Enhanced Bat Algorithm Research Articles

An Optimized PID Controller Using Enhanced Bat Algorithm in Drilling Processes

An Optimized PID Controller Using Enhanced Bat Algorithm in Drilling Processes

Optimizing prediction accuracy for early recurrent lumbar disc herniation with a directional mutation-guided SVM model

Percutaneous endoscopic lumbar discectomy (PELD) is one of the main means of minimally invasive spinal surgery, and is an effective means of treating lumbar disc herniation, but its early recurrence is still difficult to predict. With the development of machine learning technology, the auxiliary model based on the prediction of early recurrent lumbar disc herniation (rLDH) and the identification of causative risk factors have become urgent problems in current research. However, the screening ability of current models for key factors affecting the prediction of rLDH, as well as their predictive ability, needs to be improved. Therefore, this paper presents a classification model that utilizes wrapper feature selection, developed through the integration of an enhanced bat algorithm (BDGBA) and support vector machine (SVM). Among them, BDGBA increases the population diversity and improves the population quality by introducing directional mutation strategy and guidance-based strategy, which in turn allows the model to secure better subsets of features. Furthermore, SVM serves as the classifier for the wrapper feature selection method, with its classification prediction results acting as a fitness function for the feature subset. In the proposed prediction method, BDGBA is used as an optimizer for feature subset filtering and as an objective function for feature subset evaluation based on the classification results of the support vector machine, which improves the interpretability and prediction accuracy of the model. In order to verify the performance of the proposed method, this paper proves the performance of the model through global optimization experiments and prediction experiments on real data sets. The accuracy of the proposed rLDH prediction model is 93.49% and sensitivity is 88.33%. The experimental results show that Level of herniated disk, Modic change, Disk height, Disk length, and Disk width are the key factors for predicting rLDH, and the proposed method is an effective auxiliary diagnosis method.

Intelligent Meta-Heuristic-Based Optimization of Traffic Light Timing Using Artificial Intelligence Techniques

This research examines worldwide concerns over traffic congestion, encompassing aspects such as security, parking, pollution, and congestion. It specifically emphasizes the importance of implementing appropriate traffic light timing as a means to mitigate these issues. The research utilized a dataset from Montreal and partitioned the simulated area into various zones in order to determine congestion levels for each individual zone. A range of prediction algorithms has been employed, such as Long Short-Term Memory (LSTM), Decision Tree (DT), Recurrent Neural Network (RNN), Auto-Regressive Integrated Moving Average (ARIMA), and Seasonal Auto-Regressive Integrated Moving Average (SARIMA), to predict congestion levels at each traffic light. This information was used in a mathematical formulation to minimize the average waiting time for vehicles inside the road network. Many meta-heuristics were analyzed and compared, with the introduction of an Enhanced Bat Algorithm (EBAT) suggested for addressing the traffic signal optimization problem. Three distinct scenarios are described: fixed (with a constant green timing of 40 s), dynamic (where the timing changes in real-time based on the current level of congestion), and adaptive (which involves predicting congestion ahead of time). The scenarios are studied with low and high congestion scenarios in the road network. The Enhanced Bat Algorithm (EBAT) is introduced as a solution to optimize traffic signal timing. It enhances the original Bat algorithm by incorporating adaptive parameter tuning and guided exploration techniques that are informed by predicted congestion levels. The EBAT algorithm provides a more effective treatment for congestion problems by decreasing travel time, enhancing vehicle throughput, and minimizing pollutant emissions.

Machine learning prediction model based on enhanced bat algorithm and support vector machine for slow employment prediction.

The employment of college students is an important issue that affects national development and social stability. In recent years, the increase in the number of graduates, the pressure of employment, and the epidemic have made the phenomenon of 'slow employment' increasingly prominent, becoming an urgent problem to be solved. Data mining and machine learning methods are used to analyze and predict the employment prospects for graduates and provide effective employment guidance and services for universities, governments, and graduates. It is a feasible solution to alleviate the problem of 'slow employment' of graduates. Therefore, this study proposed a feature selection prediction model (bGEBA-SVM) based on an improved bat algorithm and support vector machine by extracting 1694 college graduates from 2022 classes in Zhejiang Province. To improve the search efficiency and accuracy of the optimal feature subset, this paper proposed an enhanced bat algorithm based on the Gaussian distribution-based and elimination strategies for optimizing the feature set. The training data were input to the support vector machine for prediction. The proposed method is experimented by comparing it with peers, well-known machine learning models on the IEEE CEC2017 benchmark functions, public datasets, and graduate employment prediction dataset. The experimental results show that bGEBA-SVM can obtain higher prediction Accuracy, which can reach 93.86%. In addition, further education, student leader experience, family situation, career planning, and employment structure are more relevant characteristics that affect employment outcomes. In summary, bGEBA-SVM can be regarded as an employment prediction model with strong performance and high interpretability.

Retraction Note: Enhanced bat algorithm for COVID-19 short-term forecasting using optimized LSTM.

[This retracts the article DOI: 10.1007/s00500-021-06075-8.].

A Hybridized Bat Algorithm for effective Task

The recently developed distributed framework Cloud Computing (CC) offers versatile and dynamically scalable low-cost computing services. In CC environment Task Scheduling (TS) is the key issues to be tackled to boost system performance and increase cloud satisfaction for customers. Advanced programming techniques should be introduced to ensure an efficient cloud mapping of tasks to fulfil the complex necessities of end-users applications. Having regard to a timing problem, unrelated MTs are responsible to perform individual tasks, is the key motivation behind this study. There are many subtasks to a mission. Only after completion of its predecessor subtask can a successor subtask be started. The subtasks may be carried out in the same machine or in another machine independently. It is known in advance how long each machine is run. Due to the continuous processing of other work, each computer has known release time (i.e., the time available to perform current tasks). We also considered two variants of problems for two separate objective functions in this research paper. The first version of the problem takes note of minimising the overall finishing time target while the second version considers minimising the Making-Time goal. In this paper we proposed an algorithm for hybridised bat optimization for this multi-target TS. Unlike current algorithms, an optimum solution can be sought by using fewer iterations. We have compared to determine the performance of this enhanced Bat Algorithm with the existing systems. The experiment conclude that the proposed procedureacts well in comparison with other TS algorithms.

Cyber Intrusion Detection System Based on a Multiobjective Binary Bat Algorithm for Feature Selection and Enhanced Bat Algorithm for Parameter Optimization in Neural Networks

The staggering development of cyber threats has propelled experts, professionals and specialists in the field of security into the development of more dependable protection systems, including effective intrusion detection system (IDS) mechanisms which are equipped for boosting accurately detected threats and limiting erroneously detected threats simultaneously. Nonetheless, the proficiency of the IDS framework depends essentially on extracted features from network traffic and an effective classifier of the traffic into abnormal or normal traffic. The prime impetus of this study is to increase the performance of the IDS on networks by building a two-phase framework to reinforce and subsequently enhance detection rate and diminish the rate of false alarm. The initial stage utilizes the developed algorithm of a proficient wrapper-approach-based feature selection which is created on a multi-objective BAT algorithm (MOBBAT). The subsequent stage utilizes the features obtained from the initial stage to categorize the traffic based on the newly upgraded BAT algorithm (EBAT) for training multilayer perceptron (EBATMLP), to improve the IDS performance. The resulting methodology is known as the (MOB-EBATMLP). The efficiency of our proposition has been assessed by utilizing the mainstream benchmarked datasets: NLS-KDD, ISCX2012, UNSW-NB15, KDD CUP 1999, and CICIDS2017 which are established as standard datasets for evaluating IDS. The outcome of our experimental analysis demonstrates a noteworthy advancement in network IDS above other techniques.

Enhanced bat algorithm for COVID-19 short-term forecasting using optimized LSTM.

The highly infectious COVID-19 critically affected the world that has stuck millions of citizens in their homes to avoid possible spreading of the disease. Researchers in different fields are continually working to develop vaccines and prevention strategies. However, an accurate forecast of the outbreak can help control the pandemic until a vaccine is available. Several machine learning and deep learning-based approaches are available to forecast the confirmed cases, but they lack the optimized temporal component and nonlinearity. To enhance the current forecasting frameworks’ capability, we proposed optimized long short-term memory networks (LSTM) to forecast COVID-19 cases and reduce mean absolute error. For the optimization of LSTM, we applied bat algorithm. Furthermore, to tackle the premature convergence and local minima problem of BA, we proposed an enhanced variant of BA. The proposed version utilized Gaussian adaptive inertia weight to control the individual velocity in the entire swarm. In addition, we substitute random walk with the Gaussian walk to observe the local search mechanism. The proposed LSTM examines the personal best solution with the swarm’s local best and preserves the optimal solution by combining the Gaussian walk. To evaluate the optimized LSTM, we compared it with the non-optimal version of LSTM, recurrent neural network, gated recurrent units, and other recent state-of-the-art algorithms. The experimental results prove the superiority of the optimized LSTM over other recent algorithms by obtaining 99.52 % accuracy.

Estimating and developing the dynamic parameters of two area power system using enhanced BAT algorithm

Estimating and developing the dynamic parameters of two area power system using enhanced BAT algorithm

Estimating and developing the dynamic parameters of two area power system using enhanced BAT algorithm

In this paper, a hybrid technique is developed with the combination of BAT algorithm and neural network (NN) to exploit the stability of the reduced two area power system model and estimated the generator reactance and inertias in the transfer path. Initially, the dynamic performance of the comprehensive machine for each area containing the voltage and current parameters at the set points on the transfer path is estimated. Here, the TCSC device is used to control the dynamic parameters and utilised based on the bus voltage phasor data at several buses including the voltage control bus and the line currents on the power transmission line. Based on the performance estimation, the three-phase fault signal is applied and estimated the performance variance in the system. The proposed technique is implemented in MATLAB/Simulink working platform. The performance of the proposed method is compared with the parameters based on the TCSC connected existing techniques like GA, ABC, PSO and CS.

A new approach for intrusion detection system based on training multilayer perceptron by using enhanced Bat algorithm

The most pressing issue in network security is the establishment of an approach that is capable of detecting violations in computer systems and networks. There have been several efforts for improving it from various points of view. One example is the improvement of the classification of packets on the network, which is imperative in detecting abnormal traffic and hence any potential intrusion. Thus, this study proposes a new approach for intrusion detection that is implemented using an enhanced Bat algorithm (EBat) for training an artificial neural network. The goal of the current study is to increase the accuracy of the classification for malicious and un-malicious network traffic. The proposed study herein includes a comparison with nine other metaheuristic algorithms (conventional and new algorithms) that are used to evaluate the new approach alongside the related works. Firstly, the EBat algorithm was developed and used to select suitable weights and biases. Next, the neural network was employed using the found optimal weights and biases to realize the intrusion detection approach. Four types of intrusion detection evaluation datasets were used to compare the proposed approach against the other algorithms. The findings revealed that the proposed method outperformed the other nine classification algorithms and it is unparalleled for the network intrusion detection.

Chaos-enhanced synchronized bat optimizer

In this paper, a chaos-enhanced bat algorithm is proposed to tackle the global optimization problems. Bat algorithm is a relatively new stochastic optimizer inspired by the echolocation behavior of bats in nature. Due to its effectiveness, it has been applied to many fields such as engineering design, feature selection, and machine learning. However, the classical approach is often prone to falling into local optima. This paper proposes an enhanced bat algorithm to alleviate this problem observed in the original algorithm. The proposed method controls the steps of chaotic mapping by a threshold and synchronizes the velocity of agents using a velocity inertia weight. These mechanisms are designed to boost the stability and convergence speed of the bat algorithm, instantly. Eighteen well-established and the state-of-the-art meta-heuristic approaches are considered to validate the effectiveness of the developed algorithm. Experimental results reveal that the proposed chaos-enhanced bat algorithm is not only superior to the well-established algorithms such as the original method but also the latest improved approaches. Also, the proposed method is successfully applied to I-beam design problems, welded beam design, and pressure vessel design. The results show that chaos-enhanced bat algorithm can deal with unconstrained and constrained feature spaces, effectively.

Optimal design of damage tolerant composite using ply angle dispersion and enhanced bat algorithm

In this work, minimum weight optimization of laminated composite is performed using a newly developed enhanced bat algorithm (EBA). Bat algorithm (BA) is a recently developed swarm-based optimization technique which is inspired by the echolocation behavior of bats. The standard BA shows premature convergence and reduced convergence speeds under some conditions. So, the EBA is used to perform the design optimization of laminated composites. The laminate analysis based on classical laminate theory is utilized for the stress calculations. Tsai–Wu failure curve is considered as the constraint in this constrained optimization problem. Number of plies at each orientation angle are considered as the design variables. The design optimization has been carried out for both conventional and unconventional (dispersed plies) stacking sequences considering different loading configurations: uniaxial tension, biaxial tension with and without shear loadings. Ply angles dispersed in the range of $$5^{\circ }$$–$$85^{\circ }$$, $$25^{\circ }$$–$$65^{\circ }$$ and $$0^{\circ }$$–$$90^{\circ }$$ at intervals of $$5^{\circ }$$ are considered for the unconventional stacking sequence to increase damage tolerance. In addition, a new mathematical function is proposed to measure the dispersion of ply angles in the laminate called the dispersion function. Also, the performance of EBA is compared with standard BA in the optimum weight design of composite laminates.

A hybrid enhanced bat algorithm for the generalized redundancy allocation problem

A majority of existing works dealing with redundancy allocation problems are based on traditional series-parallel structures. While in many real-life scenarios, the way of connecting subsystems is not limited to a series-only configuration. This paper considers a generalized redundancy allocation problem (GRAP), where the system structure is a more general network. Since the reliability evaluation in GRAPs is a NP-hard problem and the traditional exact symbolic reliability calculation is not suitable, a cellular automata based monte carlo simulation method is implemented in this paper to estimate the system reliability. It is a relatively simple but effective method without knowing the MPs/MCs. Moreover, to deal with GRAPs, a novel discrete bat algorithm is proposed in this paper with a goal of determining an optimal system structure that achieves the minimum cost under several constraints by using redundant components in parallel. Computational complexity of the proposed algorithm is also calculated in this paper. In the end, three experiments are carried out based on ten networks to set parameters, measure the effectiveness of the modifications, and compare with other state-of-the-art algorithms, separately. The reported computational results show that the proposed algorithm is powerful, which is more superior on this sort of problems.

ISVS3CE: Incremental Support Vector Semi-Supervised Subspace Clustering Ensemble and ENhanced Bat Algorithm (ENBA) for High Dimensional Data Clustering

In the recent work, Incremental Soft Subspace Based Semi-Supervised Ensemble Clustering (IS4EC) framework was proposed which helps in detecting clusters in the dataset. IS4EC framework also increases the results of clustering by reducing the intra-cluster distance and increasing the inter-cluster distance with increased cluster quality. It cannot attain acceptable results while handling high dimensional data. However, decreasing the dimensional subspace becomes extremely difficult issue. In IS4EC framework, to choose the optimal ensemble members also extremely becomes challenging issue. In order to solve these issues of traditional cluster ensemble methods, first propose an Incremental Support vector Semi-Supervised Subspace Clustering Ensemble (ISVS3CE) framework which makes utilized of benefits of the random subspace algorithm and the Constraint Propagation (CP) algorithm. Here the centroid values were selected by using the Support Vector Machine (SVM) classifier. In the ISVS3CE framework, Incremental Ensemble Member Chosen (IEMC) process is performed by using the ENhanced Bat Algorithm (ENBA), and the normalized cut algorithm is introduced to perform high dimensional data clustering. The ISVS3CE framework is successful for solving high dimensional data issue, at the same time as the CP algorithm is valuable for incorporating the prior information. Results demonstrate that the proposed ISVS3CE framework performs well on datasets by means of very high dimensionality, and better than the traditional clustering ensemble methods.

Enhanced Bat Algorithm for Analyzing the Reliability and Contingency Issue in Radial Distribution Power System

The paper proposes the hybrid technique to observe the contingency and improve the reliability of radial distribution system (RDS). The hybrid algorithm used is interface of Bat algorithm and particle swarm optimization that is used to examine the contingency and reliability indices in the RDS. The feeder power loss, system node voltage deviation, system average interruption frequency index, system average interruption unavailability index and the energy not supplied at every load position in a sharing scheme are used to resolve the contingencies to minimize the power quality and reliability indices. The proposed techniques are implemented on MATLAB/Simulink platform and experienced on the IEEE 33 bus RDS. To establish the effectiveness of the proposed algorithm, results are compared with the reported Bat and Firefly Algorithm.

A three-stage damage detection method for large-scale space structures using forward substructuring approach and enhanced bat optimization algorithm

In this study, an efficient three-stage method is proposed for damage detection of large-scale space structures by employing forward substructuring approach, modal strain energy and enhanced bat algorithm (EBA) optimization. EBA is a modified version of BA that is proposed in this paper and used a passive congregation operator to improve the performance of standard BA. In the first stage, the global structure is divided into manageable substructures. The stiffness matrices of independent substructures are obtained based on Kron’s substructuring method. Then a modal strain energy-based index is employed to precisely locate the eventual damages of the structure. In the third stage, damage severities are estimated via EBA using the second-stage results. To demonstrate the ability of the proposed method for detection of multiple structural damages, large-scale space structures with different types of damage scenarios are considered. The results show that the proposed method can detect the exact locations and severity of damages highly accurate in space structures.

Enhanced Bat Algorithm with Super-Region Volume Segmentation Workbench for Markov Random Field Images

Enhanced Bat Algorithm with Super-Region Volume Segmentation Workbench for Markov Random Field Images

Iterative Conditional Entropy Kalman filter (ICEKF) for noise reduction and Neuro Optimized Emotional Classifier (NOEC)

Emotion has a most important aspect in terms of interactions among the humans and this would become ideal for human emotions to get mechanically identified by the machines and primarily for enhancing the communication among the human–machine. In the recent work Enhanced Bat algorithm with Simulated Annealing (EBSA) are introduced for solving emotion recognition problem. Here the removal of noises from the speech samples and reduction in the number of speech features becomes very difficult task which reduces the accuracy of the classifier. To solve this problem this research work involves detection of emotions from speech which stimulates machines understanding human behavioral tasks namely reasoning, decision making and interaction. EBSA is used in the previous system to identify the happy, sad and neutral emotions from speech input. The performance of the previous system has been decreased due to recognition accuracy and feature selection. Improved Artificial Bee Colony (IABC) with Neuro Optimized Emotional Classifier (NOEC) solves this issue in the proposed system. The Iterative Conditional Entropy Kalman filtering (ICEKF) is initially processed to effectively filter the noisy features from the inputted speech data. Mel Frequency Cepstrum Coefficient (MFCC), pitch, energy, intensity and formants are extracted as speech features. Every extracted feature is maintained in the database and annotated along with their emotional class label. IABC algorithm chooses the feature optimally, which in turn employs the best fitness function values. From the optimally selected dataset, the NOEC is processed. Emotions can be identified from the Tamil news speech dataset with the help of the supervised machine learning technique, which demands the training set (collection of emotional speech recordings). Every recording or sample in the dataset is named with the emotional class and they are indicated as n-dimensional vector of spectrum coefficients which in turn is extracted from the Tamil news speech dataset. This dataset is collected from real time via using the search engine sites like Google, YouTube, twitter etc. By implementing IABC with NOEC classification process, the work segregates the emotional classes such as happy, sad, anger, fear and neutral emotions perfectly. From the experimental verification, it is confirmed that the proposed method IABC with NOEC gives better performances with respect to accuracy, precision, recall and f-measure values.

A new hybrid strategy for data clustering using cuckoo search based on Mantegna levy distribution, PSO and k-means

Data clustering is one of the data mining techniques which is widely used in some applications, such as pattern recognition, machine learning, image processing, etc. Swarm intelligence-based algorithms are extensively used in data clustering in recent years. Cuckoo Search (CS) is one of the recently proposed algorithms in the category of swarm intelligence-based techniques. In this paper, a new hybrid algorithm which utilises CS, Particle Swarm Optimisation (PSO) and k-means has been proposed (HCSPSO). The proposed algorithm employed PSO and k-means to produce new nests in standard CS to obtain better results. It also benefits from Mantegna levy distribution to obtain higher convergence speed as well as local search. To eliminate the problem of the high number of functional evaluations in standard CS, a fraction of nests has assigned to every section of the algorithm. The proposed algorithm's performance was evaluated by ten standard benchmark datasets. Evaluation results show that the proposed algorithm is an efficient method for data clustering and produces more optimised results in comparison with standard CS, PSO, Elephant Search Algorithm (ESA), Enhanced Bat Algorithm (EBA), Bird Flock Gravitational Search Algorithm (BFGSA), Improved Cuckoo Search (ICS) and k-means.