Ant Lion Research Articles

Optimal EV Charging Using ANT Lion Optimized ANN Based MPPT with High Gain Modified Zeta Converter

Optimal EV Charging Using ANT Lion Optimized ANN Based MPPT with High Gain Modified Zeta Converter

A Hybrid Cryptographic Mechanism for Secure Data Transmission in Edge AI Networks

As Edge AI systems become more prevalent, ensuring data privacy and security in these decentralized networks is essential. In this work, a novel hybrid cryptographic mechanism was presented by combining Ant Lion Optimization (ALO) and Diffie–Hellman-based Twofish cryptography (DHT) for secure data transmission. The developed work collects the data from the created edge AI system and processes it using the Autoencoder. The Autoencoder learns the data patterns and identifies the malicious data entry. The Diffie–Hellman (DH) key exchange generates a shared secret key for encryption, while the ALO optimizes the key exchange and improves security performance. Further, the Twofish algorithm performs the data encryption using a generated secret key, preventing security threats during transmission. The implementation results of the study show that it achieved a higher accuracy of 99.45%, lower time consumption of 2 s, minimum delay of 0.8 s, and reduced energy consumption of 3.2 mJ.

Techno-economic optimization for isolated hybrid PV/wind/battery/diesel generator microgrid using improved salp swarm algorithm.

The main objective of this study is to develop a new method for solving the techno-economic optimization problem of an isolated microgrid powered by renewable energy sources like solar panels, wind turbines, batteries, and diesel generators while minimizing greenhouse gas emissions. An Improved Salp Swarm Algorithm (ISSA) with a position adaptation mechanism for the salp leader that involves a leader salp that moves about depending on both food availability and its previous position has been proposed to overcome the convergence problem. In the original SSA, as the approach converges, it can no longer find optimal solutions and becomes trapped in a local minimum. Three Microgrid System (MS) configurations are discussed: PV/WT/BESU/DG, PV/BESU/DG, and WT/BESU/DG. The proposed method seeks to find a middle ground between technical criteria and environmental concerns when deciding on PV, WT, BESU, and DG sizes. The findings indicate that the proposed ISSA approach gives superior results compared to other well-known algorithms like the original SSA, the Ant Lion Optimizer (ALO), the Dragonfly Approach (DA), and the Moth-Flame Optimization Algorithm (MFO), which, after significant investigation, has been proven to help determine the appropriate microgrid size. With PV sizes of 10, 9 WT, 24 BESU, and 3 DG, the PV/WT/BESU/DG configuration offers the highest level of cost-effectiveness with Cost of Energy (COE) of 0.2109 $/kWh, Net Present Cost (NPC) of 376,063.8 $, Loss of Power Supply Probability (LPSP) of 4%, Renewable Energy Fraction (REF) of 96%, and CO2 emission of 12.4457 tons/year. ISSA is brought up as a possible solution to both the problem of rising energy prices and the difficulties inherent in microgrid design.

Load frequency control in deregulated power system with renewable energy sources: Hybrid GOA‐SNN technique

AbstractThis paper proposes a hybrid technique for load frequency control (LFC) in an inter‐connected deregulated power‐system. The proposed method is the combination of a gannet optimization algorithm (GOA) and spiking neural network (SNN), hence, it is named as GOA‐SNN technique. The objective of the proposed method is to minimize frequency deviations within the power system (PS). By lessening the frequency‐deviation and tie‐line power variation, this approach ensures system frequency‐control under the effect of load disturbances. The GOA method is utilized to generate the set of control signals of the controller. The SNN method is used to predict the optimum gain parameter of the controller. By then the proposed method is run in MATLAB software and evaluated their performance with various existing approaches. The proposed method shows better results than other existing methods, such as Ant Lion Optimization (ALO), particle swarm optimization (PSO), and Salp Swarm Algorithm (SSA). The GOA‐SNN approach shows a low Area control error is 0.48% and a high efficiency is 96% compared with other existing approaches.

A hybrid technique linked FOPID for a nonlinear system based on closed-loop settling time of plant

Wind and hydroelectric systems are more cost-effective and environmentally beneficial. A hybrid technique is proposed for the fractional-order proportional-integral-derivative (FOPID) controller to regulate the wind and hydro system. The proposed hybrid technique combines the feedback-artificial-tree (FAT), and atomic-orbital-search (AOS); together known as FAT-AOS approach. The proposed technique is utilized to decide the optimum controller parameters, and it guarantees system constancy in large disturbances using less computation and overshoot by restraining the parameter variation. The FAT is used to predict the optimum gain parameter of FOPID, and minimizing the system error is accomplished with the AOS approach. The performance metrics are peak time, rise time, settling time, and peak overshoot, are analyzed. The performance of the proposed method is done in the MATLAB platform. The simulation result of proposed approach for the rise time as 0.001 sec, settling time is 0.012 sec, and the overshoot percentage is 0.02 %. By comparing the existing methods, like Ant lion optimizer (ALO), Salp swarm algorithm (SSA), Particle swarm optimization (PSO), the proposed approach rise time and settling time overshoot, is less. The comparison proves that the proposed system delivers improved outcome than existing systems.

A Novel Hybrid Optimization-based Backstepping Fractional Order Sliding Mode Proportional-integral-derivative Controller for Nonlinear Biological System

Nowadays, the role of the control system in the bioengineering field plays a major part due to its significant improvement and demand. Usually, if there are any changes in the parameter of a biological system, then it tends to cause crucial diseases in humans. Previously, various control algorithms have been developed; however, the proper control process failed due to the high error rate, slow convergence, Computational Complexity, Tuning Complexity and inaccurate function. The proposed control strategy should ensure accurate tracking of the desired trajectory or reference signal, even in the presence of uncertainties and disturbances. The control system should be capable of adapting to changes in the system dynamics and maintaining stable operation. This complexity can lead to high computational requirements, making the controller computationally expensive and potentially unsuitable for real-time applications or systems with limited computing resources. To overcome these issues, in this work, the novel Hybrid Ant Lion Moth Flame Optimization (HALMFO) based Backstepping Fractional-Order Sliding mode Proportional-Integral-Derivative (BFO-SPID) controller is proposed for different nonlinear biological systems. The proposed method enhances the efficiency, improved performance, robustness, convergence speed, and solution quality of the optimization process. This paper addresses the challenge of controlling nonlinear biological systems by proposing a novel hybrid optimization-based backstepping approach combined with a fractional order sliding mode proportional-integral-derivative (PID) controller. This work’s essential contribution lies in addressing existing control strategies’ limitations by integrating multiple control techniques to achieve robust tracking performance and disturbance rejection. This system proposes predictive control with state estimation based on the backstepping sliding mode method. The gain parameters of the developed controller are optimized using the HALMFO method. The proposed control approach performance has been tested under biological systems such as Protein, Pancreas, and Genetic Regulatory Network (GRN). The execution of this proposed model is done in MATLAB/Simulink. Furthermore, the performance of the proposed control technique is compared with various state-of-the-art methods in terms of accuracy, Mean Average Error (MAE), Mean Square Error (MSE), settling time, overshoot, and rise time. The comparison illustrates that the projected controller has reached a steady condition with the least error rate in the control application. Thus the developed controller regulates the parameter variations in the biological system as per the desired level, providing significant effective outcomes to complex problems.

Optimizing brushless direct current motor design: An application of the multi-objective generalized normal distribution optimization

In this study, we tackle the challenge of optimizing the design of a Brushless Direct Current (BLDC) motor. Utilizing an established analytical model, we introduced the Multi-Objective Generalized Normal Distribution Optimization (MOGNDO) method, a biomimetic approach based on Pareto optimality, dominance, and external archiving. We initially tested MOGNDO on standard multi-objective benchmark functions, where it showed strong performance. When applied to the BLDC motor design with the objectives of either maximizing operational efficiency or minimizing motor mass, the MOGNDO algorithm consistently outperformed other techniques like Ant Lion Optimizer (ALO), Ion Motion Optimization (IMO), and Sine Cosine Algorithm (SCA). Specifically, MOGNDO yielded the most optimal values across efficiency and mass metrics, providing practical solutions for real-world BLDC motor design. The MOGNDO source code is available at: https://github.com/kanak02/MOGNDO.

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.

Optimal allocation of distributed energy storage systems to enhance voltage stability and minimize total cost.

The enhancement of energy efficiency in a distribution network can be attained through the adding of energy storage systems (ESSs). The strategic placement and appropriate sizing of these systems have the potential to significantly enhance the overall performance of the network. An appropriately dimensioned and strategically located energy storage system has the potential to effectively address peak energy demand, optimize the addition of renewable and distributed energy sources, assist in managing the power quality and reduce the expenses associated with expanding distribution networks. This study proposes an efficient approach utilizing the Dandelion Optimizer (DO) to find the optimal placement and sizing of ESSs in a distribution network. The goal is to reduce the overall annual cost of the system, which includes expenses related to power losses, voltage deviation, and peak load damand. The methods outlined in this study is implemented on the IEEE 33 bus distribution system. The outcomes obtained from the proposed DO are contrasted with those of the original system so as to illustrate the impact of ESSs location on both the overall cost and voltage profile. Furthermore, a comparison is made between the outcomes of the Ant Lion Optimizer (ALO) and the intended Design of Experiment DO, revealing that the DO has obtained greater savings in comparison to the ALO. The recommended methodology's simplicity and efficacy in resolving the researched optimization issue make the acquired locations and sizes of ESSs favorable for implementation within the system.

Optimization of Laser Cutting Parameters for PMMA Using Metaheuristic Algorithms

This study fixates on determining the optimum laser input parameters that simultaneously meet the desired kerf width and depth during CO2 laser cutting of various polymethylmethacrylate (PMMA) sheets. It has three contributions. The first is to model the cutting process of PMMA by polynomial curve fitting as a function of laser power, laser speed, and standoff distance. R-squared (R2), adjusted R2 and root-mean-square error (RMSE) criteria were taken into account to measure the performance of the proposed model. The effect of laser parameters on the process is investigated by analysis of variance (ANOVA) and sensitivity analysis. The second is to optimize the derived nonlinear regression models using genetic algorithm (GA), particle swarm optimization (PSO), whale optimization algorithm (WOA) and ant lion optimization (ALO) metaheuristic methods and compare the performances of the algorithms. The third is to compare the adequacy of the optimization process with the artificial neural network (ANN). The investigations exhibited that the best-fitting polynomials are obtained with the R2 and adjusted R2 values of over 99% and 97%, respectively. ANOVA and sensitivity test revealed that the sensitivity of the laser power, which is the most effective parameter, was 150 at low powers and decreased to 0 as the power value increased. When the nozzle distance is 4.1, the proposed metaheuristics gave effective and sufficiently accurate results. PSO stood out in terms of both best cost value (3.49 × 10–8) and relative error value (0.19%). The relative error of the ANN was found as 3% in terms of kerf depth.

A predictive energy-aware scheduling strategy for scientific workflows in fog computing

Fog computing paradigm provides diverse processing resources and storage close to the edge of Internet of Things (IoT) networks. Workflow scheduling is an open issue in fog computing as it addresses the deployment of IoT workflow tasks on fog cells. Deploying workflow tasks on fog cells in an inefficient manner can have negative consequences. It can lead to bandwidth waste, resource depletion, and significant operating expenses. Additionally, the majority of scheduling evolutionary algorithms suffer from premature convergence and sticking in local optima. In this paper, a predictive energy-aware scheduling framework is proposed as a MAPE-K control model consisting of the four Monitor, Analyzer, Planner and Executer components with a shared Knowledge base in fog computing. First, a prediction method applying an Adaptive Network-based Fuzzy Inference System (ANFIS) into an analyzer component is introduced to predict future resource load. Second, a resource management strategy based on predicted resource load is presented to reduce energy consumption. Third, the Improved Ant Lion Optimizer (ALO) and weighted Grey Wolf Optimizer (GWO) are combined into a planner component called I-ALO-GWO for workflow scheduling. In the end, the decisions made in the previous steps are executed on the fog cells. The effectiveness of the I-ALO-GWO evolutionary algorithm has been tested on the IEEE CEC2019 benchmark functions. In addition, the proposed approach is evaluated practically under several famous scientific workflows using the iFogSim tool. Experimental results indicate that I-ALO-GWO improves the makespan up to 18 %, the energy consumption up to 17 %, the total execution cost up to 11 % and 26 % in terms of efficiency in comparison with the second-best results.

Proposing hybrid prediction approaches with the integration of machine learning models and metaheuristic algorithms to forecast the cooling and heating load of buildings

Accurate prediction of heating and cooling loads in residential buildings is crucial for both researchers and practitioners. This study employs advanced forecasting techniques, utilizing support vector regression and extreme gradient boosting methods, enhanced by state-of-the-art metaheuristic algorithms. The metaheuristic optimizers applied include the Satin Bowerbird Optimizer, Ant Lion Optimizer, Artificial Ecosystem-Based Optimizer, Slime Mold Optimizer, Moth-Flame Optimizer, and Particle Swarm Optimizer. These optimizers are strategically used to refine the forecasting models, optimizing their parameters for increased precision. To evaluate the models, a suite of statistical indices is used, including mean square error, root mean square error, Mean Absolute Percentage Error, Mean Absolute Error, Relative Absolute Error, coefficient of correlation, Coefficient of Determination, and Normalized Mean Square Error. This comprehensive analysis assesses the effectiveness of the forecasting methods. The study's findings highlight that the combination of the Satin Bowerbird Optimizer and extreme gradient boosting is particularly effective, achieving high coefficients of determination for cooling and heating load predictions (0.95826 and 0.938048, respectively). This hybrid algorithm shows remarkable performance, with minimal error values and consistent convergence across various datasets. In summary, this research underscores the efficiency of integrating sophisticated machine learning models with metaheuristic optimization techniques, particularly identifying a potent hybrid algorithm for the accurate prediction of heating and cooling loads in residential buildings.

Analyzing the effect of power loss allocation to utilities optimally in power systems using constrained load flow

In present day power system operation and control needs modern methodologies for controlling the power flow in power lines. Usually power electronic based converters are used for diverting/controlling the power flow through power lines which is a costlier solution. The alternative solution to this problem is to impose constraints on the system variables. In this paper, shunt capacitors are connected at loadsfor maintaining the desired magnitude at loads. Also, the losses thus obtained are allocated to respective participants (generators/loads) based on their contributions using tracing based methodology. In this, the proportional sharing principal is used to identify the contribution of participants in the power losses. The effect of imposing constraints on loss allocation to participants is analyzed at OPF (OPF) condition. For solving this, a new methodology based on improved ant lion optimization algorithm is developed. Using this method, the OPF problem wasansweredagainst system limitations. The developed methodologies are tested on standard IEEE-14 bus with supportivenumerary.

Hybrid whale optimization algorithm for enhanced routing of limited capacity vehicles in supply chain management

The present study focuses on the problem of vehicle routing with limited capacity, with the objective of minimizing the transportation distance required to serve h clients with predetermined locations and needs. The aim is to create k trips that cover the shortest possible distance. To achieve this goal, a hybrid whale optimization algorithm (hGWOA) is proposed, which combines the whale optimization algorithm (WOA) with the grey wolf optimizer (GWO). The proposed hybrid model is comprised of two main steps. First step, the GWO’s hunting mechanism is integrated transitioning to the utilization phase of WOA, and a newly devised state is introduced that is linked to GWO. In the second step, a novel technique is incorporated into the exploration mission phase to enhance the resolve after per iteration. The algorithm’s performance is assessed and compared with other modern algorithms, including the GWO, WOA, ant lion optimizer (ALO), and dragonfly algorithm (DA) using 23 benchmark test functions and CEC2017 benchmark test function. The results indicate that the hybrid hGWOA method outperforms other algorithms in terms of delivery distance optimization for scenarios involving scale and complexity. These findings are corroborated through case studies related to cement delivery and a real-world scenario in Viet Nam.

Application of optimization‐based regression analysis for evaluation of frost durability of recycled aggregate concrete

AbstractConcrete constructed using recycled aggregates in place of natural aggregates is an efficient approach to increase the construction sector's sustainability. To improve recycled aggregate concrete () technologies in permafrost, it is essential to certify the stability in frost‐induced conditions. The main goal of this study was to use support vector regression () methods to forecast the frost durability () of on the basis of durability agent value in cold climates. Herein, three optimization methods called Ant lion optimization (), Grey wolf optimization (), and Henry Gas Solubility Optimization () were employed for indicating optimal values of key parameters. The results depicted that all developed models have capability in predicting the of in cold regions. The values of as a comprehensive index depicted that the model has the higher value at 0.0571 as the weakest model, then at 0.0312 recognized as the second‐highest model, and finally the system at 0.0224 mentioned as outperformed model. and approaches were likewise capable of accurately forecasting the of in cold regions, but the created method outperformed them all when taking into account the explanations and justifications.

A Dynamic Workflow Scheduling Method based on MCDM Optimization that Manages Priority Tasks for Fault Tolerance

Because it offers efficient on-demand service delivery over the internet, cloud computing has become more and more popular. An architectural model for cloud computing energy management is provided by the suggested Ant Lion algorithm. Virtual Machines (VMs) in cloud systems are assigned to hosts based not so much on their overall and long-term use, but rather on their immediate resource consumption, including RAM availability. The placement and scheduling processes are frequently computationally demanding and have the potential to affect the performance of deployed virtual machines. In this research work, we offer a strategy that considers the historical resource use of virtual machines (VMs) over time while scheduling them in the cloud. Our goal is to use the Ant lion approach to schedule virtual machines (VMs) in a way that maximizes performance by evaluating the utilization levels of prior VMs. The goal is to reduce the degradation of performance brought about by Cloud administration tasks like as virtual machine deployment, which might impact systems that have already been installed. Furthermore, congested virtual machines (VMs) sometimes take up resources from nearby VMs, increasing the VMs' actual CPU use. Our results show that by learning and adjusting to system behavior over time, our strategy outperforms conventional instant-based physical machine selection. We offer the idea of scheduling virtual machines (VMs) using resource monitoring data from past VM resource use. By using the Ant lion classifier, four fewer physical machines are needed.

Enhancing engineering optimization using hybrid sine cosine algorithm with Roulette wheel selection and opposition-based learning

Meta-heuristic algorithms distinguish themselves from conventional optimization methods owing to their intrinsic adaptability and straightforward implementation. Among them, the sine cosine algorithm (SCA) is lauded for its ability to transition seamlessly between exploration and exploitation phases throughout the optimization process. However, there exists potential for enhancing the balance that SCA maintains between exploration and exploitation. To augment the proficiency in global optimization of SCA, an innovative strategy—nSCA—that integrates the roulette wheel selection (RWS) with opposition-based learning was formulated. The robustness of nSCA was rigorously evaluated against leading-edge methods such as the genetic algorithm (GA), particle swarm optimization, moth-flame optimization, ant lion optimization, and multi-verse optimizer, as well as the foundational SCA. This evaluation included benchmarks set by both CEC 2019 and CEC 2021 test functions. Additionally, the performance of nSCA was confirmed through numerous practical optimization problems, emphasizing its effectiveness in applied settings. In all evaluations, nSCA consistently showcased superior performance compared to its evolutionary algorithm counterparts, delivering top-tier solutions for both benchmark functions and real-world optimization challenges. Given this compelling evidence, one can posit that nSCA serves as a strong candidate for addressing intricate optimization challenges found in real-world contexts, regardless of whether they are of a discrete or continuous nature.

Enhanced melanoma detection using a fuzzy ensemble approach integrating hybrid optimization algorithm

The high death rate of melanoma makes it an important public health issue all over the world. Therefore, early detection of melanoma is crucial for a good prognosis. Deriving meaningful features from dermoscopic pictures, however, is difficult for a number of reasons, including a lack of training data, inconsistent classes, and intra-class variability. In order to solve this issue, we offer an automated technique for melanoma diagnosis from dermoscopic pictures by using high-level characteristics obtained from a powerful CNN architecture and LightGBM. The Ant Lion Optimization may get fixated on a local optimum rather than a global optimum as the problem's complexity rises. This may be a stumbling block to a more desirable ideal solution. The Hybrid Ant Lion Optimization (ALO) and Gray Wolf Optimization algorithm (GWO) combines elements from both ALO and GWO to benefit from their respective strengths and improve optimization performance. The HAM10000 dataset was utilized for this research, and it contains information on a variety of skin cancers, some of which are more common than others. The experimental analysis is done in python and the suggested model successfully distinguished between the two forms of skin cancer with an accuracy of 97.82%, recall of 96.80%, precision of 96.01%, and F1-score of 96.49%. The accuracy of ResNet is at 89.23, that of MLP at 90.87, of EfficientNet at 91.05, of ANN at 92.19, of IN3 at 93.73, and that of MobileNet at 94.90. The results shown that the suggested model outperforms baseline methods, providing substantial assistance to dermatologists and health specialists in the diagnosis of skin cancer.

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

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

Hybrid Ant Lion Mutated Ant Colony Optimizer Technique With Particle Swarm Optimization for Leukemia Prediction Using Microarray Gene Data

Hybrid Ant Lion Mutated Ant Colony Optimizer Technique With Particle Swarm Optimization for Leukemia Prediction Using Microarray Gene Data