the ant lion optimizer (ALO) and whale optimization algorithm (WOA) to validate the effectiveness of the proposed design. Simulation results emphasized harmonics mitigation in the system, the system losses reduction, and power quality improvement with lower reactive power filter ratings than conventional single and double-tuned filters.

Performance of Ant Lion Optimization and Artificial Bee Colony Algorithm for Structural Health Monitoring of ASCE Benchmark Structure

Structural health Monitoring (SHM) has been a fast-moving tread for monitoring the health status of civil engineering structures. The SHM strategy involves application of different techniques involving the use of modal parameters, such as natural frequencies and mode shapes, to detect and localize the damage. Though localization of damage in structure plays an important role, damage quantification, which helps in performing repair works, is also essential. However, very few algorithms have been developed which calculates the amount of damage in structure. In view to this situation, the swarm-based optimization algorithms have been developed which can detect the damage severity in a structure. Some of the algorithms developed so far are Grey Wolf Algorithm (GWO), Artificial Bee Colony (ABC) algorithm, Firefly algorithm, Ant Lion Optimization (ALO) algorithm and others. Out of these algorithms, ALO and ABC have been used in limited number of cases for performing SHM. No real-life structures, considering the effect of noise, has been analysed using ALO and ABC. In this paper, the ALO and ABC optimization algorithm has been studied for damage analysis using an objective function based on the eigen value problem. The structure chosen for analysis is the ASCE Benchmark building which is a quarter scale model of an original building which give the real-life sense of SHM. Damage considered in the structure is of less severity occurring at multiple locations under an external noise, which is also a real-life challenge for damage analysis. The experimental results show that both ABC and ALO algorithms are able to quantify both major and minor damages in the presence of moderate amount of noise, proving the robustness of these algorithms.

Antlion-based sliding mode control of vienna rectifier for internet of electric vehicle

Electric car charging infrastructure has recently grown to be a crucial component of the electrified powertrain. A single-phase Vienna rectifier is the most commonly used rectifier in electric vehicles because of its high efficiency and low cost. Vienna rectifiers have unusually high values for system parameters including peak overshoot values and settling time relative to changing input voltage. Increasing system parameters reduced the stability and effectiveness of the system. In this research work, a novel Antlion optimization algorithm (ALO) based sliding mode controller (ALO-SMC) of the single-phase Vienna rectifier has been suggested. The planned ALO-SM controller enhances the system's constraints and lowers fault values to increase stability so that the battery can be charged with the least amount of overshoot and settling time. The Simulink model of the proposed single-phase Vienna rectifier with ALO-SM controller is developed with the MATLAB software tool. The resulting assessment confirms that the proposed ALO-SM controller-based EV charging station yields better output performance with improved stability in a shorter computation time. Finally, the result shows the proposed ALO-SM controller improves the system stability with a minimum overshoot of 1.22% with a settling time of 0.01s for the given 48V battery bank and steady-state error than the existing PSO and PSO-PI controllers respectively.

An adaptive sparse general regression neural network-based force observer for teleoperation system

Restricted by factors such as small robot size and harsh operating environment, the inability to obtain the interaction force between the robot arm and the environment through force sensors has become problematic in promoting the application of teleoperation systems in minimally invasive surgery, nuclear waste cleanup, and other fields. To accurately obtain the interaction force without the force sensors, a force observer based on an adaptive sparse general regression neural network (ASGRNN) is proposed in this paper. The proposed force observer uses a machine learning-based approach to obtain estimated force, thus eliminating the need for the dynamic parameters of the robot arm. Also, an innovative feature selection method incorporating the wrapper method and sparse regularization is proposed to select the input features of the force observer. Secondly, two new criteria are defined to eliminate the useless support vectors in the model. In addition, an improved antlion optimization algorithm (IALO) is proposed to optimize the bandwidth parameters of the model. To verify the performance of the proposed force observer, a 6-degree-of-freedom teleoperation robot experimental platform is built and compared with three existing force estimation models. The results show that the proposed force observer outperforms the existing model in terms of estimation accuracy, and the mean square error (MSE) is at least 35.79% lower than the existing model. In conclusion, this paper provides a feasible and effective force observer for a teleoperation system where force sensors are not applicable and dynamic parameters are non-knowable.

Nodal Electricity Price Based Optimal Size and Location of DGs in Electrical Distribution Networks Using ANT LION Optimization Algorithm

Distribution system has been the weakest link in the entire power system supply chain. It is also one of the most vital parts of the power system. However, a lot of methods have been developed to improve the condition of the distribution system. The use of distributed generations (DGs) is one such method where the generated power is closer to the load center, and the DG is also providing ancillary services to the grid. The nodal electricity price for DGs location is determined based on the Locational Marginal Price (LMP). LMP implies the price to buy and sell power at each node within electrical distribution markets. In the nodal electricity market (EM), the cost of energy is determined by the location of DG to which it is provided. This paper presents a novel approach that utilizes nodal electricity price for optimal sizing and location (OSL) of DGs. A multi-objective ANTLION optimization (MOALO) has been utilized as an optimization approach to compute the OSL of DGs units. ANTLION optimization (ALO) is based on the unique hunting behaviour of antlions. Optimization has been done for social welfare maximization, loss minimization, and voltage profile improvement in distribution networks (DNs). The results of the proposed technique have been evaluated for IEEE 33 bus DNs.