Ant Lion Optimization Algorithm Research Articles

APPLIED LINGUISTICS WITH DEEP LEARNING-BASED DATA-DRIVEN TEXT-TO-SPEECH SYNTHESIZER FOR ARABIC CORPUS

The field of Applied Linguistics, which deals with language and its practical uses, connects with technology in interesting methods, especially in the advancement of text-to-speech (TTS) synthesizers. TTS synthesizers change written text into spoken words, deploying ethics from phonology, phonetics, and syntax to create natural-sounding speech. Within linguistics use, these methods are invaluable purposes such as increasing communication in fractal human–computer interactions (HCIs), language-learning tools, and providing accessibility solutions for visually impaired individuals. TTS purposes at synthesizing understandable and natural speech from text, and it has advanced quickly in recent times because of the progress of artificial intelligence (AI). During past years, deep learning (DL)-based TTS techniques have been established quickly, enabling the generation of natural speech with a high-quality narrator that matches human levels. Creating TTS methods at the quality of the human level has always been the aspiration of speech synthesis practitioners. Although current TTS techniques achieve impressive voice quality, there remains an evident gap in quality compared to human recordings. In this paper, we present an Applied Linguistics with Deep Learning-based Data-Driven Text-to-Speech Synthesizer (ALDL-DDTTS) technique for Arabic corpus. The ALDL-DDTTS technique mainly aims to detect the text and convert it into speech signals on Arabic corpora. In the ALDL-DDTTS technique, a multi-head attention bi-directional long short-term memory (MHA-BiLSTM) approach can be employed with fractal optimization methods to predict the diacritic and gemination signs. Additionally, the Buckwalter code has been deployed for capturing, storing, and displaying the Arabic texts. To boost the efficiency of the ALDL-DDTTS technique, the hyperparameter selection process uses the fractal ant lion optimization (ALO) algorithm. For examining the boost performance of the ALDL-DDTTS methodology, a wide range of simulations is involved. The experimental outcomes illustrated that the ALDL-DDTTS technique reaches better performance than other models.

Green Communication in IoT for Enabling Next-Generation Wireless Systems

Recent developments and the widespread use of IoT-enabled technologies has led to the Research and Development (R&D) efforts in green communication. Traditional dynamic-source routing is one of the well-known protocols that was suggested to solve the information dissemination problem in an IoT environment. However, this protocol suffers from a high level of energy consumption in sensor-enabled device-to-device and device-to-base station communications. As a result, new information dissemination protocols should be developed to overcome the challenge of dynamic-source routing, and other similar protocols regarding green communication. In this context, a new energy-efficient routing protocol (EFRP) is proposed using the hybrid adopted heuristic techniques. In the densely deployed sensor-enabled IoT environment, an optimal information dissemination path for device-to-device and device-to-base station communication was identified using a hybrid genetic algorithm (GA) and the antlion optimization (ALO) algorithms. An objective function is formulated focusing on energy consumption-centric cost minimization. The evaluation results demonstrate that the proposed protocol outperforms the Greedy approach and the DSR protocol in terms of a range of green communication metrics. It was noticed that the number of alive sensor nodes in the experimental network increased by more than 26% compared to the other approaches and lessened energy consumption by about 33%. This leads to a prolonged IoT network lifetime, increased by about 25%. It is evident that the proposed scheme greatly improves the information dissemination efficiency of the IoT network, significantly increasing the network’s throughput.

Maximizing chitin and chitosan recovery yields from Fusarium verticillioides using a many-factors-at-a-time approach

The extraction of chitin and chitosan presents challenges due to the complexity of the process and the influence of many variables. This study aimed to optimize chitin and chitosan extraction from Fusarium verticillioides by analyzing many additives and processing variables and modeling their yields using multiple linear regression (MLR) and evolutionary algorithms. FT-IR analysis confirmed the presence of characteristic bands in the extracted samples, and SEM analysis further revealed the microfibrillar appearance of the chitin and the dense, non-porous structure of the chitosan. The Ant Lion Optimizer (ALO) was employed to select significant factors and optimize model parameters. A transformation was applied to capture nonlinear relationships, and the fine-tuned models showed improved predictive power, with p-values of 0.00203 for chitin and 0.00884 for chitosan. Multi-objective optimization (MOO) using the Adaptive Geometry Estimation-based Multi-Objective Evolutionary Algorithm (AGE-MOEA) further identified significant factors for optimal yields, achieving 3 g of Arginine, 100 ml of culture medium volume, 7 to 11 days of incubation time, 0.2 to 1.76 ml of Oligochitin, 1.4 g of FeSO4, 1.5 g of K2HPO4, and 1 g of NaCl. Therefore, the integration of ALO and AGE-MOEA algorithms effectively modeled and optimized chitin and chitosan yields by maximizing biopolymer recovery, enabling significant industrial exploitation.

Improved TLBO algorithm for optimal energy management in a hybrid microgrid with support vector machine-based forecasting of uncertain parameters

The worldwide need for electrical energy is increasing, and integrating renewable energy sources (RES) into the power grid will enhance the efficient use of clean energy to fulfill the growing demand for energy. However, the uncertain nature of power from the RES like solar and wind, utility price, and load demand, necessitates accurate forecasting of the uncertain parameters (UP) to improve the reliability of the hybrid microgrid. In this work, optimal energy management (EM) of a hybrid AC-DC microgrid (HMG) is proposed which comprises of two phases, forecasting and scheduling. In the former phase, the uncertainties like day-ahead utility price, electrical demand, and power from the RES are forecasted using the support vector machine (SVM) algorithm and the results are compared with the artificial neural network (ANN) algorithm. In the second phase, the improved Teaching and Learning-Based Optimization (ITLBO) algorithm isused to reduce the generation costs over a 24-h period in a hybrid microgrid. The forecasted uncertain parameters are used as input in the second phase. Power trading occurs between the utility grid and the hybrid microgrid based on load demand and bidding costs, aiming to minimize generation costs. The proposed framework's viability and performance are assessed using IEEE standard test systems. The generating cost, as well as the optimal power dispatch of the HMG, is obtained using the ITLBO algorithm, and the results are compared with different meta-heuristic techniques such as the teaching and learning-based algorithm (TLBO), Ant Lion Optimization algorithm (ALO) and the artificial bee colony algorithm (ABC). The results obtained demonstrate the superiority of the SVM algorithm in forecasting and the ITLBO algorithm over other methods in minimizing operating costs.

Revolutionizing photovoltaic consumption and electric vehicle charging: A novel approach for residential distribution systems

AbstractElectric vehicles (EVs) and small photovoltaic (PV) installations advance residential power grids by lowering charging costs and fostering eco‐friendly operations. Yet, the variable nature of EV charging presents challenges to grid reliability. This research introduces a Monte Carlo‐based simulation for predicting EV charging loads and a systematic charging method that integrates a ‘green electricity’ pricing scheme with a joint optimization model for PV and EV management. By applying an improved ant lion optimizer (IALO) algorithm enriched with differential evolution features, an optimization strategy that markedly enhances grid performance is devised. In a park scenario, this ‘green electricity’ model reduced the mean square error of EV charging load by 11.82%, smoothed the power load curve, and improved grid stability. When compared with particle swarm optimization (PSO) and grey wolf optimizer (GWO) algorithms, the IALO algorithm boosted overall revenue by 16.8% and 12.8%, increased PV utilization by 162.3% and 37.1%, and significantly cut carbon emissions by 159.6% and 31.6%, respectively. These outcomes affirm the financial, environmental, and functional benefits of our proposed approach.

High-speed 3D printing temperature system with optimized PlD parameters based on improved ant lion optimization algorithm

An Ant Lion Optimization algorithm based on elite Opposition-based learning and Cosine factors (OCALO) is proposed to address the problem of poor response and stability during heating process in high-speed 3D printing temperature system.The generation of the initial solution in OCALO algorithm is enhanced by the introduction of a new Tent-Logistic-Cotangent composite chaotic mapping, which guarantees the diversity of population. The PID parameters are optimized using the improved algorithm. Compared with two existing classical algorithms and three improved ALO algorithms, the proposed algorithm improves the convergence speed, global search ability and the ability to jump out of the local optimal solution. The outcomes of simulation and experimentation demonstrate that the algorithm improves the transient and steady-state performance of temperature control with better accuracy and robustness. It takes at least 123 s faster than other controllers to reach stability and is more than two times stronger than other PID controllers, making it better suited to high-speed 3D printing temperature systems.

Energy Management Strategy for Wind Solar Storage Microgrid Based on Improved Ant Lion Optimizer

The energy management of microgrids involves optimizing the capacity configuration, which significantly impacts the economic and stable operation of microgrids. This paper presents a control strategy for microgrid operation that effectively manages distributed power sources and energy storage to optimize capacity configuration. A mathematical optimization model for microgrid energy management is established considering minimum annual cost and optimal scale constraints. The traditional Ant Lion Optimizer (ALO) is improved by using dynamic weight coefficients and chaotic mapping to enhance the diversity of the population and improve the convergence of the algorithm. This can effectively avoid local optimal solutions and premature problems, and improve the convergence speed and search ability of the ALO algorithm. Based on this control strategy and improved ALO algorithm, simulations and tests were conducted using actual data from an independent microgrid, resulting in the optimal solution for the microgrid capacity allocation model. The case study results validate the practicality of the proposed microgrid operation control strategy as well as the superiority of the improved ant colony optimization algorithm.

A Novel Equal Area-Equal Width-Equal Bin Numbers Technique Using Salp Swarm Optimization Algorithm for Maximizing the Success Rate of Ball Bearing Assembly

In this work, an algorithmic technique is used to minimize the excess parts and maximize the success rate of selective assembly. In this study, a unique method known as Equal Area-Equal Width-Equal Bin Numbers is introduced to group the parts of a ball bearing assembly by taking into account their range of tolerance. A full factorial design is used to conduct the experiments, and the salp swarm optimization (SSO) algorithm is employed to evaluate the best bin combinations and identify the possibility of making the maximum number of assemblies. Computational results showed a 13.16 percent increase in success rate when compared to prior research when employing the proposed method. Comparing the computational outcomes versus those obtained by the Antlion optimization and Genetic algorithms validates the adoption of the SSO algorithm. A paired T-test is performed to assess the statistical significance of the findings. The convergence plot further supports the superiority of the SSO algorithm.

Enhancing Coordination Efficiency with Fuzzy Monte Carlo Uncertainty Analysis for Dual-Setting Directional Overcurrent Relays Amid Distributed Generation.

In the contemporary context of power network protection, acknowledging uncertainties in safeguarding recent power networks integrated with distributed generation (DG) is imperative to uphold the dependability, security, and efficiency of the grid amid the escalating integration of renewable energy sources and evolving operational conditions. This study delves into the optimization of relay settings within distribution networks, presenting a novel approach aimed at augmenting coordination while accounting for the dynamic presence of DG resources and the uncertainties inherent in their generation outputs and load consumption-factors previously overlooked in existing research. Departing from conventional methodologies, the study proposes a dual-setting characteristic for directional overcurrent relays (DOCRs). Initially, a meticulous modeling of a power network featuring distributed generation is undertaken, integrating Weibull probability functions for each resource to capture their probabilistic behavior. Subsequently, the second stage employs the fuzzy Monte Carlo method to address generation and consumption uncertainties. The optimization conundrum is addressed using the ant lion optimizer (ALO) algorithm in the MATLAB environment. This thorough analysis was conducted on IEEE 14-bus and IEEE 30-bus power distribution systems, showcasing a notable reduction in the total DOCR operating time compared to conventional characteristics. The proposed characteristic not only achieves resilient coordination across a spectrum of uncertainties in both distributed generation outputs and load consumption, but also strengthens the resilience of distribution networks overall.

Joint Estimation of Driving State and Road Surface Adhesion Coefficient of a Four-Wheel Independent and Steering-Drive Electric Vehicle

Vehicle running state parameters and road surface state are crucial to the stability of four-wheel independent drive and steering electric vehicle control. Therefore, this study explores the estimation of vehicle driving state parameters and road surface adhesion coefficients using a combination of federal Kalman filtering and an intelligent bionic antlion optimization algorithm. Firstly, according to the research purpose of the paper and the focus on the accuracy of the establishment of the three degrees of freedom dynamics model, fully considering the road conditions, the paper adopts the Dugoff tire model and finally completes the establishment of the vehicle state estimation model. Secondly, the drive state estimation algorithm is developed utilizing the principles of federal Kalman filtering and volume Kalman filtering. At the same time, robust estimation theory is introduced into the sub-filter, and the antlion optimization module is designed at the lower layer of the main filter to enhance the accuracy of estimates. It is easy to see that the design of the Antlion federal Kalman travel state estimation algorithm has noticeably enhanced accuracy and traceability, according to the result. Thirdly, a joint estimation algorithm of state estimation and road surface adhesion coefficient has been devised to enhance the stability and precision of the estimation process. Finally, the results showed that the joint estimation algorithm has high accuracy in estimating vehicle driving state parameters such as the center of mass lateral deflection angle and road surface adhesion coefficient by simulation.

Many-objective ant lion optimizer (MaOALO): A new many-objective optimizer with its engineering applications

Many-objective optimization (MaO) is an important aspect of engineering scenarios. In many-objective optimization algorithms (MaOAs), a key challenge is to strike a balance between diversity and convergence. MaOAs employs various tactics to either enhance selection pressure for better convergence and/or implements additional measures for sustaining diversity. With increase in number of objectives, the process becomes more complex, mainly due to challenges in achieving convergence during population selection. This paper introduces a novel Many-Objective Ant Lion Optimizer (MaOALO), featuring the widely-popular ant lion optimizer algorithm. This method utilizes reference point, niche preserve and information feedback mechanism (IFM), to enhance the convergence and diversity of the population. Extensive experimental tests on five real-world (RWMaOP1- RWMaOP5) optimization problems and standard problem classes, including MaF1-MaF15 (for 5, 9 and 15 objectives), DTLZ1-DTLZ7 (for 8 objectives) has been carried out. It is shown that MaOALO is superior compared to ARMOEA, NSGA-III, MaOTLBO, RVEA, MaOABC-TA, DSAE, RL-RVEA and MaOEA-IH algorithms in terms of GD, IGD, SP, SD, HV and RT metrics. The MaOALO source code is available at: https://github.com/kanak02/MaOALO.

Enhancement of Brain Computer Interface System Based on Artificial Intelligent Technique

With the rapid development of information technology, the EEG has become an increasingly popular method for analyzing brain signals. EEG signals greatly contribute to(BCI) brain-computer communication . BCI outputs are used to restore many functions in relation to individuals with a motor impairment, and these signals are complex random signals produced from hundreds of millions of neurons in EEG mixtures in the brain that contain many data about brain activity. its include artefacts which have a great influence on the diagnosis; Hence, these unwanted signals become the most important problem in EEG signal analysis. Therefore, used four blind source separation techniques (BSS), STONE, FICA, BEFICA and EFICA. The proposed system is using one of the new Antlion (ALO) optimization algorithms to improve the performance of the previous algorithms and find out which ones are the most responsive, by comparing them and choosing the best ones according to the (PSD) standard. With the use of real data, noticed through the results that the EFICA algorithm is the best response to the improvement and the most efficient, as the ratio of (PSD) was the least possible, as ALO worked to reduce thevariance in the distribution of the frequency spectrum because it relied on solving constrained problems using various searches.

Land subsidence prediction in coal mining using machine learning models and optimization techniques.

Land surface subsidence is an environmental hazard resulting from the extraction of underground resources. In underground mining, when mineral materials are extracted deep within the ground, the emptying or caving of the mined spaces leads to vertical displacement of the ground, known as subsidence. This subsidence can extend to the surface as trough subsidence, as the movement and deformation of the hanging-wall rocks of the mining stope propagate upwards. Accurately predicting subsidence is crucial for estimating damage and protecting surface buildings and structures in mining areas. Therefore, developing a model that considers all relevant parameters for subsidence estimation is essential. In this article, we discuss the prediction of land subsidence caused by the caving of a stop's roof, focusing on coal mining using the longwall method. The main aim of this research is to improve the accuracy of prediction models of land subsidence due to mining. For this purpose, we consider a total of 11 parameters related to coal mining, including mining thickness and depth (related to the deposit), as well as density, cohesion, internal friction angle, elasticity modulus, bulk modulus, shear modulus, Poisson's ratio, uniaxial compressive strength, and tensile strength (related to the overburden). We utilize information collected from 14 coal mines regarding mining and subsidence to achieve this. We then explore the prediction of subsidence caused by mining using the gene expression programming (GEP) algorithm, optimized through a combination of the artificial bee colony (ABC) and ant lion optimizer (ALO) algorithms. Modeling results demonstrate that combining the GEP algorithm with optimization based on the ABC algorithm yields the best subsidence prediction, achieving a correlation coefficient of 0.96. Furthermore, sensitivity analysis reveals that mining depth and density have the greatest and least effects, respectively, on land surface subsidence resulting from coal mining using the longwall method.

Enhanced FOPID Controller Based Cascaded Inverter for Grid-Connected Photovoltaic System

An enhanced controller is proposed to investigate grid-connected photovoltaic systems employing cascaded two-level inverters. The primary focus is on optimizing power output, achieved through the development, modeling, and testing of photovoltaic systems using the suggested upgraded controller. This advanced controller, specifically a Fractional Order PID (FOPID) controller, incorporates the Ant-Lion Optimizer (ALO) algorithm for enhanced performance compared to conventional controllers. The FOPID controller’s reliability is emphasized, and further improvements are pursued by optimizing gain parameters through the prescribed method. Power supply under both schemes is examined, and the controller’s performance in extracting maximum power under specified operating conditions is deemed satisfactory. To validate the proposed approach, practical implementation is conducted using the MATLAB/Simulink platform. The effectiveness of proposed and conventional methodologies for analyzing the id and iq currents is assessed. A comparison is drawn between the suggested approach and current methods such as the base controller, GSA, and ABC methodologies. In comparison to the existing methods, the suggested FOPID controller demonstrates superior performance in maintaining the DC link voltage, achieving an efficiency of 94%, while the GSA method reaches 91.5%, the ABC method achieves 91%, and the base controller achieves 90%.

Multi-Objective Optimization of Cell Voltage Based on a Comprehensive Index Evaluation Model in the Aluminum Electrolysis Process

In the abnormal situation of an aluminum electrolysis cell, the setting of cell voltage is mainly based on manual experience. To obtain a smaller cell voltage and optimize the operating parameters, a multi-objective optimization method for cell voltage based on a comprehensive index evaluation model is proposed. Firstly, a comprehensive judgment model of the cell state based on the energy balance, material balance, and stability of the aluminum electrolysis process is established. Secondly, a fuzzy neural network (FNN) based on the autoregressive moving average (ARMA) model is designed to establish the cell-state prediction model in order to finish the real-time monitoring of the process. Thirdly, the optimization goal of the process is summarized as having been met when the difference between the average cell voltage and the target value reaches the minimum, and the condition of the cell is excellent. And then, the optimization setting model of cell voltage is established under the constraints of the production and operation requirements. Finally, a multi-objective antlion optimization algorithm (MOALO) is used to solve the above model and find a group of optimized values of the electrolysis cell, which is used to realize the optimization control of the cell state. By using actual production data, the above method is validated to be effective. Moreover, optimized operating parameters are used to verify the prediction model of cell voltage, and the cell state is just excellent. The method is also applied to realize the optimization control of the process. It is of guiding significance for stabilizing the electrolytic aluminum production and achieving energy saving and consumption reduction.

Filtered Ofdm System Model for Papr Reduction in the Growth Of 5g

A proposed waveform for fifth generation (5G) communications is the filtered orthogonal frequency division multiplexing (F-OFDM) system. Filtering-based waveform frameworks distinguish themselves by suppressing out-of-band emission and asynchronous transmission. Meanwhile, the high Peak-To-Average Power Ratio (PAPR) remains a barrier to new waveform possibilities. Partials transmit sequence (PTS) is a successful strategy for reducing high PAPR in multicarrier networks. This work presents a multicarrier in OFDM and UFMC based on the Filter Bank Multicarrier (FBMC) waveform for communication systems. Furthermore, FBMC waveforms have limited orthogonality between subcarriers, which can mitigate Doppler and multipath effects. This FBMC approach uses the cosine law to filter the traffic signal, and the Welch algorithm is offered for segmentation, which lowers noise in estimated power spectra. As a result, a hybrid Selective Mapping (SLM) with Cuckoo Search and Ant Lion Optimization (CS-ALO) algorithms is suggested using the FBMC framework to reduce PAPR in the OFDM system. The proposed work is implemented, and detailed experimental research is carried out to show the increased performance in 5G networks. Based on a fair comparison, the F-OFDM scheme outperforms previous approaches in terms of BER and multicarrier system capacity. Simulation results show that the proposed waveform design and processing method outperform standard OFDM and F-OFDM methods in PAPR reduction and communication integrated systems.

Ant-Lion Optimization Algorithm Based Optimal Performance of Micro Grids

Ant-Lion Optimization Algorithm Based Optimal Performance of Micro Grids

A quality-of-service aware composition-method for cloud service using discretized ant lion optimization algorithm

A quality-of-service aware composition-method for cloud service using discretized ant lion optimization algorithm

Robust adaptive backstepping control of H‐bridge inverter based on type‐2 fuzzy optimization of parameters

AbstractA type‐2 fuzzy logic‐based adaptive backstepping control (T2FABSC) approach is designed for an H‐bridge inverter. This inverter has an LC filter to decrease the level of total harmonic distortion (THD) that can affect the efficiency of the system. Reduction of THD and stability insurance of the filter are challenging trade‐offs, tackled here by a controller design. The performance, however, is not suitable for actual applications under a wider range of disturbances, and the parameters need to be adjusted once more for more dependable operations. Backstepping control is given a Lyapunov definition‐based adaptive mechanism that can improve this scheme's stability and robustness in the face of numerous disturbances. Additionally, the system is viewed as a “Black box” without the need for a precise mathematical model, which might lead to a lighter computing burden and simpler implementation. Moreover, a fuzzy type‐2 structure is adopted that can optimize the gains of the adaptive Backstepping controller (ABSC) in challenging conditions. The antlion optimization algorithm is employed to optimize the parameters of the membership functions in the fuzzy system, hence improving the performance of the controller. In addition, compared with different optimization algorithms, it can more quickly and accurately locate the best solutions. Finally, the findings of both the simulations and the experimental outputs are examined, proving the T2FABSC's significant robustness and faster dynamics in a variety of challenging circumstances.

Diesel engine quality abnormal patterns recognition based on feature fusion and adaptive decision fusion

The current assembly process of marine diesel engines is low in intelligence and the control chart pattern classifier with unstable performance, which makes it difficult to control and identify the quality control chart pattern. This paper proposes a new assembly quality control diagram recognition method based on an adaptive decision model to address these problems. Through characteristics and changes of the diesel engine assembly process analyses, the triangular norm is used to fuse the extracted shape features and statistical features to reduce the influence of data fluctuation and imbalance on pattern recognition. An adaptive decision fusion model of the assembly process is established by defining multiple weights with considering the complexity and uncontrollability of the diesel engine assembly process. Based on these, the fusion coefficients within the adaptive decision model are optimized by the Ant Lion Optimization algorithm (ALO) to improve the decision efficiency and classification precision. To validate the proposed model, diesel engine exhaust pressure is selected as a case for abnormal pattern recognition, and the ability of the model is discussed in terms of recognition accuracy and stability.