Bat Algorithm Research Articles

Advancements in Image Enhancement and Attention based EfficientDet Optimization Classifier for Precise Osteosarcoma Lung Nodule Detection

Introduction: Osteosarcoma is a malignant bone tumor that frequently spreads to the lungs, hence therapy effectiveness depends on early identification. However, noise and subtle characteristics still pose a challenge for reliable Lung Nodules Detection (LND) in medical pictures. In earlier work, SSD-VGG16 was implemented to provide a bounding box with an accuracy score that represented a single osteosarcoma nodule. Increasing model complexity is sometimes necessary to achieve improved accuracy with current approaches, which might worsen their computing inefficiencies.Methods: For accurate osteosarcoma lung nodule identification, this study offers the hybrid Dynamic Virtual Bats Algorithm with Attention based Efficient Object identification (A- EfficientDet). In order to improve the quality and informativeness of clinical pictures, this study suggests including Chebyshev filtering into the pre-processing pipeline. It focuses on CT scans for the purpose of detecting lung nodules associated with osteosarcoma. Additionally, provide the optimized A-EfficientDet model, a hybrid EfficientDet model improved using the DVBA optimization technique for accurate lung nodule identification. Results: The effectiveness of the suggested strategy in attaining accurate osteosarcoma LND is demonstrated by the experimental findings. Chebyshev filtering is incorporated during the pre-processing step, which leads to more accurate detection findings by improving the signal-to-noise ratio (SNR) and lung nodule visibility. Conclusion: Additionally, the improved EfficientDet model demonstrates its suitability for clinical applications in early osteosarcoma detection and treatment monitoring by achieving (SOTA) State-Of-The-Art execution by the metrics of sensitivity, specificity, and F1 score.

Harmonic Detection Methods and Optimization Strategies for Charger in Power Grid

The charger’s operation in the power grid will cause harmonic pollution, affecting the service life of the equipment and the quality of the power grid. The harmonic characteristics and detection during the charging process are studied. The simulation model of charger is established first using MATLAB, the results of the experiment show that odd harmonics are mainly generated during charging, and the distortion rate of the current harmonics remains between 0.231 and 0.443 during the charging process. A new wavelet neural network is proposed to detect the harmonic method. Experimental results indicate that, compared with previous detection methods, this network has better detection efficiency and accuracy for harmonics. But detection result of this network is unstable and does not even converge. A new adaptive bat algorithm with improved focusing distance is presented here to optimize the wavelet neural network, which effectively solves the defect of being sensitive to the node number of hidden layer, resulting in higher detection accuracy, faster detection speed, and stable convergence. The detection error range is reduced from −0.03 to 0.02, and the number of iterations is reduced to around 1000.

Bat algorithm based on kinetic adaptation and elite communication for engineering problems

AbstractThe Bat algorithm, a metaheuristic optimization technique inspired by the foraging behaviour of bats, has been employed to tackle optimization problems. Known for its ease of implementation, parameter tunability, and strong global search capabilities, this algorithm finds application across diverse optimization problem domains. However, in the face of increasingly complex optimization challenges, the Bat algorithm encounters certain limitations, such as slow convergence and sensitivity to initial solutions. In order to tackle these challenges, the present study incorporates a range of optimization components into the Bat algorithm, thereby proposing a variant called PKEBA. A projection screening strategy is implemented to mitigate its sensitivity to initial solutions, thereby enhancing the quality of the initial solution set. A kinetic adaptation strategy reforms exploration patterns, while an elite communication strategy enhances group interaction, to avoid algorithm from local optima. Subsequently, the effectiveness of the proposed PKEBA is rigorously evaluated. Testing encompasses 30 benchmark functions from IEEE CEC2014, featuring ablation experiments and comparative assessments against classical algorithms and their variants. Moreover, real‐world engineering problems are employed as further validation. The results conclusively demonstrate that PKEBA exhibits superior convergence and precision compared to existing algorithms.

An improved BAT algorithm for collaborative dynamic target tracking and path planning of multiple UAV

It is well known that multi-UAV cooperative dynamic target path planning is a challenging field. In this field, multi-UAV cooperative dynamic target path planning is very important to achieve efficient task completion. However, the existing algorithms have some limitations in solving the problems of insufficient search range, premature convergence, local optimization and insufficient population diversity. In order to solve these problems and improve the efficiency and accuracy of path planning, this paper proposes an innovative method. Firstly, we use A* algorithm to obtain the initial assignment result of UAV target, and then use Hungarian algorithm and genetic algorithm to optimize the assignment result and expand the target assignment range. Secondly, in the path planning stage, the bat algorithm is improved, and sine function, dynamic expansion factor and nonlinear function are introduced to solve the problems of insufficient search range, premature convergence and local optimization. At the same time, genetic algorithm is used to solve the problem of insufficient population. By optimizing target assignment and path planning, the performance of multi-UAV cooperative system is improved, so as to better adapt to the task requirements in dynamic environment and provide more reliable solutions for UAV applications. The experimental results show that the tracking and path planning are more effective than BA, CCWOA and OUMPOA under the condition of good stability. Compared with BA, the fitness function and convergence speed are improved by 34.64% and 30.29% respectively.

BA-Optimized Variable Domain Fuzzy PID Control Algorithm for Water and Fertilizer Ratio Control System in Cotton Field

Due to the time-varying, hysteresis and nonlinear characteristics of fertilizer concentration control in the water–fertilizer ratio control system, common control algorithms such as PID and fuzzy PID cannot obtain the expected control effect. In order to accurately control the cotton field water–fertilizer ratio regulation system drip irrigation process of the water–fertilizer ratio that will be controlled within a reasonable range, it is needed to design a bat-optimized variable-domain fuzzy PID water–fertilizer ratio control strategy, through the use of bat algorithm to find out the optimal expansion factor and the best domain of the current conditions, and then according to the changes in working conditions to automatically adjust the fuzzy control of the domain, through the control of the valve openings to change the fertilizer pump back to the amount of water. Realize the fast and precise control of fertilizer concentration in the water–fertilizer ratio control system. Comparative tests were conducted to verify the traditional PID, fuzzy PID, variable domain fuzzy PID and bat-optimized variable-domain fuzzy PID control algorithms. The results show that: if the water–fertilizer ratio is adjusted to 50:1 from the startup, the adjustment time required to reach the target water–fertilizer ratio under the bat-optimized variable-domain fuzzy PID control is 15.29 s, and the maximum overshooting amount is 16.28%, which is a smaller adjustment time and overshooting amount; if the water–fertilizer ratio is adjusted to 40:1 from 50:1, the advantages of bat-optimized variable-domain fuzzy PID are more obvious, with the best balance of response speed, overshooting amount and optimal control effect. In terms of response speed, overshooting amount and regulation time, the optimal balance is achieved, showing the optimal control effect. It is proved that the performance of the water–fertilizer ratio regulation system in cotton field under bat-optimized variable-domain fuzzy PID control designed in this paper can meet the actual production requirements, and these findings can help to develop precise irrigation technology for cotton cultivation under drip irrigation conditions.

A Reinforcement Learning-Based Multi-Objective Bat Algorithm Applied to Edge Computing Task-Offloading Decision Making

Amid the escalating complexity of networks, wireless intelligent devices, constrained by energy and resources, bear the increasing burden of managing various tasks. The decision of whether to allocate tasks to edge servers or handle them locally on devices now significantly impacts network performance. This study focuses on optimizing task-offloading decisions to balance network latency and energy consumption. An advanced learning-based multi-objective bat algorithm, MOBA-CV-SARSA, tailored to the constraints of wireless devices, presents a promising solution for edge computing task offloading. Developed in C++, MOBA-CV-SARSA demonstrates significant improvements over NSGA-RL-CV and QLPSO-CV, enhancing hypervolume and diversity-metric indicators by 0.9%, 15.07%, 4.72%, and 0.1%, respectively. Remarkably, MOBA-CV-SARSA effectively reduces network energy consumption within acceptable latency thresholds. Moreover, integrating an automatic switching mechanism enables MOBA-CV-SARSA to accelerate convergence speed while conserving 150.825 W of energy, resulting in a substantial 20.24% reduction in overall network energy consumption.

Advanced machine learning computations for estimation of hydrogen solubility in oil samples: Model comparisons and validation

For analyzing hydrogenation process for treatment of petroleum-based fuels, solubility of hydrogen in the feed should be well correlated. The main correlation factors are temperature and pressure which have a great effect on hydrogen solubility. This research paper presents the development of three models for predicting the solubility of hydrogen gas (H2) in diesel. Pressure and Temperature are the input parameters and solubility is single output. The models were fine-tuned using the Bat Algorithm (BA). The three models include Orthogonal Matching Pursuit Regression (OMP), K Nearest Neighbors Regression (KNN), and Tweedie Regression (TDR). The results of the study revealed that the OMP model achieved the highest level of accuracy, with an R2 score of 0.98, and the least RMSE and MAE error rates of 0.24 and 0.19, respectively. The KNN model also performed well with an R2 score of 0.92, an RMSE of 0.42, and an MAE of 0.37. The TDR model had the lowest accuracy compared to the other two models. These results imply that the OMP model is the most suitable one for predicting H2 solubility. The models can be used to enhance the efficiency of fuel production by providing accurate predictions of H2 solubility.

Adaptive Channel Equalization for Digital Communication with Tunicate Swarm Algorithm

In wireless communication systems, the information is transferred as digital symbols that get affected by noise interference while transmitting through the wireless channel. In today’s world, the increased demand for rapid data transmission rates leads to more inter-symbol interference in the received signal. To diminish the effects of inter-symbol interference, adaptive channel equalization is utilized in digital communication. In this paper, the inter-symbol interference effect in the finite impulse response channel is reduced in terms of an adaptive equalizer method. The weights/coefficients of the equalizer are optimized through a tunicate swarm algorithm. Channel equalization involves optimizing the channel coefficients to mitigate the impact of inter-symbol interference. This equalization process can be viewed as an iterative optimization task, aimed at lessening the mean square error among the transmitted signal and the equalizer’s output. The implementation of the proposed method is executed using MATLAB software. We assess its effectiveness through a comparative analysis, considering metrics such as mean square error, learning rate, bit error rate, convergence rate, and computational time. Furthermore, we conduct a comparative evaluation with other optimization approaches, including the Bat Algorithm, Slime Mould Algorithm, and Harris Hawks Optimization Algorithm. This comparison demonstrates the superiority of the proposed algorithm over traditional optimization techniques.

Optimal smart contracts for controlling the environment in electric vehicles based on an Internet of Things network

The scientific community has recently focused on intelligent models for predicting and optimizing EV energy management. Despite numerous studies in energy management optimization, there is a critical need to address the trade-off between energy consumption and occupant comfort. Existing IoT systems face challenges in data analytics security and authenticity, highlighting the need for contemporary models to overcome data privacy and cost-related issues. This study introduces a smart contract model based on optimization and control modules, aiming to manage energy consumption while satisfying user comfort requirements intelligently. Introducing a smart contract model with hierarchical layers—prediction, optimization, control, and Blockchain—the proposed approach intelligently manages energy consumption while meeting user comfort requirements. Utilizing a Kalman filter for prediction and the BAT algorithm for optimization, the model integrates modules to tailor user preferences and enhance comfort. The synergy between the optimization module and a convolutional FLC enhances system performance, ensuring minimized energy usage and elevated user comfort levels. The study also evaluates the model’s implementation of the Hyperledger Fabric network, assessing outcomes regarding caliper, latency, throughput, and resource utilization.

Network Information Security Monitoring Under Artificial Intelligence Environment

At present, network attack means emerge in endlessly. The detection technology of network attack must be constantly updated and developed. Based on this, the two stages of network attack detection (feature selection and traffic classification) are discussed. The improved bat algorithm (O-BA) and the improved random forest algorithm (O-RF) are proposed for optimization. Moreover, the NIS system is designed based on the Agent concept. Finally, the simulation experiment is carried out on the real data platform. The results showed that the detection precision, accuracy, recall, and F1 score of O-BA are significantly higher than those of references [17], [18], [19], and [20], while the false positive rate is the opposite (P < 0.05). The detection precision, accuracy, recall, and F1 score of O-RF algorithm are significantly higher than those of Apriori, ID3, SVM, NSA, and O-RF algorithm, while the false positive rate is significantly lower than that of Apriori, ID3, SVM, NSA, and O-RF algorithm (P < 0.05).

Suspended sediment load prediction using sparrow search algorithm-based support vector machine model

Prediction of suspended sediment load (SSL) in streams is significant in hydrological modeling and water resources engineering. Development of a consistent and accurate sediment prediction model is highly necessary due to its difficulty and complexity in practice because sediment transportation is vastly non-linear and is governed by several variables like rainfall, strength of flow, and sediment supply. Artificial intelligence (AI) approaches have become prevalent in water resource engineering to solve multifaceted problems like sediment load modelling. The present work proposes a robust model incorporating support vector machine with a novel sparrow search algorithm (SVM-SSA) to compute SSL in Tilga, Jenapur, Jaraikela and Gomlai stations in Brahmani river basin, Odisha State, India. Five different scenarios are considered for model development. Performance assessment of developed model is analyzed on basis of mean absolute error (MAE), root mean squared error (RMSE), determination coefficient (R2), and Nash–Sutcliffe efficiency (ENS). The outcomes of SVM-SSA model are compared with three hybrid models, namely SVM-BOA (Butterfly optimization algorithm), SVM-GOA (Grasshopper optimization algorithm), SVM-BA (Bat algorithm), and benchmark SVM model. The findings revealed that SVM-SSA model successfully estimates SSL with high accuracy for scenario V with sediment (3-month lag) and discharge (current time-step and 3-month lag) as input than other alternatives with RMSE = 15.5287, MAE = 15.3926, and ENS = 0.96481. The conventional SVM model performed the worst in SSL prediction. Findings of this investigation tend to claim suitability of employed approach to model SSL in rivers precisely and reliably. The prediction model guarantees the precision of the forecasted outcomes while significantly decreasing the computing time expenditure, and the precision satisfies the demands of realistic engineering applications.

Economic optimization of hybrid renewable energy resources for rural electrification

In rural areas, grid expansions and diesel generators are commonly used to provide electricity, but their high maintenance costs and CO2 emissions make renewable energy sources (RES) a more practical alternative. Traditional methods such as analytical, statistical, and numerical-based techniques are inadequate for designing an energy-efficient RES. Therefore, this study utilized the bat algorithm (BA) to optimize the use of hybrid RES for rural electrification. A feasibility study was conducted in the village of Kalema to assess energy consumption, and a diesel-only system was modeled to serve the entire community. The BA was used to determine the optimal size and cost-effectiveness of the hybrid RES, with MATLAB R (2021a) utilized for simulation. The BA's performance was compared with diesel only and GA using cost of energy (COE) and CO2 emissions as metrics. Diesel generators only produced a COE of $6,562,000 and 1679.6 lb/hr of CO2 emissions. COE with BA was $356,9781.37 (a 45.6% reduction) and CO2 emissions were 635.29 lb/hr (a 62.2% drop). Genetic algorithm (GA) resulted in $364,3122.46 COE and 652.69 lb/hr CO2 emissions, indicating 61.1% and 44.5% decreases, respectively. BA significantly reduced COE and CO2 emissions over GA, according to the analysis.

OPTIMIZING HEART ATTACK DIAGNOSIS USING RANDOM FOREST WITH BAT ALGORITHM AND GREEDY CROSSOVER TECHNIQUE

Cardiovascular disease stands as one of the primary contributors to global mortality, with the World Health Organization (WHO) reporting approximately 17.9 million deaths annually. Swift and accurate diagnosis of heart attacks is crucial to ensure timely and specialized intervention for patients afflicted by this ailment. A machine learning algorithm that can be employed for addressing such issues is the Random Forest algorithm. However, the efficacy of the model is significantly influenced by the features selected during the training phase. To mitigate this, the Binary Bat Algorithm (BBA) with greedy crossover has been utilized to enhance feature selection within the model. This approach is particularly adept at preventing convergence issues often associated with local minima. The optimal parameters for BBA with greedy crossover are determined to be , , , and . With these parameters, the proposed algorithm identifies the most relevant features, including age, gender, cp, chol, thalach, oldpeak, slope, and ca, achieving an accuracy of 94.19% on the training data and 91.8% on the test data. Furthermore, the precision and recall values for both classes range from 0.87 to 0.96, contributing to an approximate -score of 0.92. The proposed method has increased its -score by 0.05 if compared with the regular Random Forest model. These results underscore the effectiveness of the proposed algorithm in providing accurate and reliable predictions for heart disease diagnosis. As such, this model makes diagnosing heart attack more convenient and effective because it does not require too much medical features or patient data. Hopefully, the results of this research help medical practitioners make better and timely decisions in the diagnosis and treatment of heart attacks, as well as assist in planning more effective public health programs for heart attack prevention.

Research on Continuous Casting–Hot Rolling Scheduling Model Involving Reheating Furnace Conversion Mode and Improved Bat Optimization Solution Algorithm

As the main energy-consuming equipment, the reheating furnace plays an important role in the scheduling of continuous casting and hot rolling for a steel mill. This paper studies the energy consumption of cold and hot slab conversion mode in a reheating furnace, forming four conversion relationships, with the goal of minimizing the coupling function between the maximum completion time of the hot rolling and the energy waste of the reheating furnace, and modelling the scheduling of a continuous casting–hot rolling process. An improved ordered pair bat algorithm is used to solve for special process conditions of the proposed scheduling model. The effectiveness of the proposed model and algorithm is verified by solving the different slab charging sequence and conversion mode. Moreover, a comparative study of different types of batches is also conducted, and it is found that the model can save more than 5000 GJ per month for the steel mill, which can achieve the goal of effective energy saving and increasing enterprise profit.

Optimasi LFC (Load Frequency Control) Pada Mikrohidro Menggunakan Metode ACO-ANFIS dan BA-ANFIS

Micro-hydro Power Plant is a small-scale power plant. Micro-hydro plants are built with enough water potential to generate electrical energy. A common problem with micro-hydro generating systems is that the output of the generator is not constant. This is caused by changes in connected loads. Thus causing frequent fluctuations in the frequency and voltage of the system that can cause damage to electrical equipment. Because it is used Load Frequency Control (LFC) to control the frequency can be more stable. To obtain optimal control parameters on micro hydropower systems used by Artificial Intelligence (AI) is Adaptive Neuro-Fuzzy Inference System (ANFIS). ANFIS data is retrieved from training data of PID controllers tuned using Ant Colony Optimization (ACO) and Bat Algorithm (BA). This study compared uncontrolled methods, PID-ZN control methods, PID-ACO method, PID-BA, PID-ACO-ANFIS, and PID-BA-ANFIS obtained the best control method. The result of this research is the control method of PID-ACO-ANFIS is the best control method with overshoot 0.00 and the fastest settling time is 0.00. The results showed that the smallest overshoot (0) in the PID-ACO-ANFIS model, the smallest undershoots (1,12x10-5) in PID-ACO-ANFIS and the fastest settling time (3.77 seconds) in the starting also at PID-ACO-ANFIS. The results of this study will be tried bengan other methods, which results may be better

Enhancing the Efficiency of a Cybersecurity Operations Center Using Biomimetic Algorithms Empowered by Deep Q-Learning.

In the complex and dynamic landscape of cyber threats, organizations require sophisticated strategies for managing Cybersecurity Operations Centers and deploying Security Information and Event Management systems. Our study enhances these strategies by integrating the precision of well-known biomimetic optimization algorithms-namely Particle Swarm Optimization, the Bat Algorithm, the Gray Wolf Optimizer, and the Orca Predator Algorithm-with the adaptability of Deep Q-Learning, a reinforcement learning technique that leverages deep neural networks to teach algorithms optimal actions through trial and error in complex environments. This hybrid methodology targets the efficient allocation and deployment of network intrusion detection sensors while balancing cost-effectiveness with essential network security imperatives. Comprehensive computational tests show that versions enhanced with Deep Q-Learning significantly outperform their native counterparts, especially in complex infrastructures. These results highlight the efficacy of integrating metaheuristics with reinforcement learning to tackle complex optimization challenges, underscoring Deep Q-Learning's potential to boost cybersecurity measures in rapidly evolving threat environments.

Fast Linde-Buzo-Gray (FLBG) Algorithm for Image Compression through Rescaling Using Bilinear Interpolation.

Vector quantization (VQ) is a block coding method that is famous for its high compression ratio and simple encoder and decoder implementation. Linde-Buzo-Gray (LBG) is a renowned technique for VQ that uses a clustering-based approach for finding the optimum codebook. Numerous algorithms, such as Particle Swarm Optimization (PSO), the Cuckoo search algorithm (CS), bat algorithm, and firefly algorithm (FA), are used for codebook design. These algorithms are primarily focused on improving the image quality in terms of the PSNR and SSIM but use exhaustive searching to find the optimum codebook, which causes the computational time to be very high. In our study, our algorithm enhances LBG by minimizing the computational complexity by reducing the total number of comparisons among the codebook and training vectors using a match function. The input image is taken as a training vector at the encoder side, which is initialized with the random selection of the vectors from the input image. Rescaling using bilinear interpolation through the nearest neighborhood method is performed to reduce the comparison of the codebook with the training vector. The compressed image is first downsized by the encoder, which is then upscaled at the decoder side during decompression. Based on the results, it is demonstrated that the proposed method reduces the computational complexity by 50.2% compared to LBG and above 97% compared to the other LBG-based algorithms. Moreover, a 20% reduction in the memory size is also obtained, with no significant loss in the image quality compared to the LBG algorithm.

Nature-inspired approaches in Software Fault Prediction

In software engineering, predicting software faults is a crucial task for ensuring high software quality and reducing costs. In recent years, nature inspired approaches have been increasingly used in software fault prediction. In this paper, we explore the effectiveness of six nature inspired algorithms, namely Ant Colony, Particle Swarm Optimization, Firefly, Bat, Harris Hawks, and Genetic Algorithm, for software fault prediction. We evaluate the algorithms using three commonly used datasets, JM1, CM1, and PC1. Our experimental results show that nature inspired approaches can effectively predict software faults, with some algorithms performing better than others depending on the dataset used. Our findings suggest that these approaches have potential to be used as a practical and efficient means for software fault prediction. Keywords— nature inspired algorithms; PSO; Ant Colony Optimization; Harris Hawks; Genetic Algorithm (GA); python programming; Jupyter Notebook; confusion matrix;

Enhancing Buoy-Based Swinging Energy Harvesters through Bat Algorithm Optimization: A Comprehensive Study

Enhancing Buoy-Based Swinging Energy Harvesters through Bat Algorithm Optimization: A Comprehensive Study

Expanding the wrench feasible workspace of quadrotor-based mobile cable-driven parallel manipulators using multi-objective optimization and machine learning

Quadrotor-based Reconfigurable Cable-Driven Parallel Manipulators (QRCDPMs) have previously been introduced as having significant potential in extending the feasible workspace of Cable-Driven Parallel Manipulators (CDPMs). However, such configurations have limitations in executing positive Z-direction trajectories resulting in non-optimal workspace and performance. This study proposes a novel Quadrotor-based Enhanced Reconfiguration Cable-Driven Parallel Manipulator (QERCDPM) that significantly enhances the Wrench Feasible Workspace (WFW). This allows for extended safe platform operation along all XYZ directions by adjusting the bottom cable exit point. Simulation results indicate that the proposed setup achieves a 96.39 % ratio of safe workspace to available workspace in various cable exit points configurations. Tension Distribution analysis for different trajectories demonstrates the proposed setup's superior cable tension management, increased flexibility and reachability. A Random Forest based Particle Swarm Optimization - Modified Frequency Bat Algorithm (RFPSOMFB) optimization algorithm is proposed, which outperforms existing methods in terms of run time and solution quality. The findings hold significance for applications requiring safe and efficient trajectory execution in dynamic and constrained environments, contributing to the evolution of multi-robot based intelligent and distributed systems.