Bat Algorithm Research Articles

Leveraging Swarm Intelligence for Optimal Thermal Camera and Sensor Placement in Industrial Environments

This research addresses the sensor placement optimization problem (SPOP) in the industrial sector, aiming to enhance operational efficiency and safety through the strategic deployment of sensors. The focus is on optimizing the locations of thermal cameras and motion sensors, with a dual objective of maximizing coverage and minimizing redundancy in the production hall. To solve this challenge, a method based on the application of the nature-inspired bat algorithm was employed. The study reveals noteworthy findings, emphasizing the proficiency of the bat algorithm (BA) in optimizing the placement of thermal cameras and motion sensors. Numeric outcomes demonstrate the algorithm’s effectiveness in maximizing machine coverage while minimizing sensor usage within a real-world industrial environment. These results underscore the versatility and reliability of the BA, establishing it as a valuable tool for addressing complex optimization tasks in industrial settings.

No-Wait Open-Shop Scheduling Problems with Reverse Flows

Open shop scheduling has a special place in both production and optimization problems. This study investigates an assembly (direct flow) and disassembly (reverse flow) scheduling problem in an open shop environment where both are processed on the same machine, but only one is a no-wait operation. The objective is to minimize makespan of jobs on all machines. A mixed-integer linear program is developed. GAMS 25.1.1.2 is used to solve small size test problems. Differential Evolution (DE) Algorithm is applied to large-scale problems. A comparison of the computing results of this algorithm and the Genetic algorithm (GA), Whale Optimization Algorithm (WOA), Bat algorithm (BA), Artificial bee colony algorithm (ABC), and vibration damping optimization algorithm (VDO) is presented. Based on the results obtained, it has been found that there are no obvious differences between the performance of the algorithms, but the DE algorithm produces better solutions.

Performance Comparison Between Differential Evolution and Bat Algorithm in P.I.D Tuning for Optimization of Speed Control on Parallel Hybrid Electric Vehicle

In the last decade, there have been many means of transportation that use fuel oil (Internal Combustion Engine (ICE)). This has a serious impact on the environment due to the resulting pollutant gas emissions. One solution is the use of hybrid electric vehicles (HEV) as a replacement for vehicles that use ICE. One of the performances that a HEV must have is to have a stable speed when driving. In this research, several methods used in the metaheuristic algorithm in the disturbance observer have the advantage of describing the inverse model of the plant without creating a mathematical model. Testing was carried out by comparing two methods of metaheuristic algorithms, namely Differential Evolution and Bat Algirthm. The simulation results show that the method used on this HEV is to maintain its speed, so according to the test results it shows that the Differential Evolution method is the best method for controlling speed on a Parallel Hybrid Electic Vehicle.

A Novel Framework for Epileptic Seizure Detection Using Electroencephalogram Signals Based on the Bat Feature Selection Algorithm

The precise electroencephalogram (EEG) signal classification with the highest possible accuracy is a key goal in the brain-computer interface (BCI). Considering the complexity and nonstationary nature of the EEG signals, there is an urgent need for effective feature extraction and data mining techniques. Here, we introduce a novel pipeline based on Bat and genetic algorithms for feature construction and dimension reduction of EEG signals. After wavelet extraction and segmentation, the Bat algorithm identifies the most relevant features. We use these features and a genetic algorithm combined with a neural network method to automatically classify the segments of the epilepsy EEG signals. We also use available classification methods based on k-Nearest Neighbors or naïve Bayes for comparison purposes. The code distinguishes individual signals within various combinations of data obtained from healthy volunteers with open or closed eyes and patients suffering from epilepsy disorders during seizure-free periods or seizure activities. Compared to the previously introduced methods, our proposed framework demonstrates a superior balance of high accuracy and short runtime. The minimum achieved accuracies for balanced and unbalanced classes are 100% and 75.9%, respectively. This approach has the potential for direct applications in clinics, enabling accurate and rapid analysis of the epilepsy EEG signals obtained from patients.

Optimization of β-cyclodextrin based ultrasonic-assisted extraction of apigenin and luteolin from celery seed using support vector regression and different nature-inspired optimization algorithms

This study aimed to extract apigenin and luteolin from celery seeds using 2-hydroxypropyl-β-cyclodextrin (HP-β-CD) based ultrasonic-assisted technique. Optimal extraction conditions were determined through the integration of support vector regression combined with various optimization algorithms, including Genetic Algorithm, Particle Swarm Optimization, Gray Wolf Optimizer, Bat Algorithm, Firefly Algorithm, and Cuckoo Search Algorithm. The identified optimal conditions included an extraction time of 52 min, an HP-β-CD concentration of 24 mg/mL, and a solvent-to-solid ratio of 24 mL/g. Under these conditions, the predicted apigenin and luteolin content in the extract were 0.156 and 0.814 mg/g, respectively, closely aligning with the actual values of 0.158 ± 0.005 mg/g and 0.807 ± 0.008 mg/g. Notably, the HP-β-CD/water extract exhibited significantly higher concentrations of active ingredients and antioxidant activity compared to the water extract, with the content of apigenin and luteolin in the water extract being only 25 and 45%, respectively, compared to the HP-β-CD/water extract. Regarding the antioxidant effect, the water extract displayed a 50% scavenging capacity (SC50) of 331.29 ± 20.5 g/mL, whereas the SC50 for the HP-β-CD/water extract was 89.04 ± 4.70 g/mL. Molecular modeling and IR spectra analysis also suggested the formation of inclusion complexes between HP-β-CD and apigenin/luteolin. These findings suggested that HP-β-CD could be a preferable alternative to conventional solvents due to its advantages, such as easier control, safety, and cost-effectiveness. In addition, the use of support vector regression and nature-inspired optimization algorithms could be promising alternatives to traditional response surface methodology.

A binary bat algorithm with improved crossover operators and Cauchy mutation for unit commitment problem

A binary bat algorithm with improved crossover operators and Cauchy mutation for unit commitment problem

Assessment of Landslide Susceptibility Using the PCA and ANFIS with Various Metaheuristic Algorithms

It is very important for the susceptibility assessment and disaster prediction of the region to effectively evaluate the landslide susceptibility. In this study, Particle Swarm Optimization (PSO), Artificial Bee Colony algorithm (ABC), Shuffled Frog Leaping Algorithm (SFLA) and Bat algorithm (BAT) are used to optimize Adaptive Neuro-Fuzzy Inference System (ANFIS) to evaluate the landslide susceptibility. 811 sample points were collected through remote sensing analysis and field investigation for susceptibility analysis. Fifteen landslide evaluation factors were quantified and normalized, and the Principal Component Analysis (PCA) method was used to compress them into 6 main factors. The accuracy analysis results of the area under the curve (AUC), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) evaluation models show that the AUC values of PSO, ABC, SFLA and BAT are 93.6%, 96.2%, 90.8% and 86.1%, respectively. Among them, the accuracy of ABC is the highest. This study effectively evaluates the landslide susceptibility through a new neural network hybrid method, which provides a theoretical basis for landslide disaster susceptibility management.

Optimal allocation method of shared energy storage in multienergy coupled microgrid with carbon emission constraint considering dual uncertainty of source and load

Abstract In order to realize the stable operation of the multienergy coupled microgrid under the low-carbon constraint, a carbon emission constrained multienergy coupled microgrid shared energy storage optimization configuration method considering the dual uncertainty of source and load is proposed. This method constructs a source load double uncertainty and carbon emission model, and it analyzes the output characteristics and load demand characteristics of multiple coupled energy sources and the carbon emission results under these characteristics. According to the analysis results, the multienergy coupling microgrid shared energy storage optimization configuration model is constructed, and after the optimization configuration objective function and constraint conditions are determined, the bat algorithm is used to solve it, and the microgrid shared energy storage optimization configuration scheme that meets the objective function and constraint conditions is obtained. The test results show that after the method is applied, the utilization rate of multi-energy sources reaches 90.6%, and the expected carbon emissions are lower than 2.04 × 104kg. The microgrid fluctuation suppression rate reached 92.7%, and the net load fluctuation range was significantly reduced. Thus, it ensures reliable power supply of the microgrid and has good applicability.

Secured VM Deployment in the Cloud: Benchmarking the Enhanced Simulation Model

Cloud computing has gained widespread recognition for facilitating myriad online services and applications. However, the current stages of commercial cloud computing employ a moderate design, wherein computational resources like storage and servers are housed in a few sizable worldwide data centers. System reliability, efficiency, and low latency are all goals of virtual machine (VM) placement. Load balancing has emerged as a crucial challenge for attaining energy efficiency in a fictitious grid computing architecture where a variety of users’ workloads are distributed across several virtual machines. We propose a more effective optimization technique known as the twin fold moth flame algorithm. This algorithm considers multiple constraints, including computation time, stability, and placement cost. The proposed model’s effectiveness will be evaluated based on relocation costs, reaction times, and stability assessments. The most significant gains of the presented work are 4.24%, 9.73%, 11.10%, 28.83%, 7.63%, and 10.62% for 20 count data of nodes for artificial bee colony–bat algorithm, ant colony optimization, crow search algorithm, krill herd, whale optimization genetic algorithm, and improved Lévy-based whale optimization algorithm, respectively.

Performance investigation and element optimization of 2D array transducer using Bat Algorithm

Performance investigation and element optimization of 2D array transducer using Bat Algorithm

Leveraging Bat Algorithm with Rough Neutrosophic Soft Set for Enhanced Oral Cancer Detection and Classification

Neutrosophic soft sets (NSS) are highly effective in representing neutral uncertain data. NSS model attracts several authors because it has huge range of applications in several areas such as decision-making, data analysis, smoothness of functions, probability theory, measurement theory, predicting, and operations research. Oral squamous cell carcinoma (OSCC) is the most general tumor around the world and its occurrence is on the increase in several populations. Early diagnosis plays vital role in improving diagnosis, treatment outcomes and survival rates. Although the new developments in understanding molecular mechanisms, late analysis and the implementation of precision medicine for OSCC patients continue to present problems. Early diagnosis and detection can support doctors in offering optimum patient care and effectual treatment. In recent years, the execution of several machine-learning (ML) approaches in cancer analysis has provided valuable insights, facilitating more effective and precise treatment decision-making. Oral Cancer screening can progress with the execution of artificial intelligence (AI) approaches. AI offers support to the oncology region by correctly examining a huge database in many imaging modalities. This article develops a Bat Algorithm with Rough Neutrosophic Soft Set for Oral Cancer Diagnosis (BARNSS-OCD) technique. The main intention of the BARNSS-OCD technique is to exploit deep learning (DL) model for enhanced identification of OC. In the BARNSS-OCD technique, median filtering (MF) is used for image pre-processing and the feature extraction takes place using deep convolutional neural network (DCNN) model. In addition, bat algorithm (BA) is used for the hyperparameter selection of the DCNN model. For OC detection process, the BARNSS-OCD technique applies RNSS model. To exhibit the improved performance of the BARNSS-OCD technique, a sequence of experiments is involved. The simulation outcomes indicate that the BARNSS-OCD technique gains better performance compared to other DL models

Bat algorithm based semi‐distributed resource allocation in ultra‐dense networks

AbstractThis paper addresses the resource allocation (RA) for ultra‐dense network (UDN), where base stations (BSs) are densely deployed to meet the demands of future wireless communications. However, the design of RA in UDN is challenging, as the RA problem is non‐convex and NP‐hard. Therefore, this paper considers and studies a semi‐distributed resource block (RB) allocation scheme, in order to achieve a well‐balanced trade‐off between performance and complexity. In the context of semi‐distributed RB allocation scheme, the problem can be decomposed into the subproblem of clustering and the subproblem of cluster‐based RB allocation. We first improve the K‐means clustering algorithm by employing the Gaussian modified method, which can significantly decrease the number of iterations for carrying out the K‐means algorithm as well as the failure possibility of clustering. Then, bat algorithm (BA) is introduced to attack the problem of cluster‐based RB allocation. In order to make the original BA applicable to the problem of RB allocation, chaotic sequences are adopted to discretize the initial position of the bats, and simultaneously increase the population diversity of the bats. Furthermore, in order to speed up the convergence of BA, the logarithmic decreasing inertia weight is employed for improving the original BA. Our studies and performance results show that the proposed approaches are capable of achieving a desirable trade‐off between the performance and the implementation complexity.

Adaptive PI control of temperature with natural ventilation in greenhouses using a bat algorithm variant

This paper presents a self-tuning PI controller to control the temperature of a greenhouse using natural ventilation. The PI controller parameters are adapted according to the changing dynamics of the process, identified with a simplified greenhouse temperature model based on first principles. The time-varying model parameters are estimated online using the random scaling-based bat algorithm. The model is linearized to obtain a first-order transfer function which facilitates the design of the PI controller using well-known tuning methods. Simulated results with real greenhouse data show that the proposed solution could be applied to keep controllers tuned throughout different agri-seasons.

A Low‐Frequency Oscillation Identification Method for Power System Based on Adaptive Generalized S‐Transform with Bat Algorithm

A Low‐Frequency Oscillation Identification Method for Power System Based on Adaptive Generalized S‐Transform with Bat Algorithm

Hybrid Bat and Salp Swarm Algorithm for Feature Selection and Classification of Crisis-Related Tweets in Social Networks

Hybrid Bat and Salp Swarm Algorithm for Feature Selection and Classification of Crisis-Related Tweets in Social Networks

Suppression of Peak Sidelobe Level in Linear Symmetric Antenna Arrays Using Hybrid Grey Wolf and Improved Bat Algorithm

Suppression of Peak Sidelobe Level in Linear Symmetric Antenna Arrays Using Hybrid Grey Wolf and Improved Bat Algorithm

A discrete bat algorithm for collaborative scheduling of discrete manufacturing logistics

A discrete bat algorithm for collaborative scheduling of discrete manufacturing logistics

A discrete bat algorithm for collaborative scheduling of discrete manufacturing logistics

A discrete bat algorithm for collaborative scheduling of discrete manufacturing logistics

Particle Swarm Optimization Bat Algorithm Path Automatically Planning Research for Police Drones in Hilly Cities

Particle Swarm Optimization Bat Algorithm Path Automatically Planning Research for Police Drones in Hilly Cities

A Precise Framework for Rice Leaf Disease Image-Text Retrieval Using FHTW-Net.

Cross-modal retrieval for rice leaf diseases is crucial for prevention, providing agricultural experts with data-driven decision support to address disease threats and safeguard rice production. To overcome the limitations of current crop leaf disease retrieval frameworks, we focused on four common rice leaf diseases and established the first cross-modal rice leaf disease retrieval dataset (CRLDRD). We introduced cross-modal retrieval to the domain of rice leaf disease retrieval and introduced FHTW-Net, a framework for rice leaf disease image-text retrieval. To address the challenge of matching diverse image categories with complex text descriptions during the retrieval process, we initially employed ViT and BERT to extract fine-grained image and text feature sequences enriched with contextual information. Subsequently, two-way mixed self-attention (TMS) was introduced to enhance both image and text feature sequences, with the aim of uncovering important semantic information in both modalities. Then, we developed false-negative elimination-hard negative mining (FNE-HNM) strategy to facilitate in-depth exploration of semantic connections between different modalities. This strategy aids in selecting challenging negative samples for elimination to constrain the model within the triplet loss function. Finally, we introduced warm-up bat algorithm (WBA) for learning rate optimization, which improves the model's convergence speed and accuracy. Experimental results demonstrated that FHTW-Net outperforms state-of-the-art models. In image-to-text retrieval, it achieved R@1, R@5, and R@10 accuracies of 83.5%, 92%, and 94%, respectively, while in text-to-image retrieval, it achieved accuracies of 82.5%, 98%, and 98.5%, respectively. FHTW-Net offers advanced technical support and algorithmic guidance for cross-modal retrieval of rice leaf diseases.