Hybrid Bat Algorithm Research Articles

A novel rate of penetration prediction model with identified condition for the complex geological drilling process

The accurate prediction of rate of penetration (ROP) has a crucial role in improving efficiency and minimizing cost in geological drilling process. Considering the drilling characteristics of strong nonlinearity, complexity, multiple variables and drilling conditions in drilling process, an online hybrid prediction model based on the drilling data is developed to achieve high accuracy prediction of the ROP. First, mutual information analysis is used to determine the appropriate model inputs. Then,k-nearest neighbor algorithm and dynamic time warping (KNN–DTW) are combined to identify drilling condition. After that, ROP prediction model is established by support vector regression (SVR) method. The hyperparameters of SVR method are obtained by hybrid bat algorithm (HBA) and nondominated sorting genetic algorithm II (NSGA-II) based on the identified drilling condition. Finally, a modified sliding window method is developed to update the prediction model to deal with complex and variable drilling process. The simulation results show that our method has higher accuracy than other methods, and our method can identify the drilling condition and provide guidance for the drilling operation.

A Novel Hybrid Approach Based on BAT Algorithm with Artificial Neural Network to Forecast Iran’s Oil Consumption

In this paper, we develop a function of population, GDP, import, and export by applying a hybrid bat algorithm (BAT) and artificial neural network (ANN). We apply these methods to forecast oil consumption in Iran. For this purpose, an improved artificial neural network (ANN) structure, which is optimized by the BAT is proposed. The variables between 1980 and 2017 were used, partly for installing and testing the method. This method would be helpful in forecasting oil consumption and would provide a level playing field for checking the energy policy authority impacts on the structure of the energy sector in an economy such as Iran with high economic interventionism by the government. The result of the model shows that the findings are in close agreement with the observed data, and the performance of the method was excellent. We demonstrate that its efficiency could be a helpful and reliable tool for monitoring oil consumption.

Intelligent Compensating Method for MPC-Based Deviation Correction with Stratum Uncertainty in Vertical Drilling Process

With the rapid development of control technology, increasing applications are using model predictive control (MPC) for deviation correction in vertical drilling. However, the accuracy of the predictive model is affected by the uncertainty of the stratum, which results in model mismatch and a reduction in control performance. In this paper, an intelligent compensating method is proposed for MPC-based deviation correction with stratum uncertainty in a vertical drilling process to increase control accuracy. First, a trajectory extension model is introduced as the predictive model for MPC, and the uncertainty of the stratum is discussed. Then, the compensation for the MPC is acquired based on a Gaussian fitting method and hybrid bat algorithm. Finally, based on the actual drilling data, a simulation is performed to demonstrate the effectiveness of the proposed method.

Optical Phased Array with Low Grating Lobes Optimized by Using Hybrid Bat Algorithm

Optical Phased Array with Low Grating Lobes Optimized by Using Hybrid Bat Algorithm

Performance Analysis of Hybrid BAT Algorithm and Cuckoo Search Algorithm [HB-CSA] for Task Scheduling in Mobile Cloud Computing

Performance Analysis of Hybrid BAT Algorithm and Cuckoo Search Algorithm [HB-CSA] for Task Scheduling in Mobile Cloud Computing

Indoor High Precision Three-Dimensional Positioning System Based on Visible Light Communication Using Improved Hybrid Bat Algorithm

To solve problems of low positioning accuracy and long time-consuming in indoor visible light three-dimensional positioning system, a visible light three-dimensional positioning method based on the improved hybrid bat algorithm (IHBA) is proposed in this paper. Firstly, some beacon points are set at the beginning of the IHBA to reduce the number of iterations. Secondly, weight coefficient is defined to improve positioning accuracy when the fitness function is constructed. Thirdly, aiming at controlling the search speed reasonably, an adaptive search factor is introduced while the bat individual update formula is designed. Finally, the chaotic perturbation operation is used to avoid the algorithm falling into local optimum. In indoor simulation environment, the size of which is 5m × 5m × 3m, the average positioning error of the IHBA is 1.16 cm and the positioning time is 1.85s. To verify the actual effectiveness of IHBA, a receiver is placed in the experimental environment, the size of which is 1.5m × 1.2m × 2m. Through the optical power detected by the receiver, the processor calculate its coordinates with IHBA. The average positioning error of the IHBA is 3.64 cm and the positioning time is 0.89 s. The proposed algorithm has practical application value in existing indoor positioning methods.

Enhancement of maximum power point tracking technique based on PV-Battery system using hybrid BAT algorithm and fuzzy controller

Solar energy has been widely adopted in power systems, particularly using the photovoltaic (PV) generation technology. In this respect, the power generation of such a technology is highly impacted by several factors, such as temperature and solar irradiance. As an effective solution, maximum power point tracking (MPPT) approaches have been developed and used in PV systems to increase the efficiency subject to the changing climate conditions. In this respect, a combinatorial MPPT technique is presented in this paper based on the fuzzy controller and BAT optimization algorithm to desirably tune the control parameters. To this end, the membership functions of the fuzzy logic controller (FLC) are appropriately specified to cope with uncertainties caused by changing climatic conditions. The studied PV system, equipped with the MPPT technique, operates jointly with an electrical energy storage system which is based on a lead-acid battery. By employing this hybrid generation system, the solar power generation intermittency can be well compensated and the stabilized power output can be achieved. The proposed model is then simulated on a typical hybrid energy system, including a PV system and a battery energy storage (BES) system. In this respect, the superior performance of the suggested control scheme is verified through making a comprehensive comparison with other well-known techniques. Besides, the behavior of the system under varying climate conditions is studied and the desired performance of the suggested combinatorial controller is validated. For example, the presented BAT-FLC scheme can help the hybrid system reach 99% efficiency for partial shading conditions (PSCs) which is 18% more compared to the prevalent perturb and observe (P&O) technique.

GIS-Based Niche Hybrid Bat Algorithm for Solving Optimal Spatial Search

Complex nonlinear optimization problems are involved in optimal spatial search, such as location allocation problems that occur in multidimensional geographic space. Such search problems are generally difficult to solve by using traditional methods. The bat algorithm (BA) is an effective method for solving optimization problems. However, the solution of the standard BA is easily trapped at one of its local optimum values. The main cause of premature convergence is the loss of diversity in the population. The niche technique is an effective method to maintain the population diversity, to enhance the exploration of the new search domains, and to avoid premature convergence. In this paper, a geographic information system- (GIS-) based niche hybrid bat algorithm (NHBA) is proposed for solving the optimal spatial search. The NHBA is able to avoid the premature convergence and obtain the global optimal values. The GIS technique provides robust support for processing a substantial amount of geographical data. A case in Fangcun District, Guangzhou City, China, is used to test the NHBA. The comparative experiments illustrate that the BA, GA, FA, PSO, and NHBA algorithms outperform the brute-force algorithm in terms of computational efficiency, and the optimal solutions are more easily obtained with NHBA than with BA, GA, FA, and PSO. Moreover, the precision of NHBA is higher and the convergence of NHBA is faster than those of the other algorithms under the same conditions.

Modified hybrid bat algorithm with genetic crossover operation and smart inertia weight for multilevel image segmentation

Multilevel thresholding is one of the most commonly used methods in image segmentation. However, the exhaustive search method is computationally expensive for selecting the optimal thresholds. Therefore, a hybrid bat algorithm with genetic crossover operation and smart inertia weight (SGA-BA) is proposed to choose the optimal thresholds. Furthermore, between-class variance (the Otsu method) and Kapur’s entropy are used as objective functions. In the novel SGA-BA, the smart inertia weight balances the SGA-BA’s exploration and exploitation based on the number of iterations and fitness values. Moreover, the local search capability of SGA-BA is strengthened by the crossover operation of the genetic algorithm. Meanwhile, the random vector is replaced by the beta distribution, which updates the frequency of bat in a smart way. The proposed SGA-BA was evaluated by a set of benchmark images with various levels of thresholds. Additionally, SGA-BA was compared with some well-known and recent heuristic algorithms, such as the genetic algorithm (GA), gravitational search algorithm (GSA), particle swarm optimization (PSO), whale optimization algorithm (WOA), improved salp swarm algorithm (LSSA) and basic bat algorithm (BA). The experimental results show that the proposed SGA-BA provides better outcomes than the other algorithms.

A Novel Support Vector Machine Based on Hybrid Bat Algorithm and Its Application to Identification of Low Velocity Impact Areas

The standard support vector machine (SVM) performs poorly on the identification problem of low velocity impact areas due to its lower accuracy rate. Improving SVM’s performance using the bat algorithm (BA) is feasible, but BA has the premature convergence problem. In this study, a hybrid bat algorithm with double mutation operations (DMBA), in which the Cauchy mutation operator and the extremal optimization mutation operator are integrated into BA, is proposed to enhance BA’s ability to jump out of the local optima. Then, a novel SVM based on this hybrid BA, which is called SVM_DMBA, is developed to address the identification problem. Compared with the standard SVM and twelve improved SVM methods which are combined with the standard algorithms, advanced algorithms, and bat variants, the significant performance of SVM_DMBA is validated using UCI datasets. Moreover, to identify low velocity impact areas, SVM_DMBA is applied to the low velocity impact localization system based on fiber Bragg grating (FBG) sensors. The statistical results indicate that SVM_DMBA is a significantly effective method for identifying the low velocity impact areas and generates higher identification accuracy than comparative methods. For 64 low velocity impact areas of $30\,\,\text {mm}\times 30\,\,\text {mm}$ on an aluminium plate, the average identification error obtained by SVM_DMBA is 1.615%.

A novel hybrid bat algorithm with a fast clustering-based hybridization

Bat algorithm (BA) is a new and promising metaheuristic search algorithm which could outperform existing algorithms. However, BA can be easily trapped in a local optimum regarded to low exploration ability. The present study proposed a new local-search-based hybrid heuristic to escape such scenario. The proposed hybrid BA (hBA) uses a clustering-based hybridization method which detects the early convergence of BA population by analyzing similarities among individuals. The main motivation for such an analysis is that when BA is continually converging, the similarity among individuals becomes higher. The proposed hBA is extensively evaluated on CEC2017 benchmark suite. The Experiments demonstrate that the algorithm achieves better results than continues variants of BA in every way. Moreover, as a case study, a binary version of the proposed hBA (hBBA) is applied to the well-known feature selection problem. The recorded results on 13 datasets demonstrate that hBBA would be considered as a new state-of-art in metaheuristic-based wrapper feature selection methods.

A New Hybrid Bat Algorithm and its Application to the ROP Optimization in Drilling Processes

Rate of penetration (ROP) optimization is crucial for drilling processes due to its vital role in increasing efficiency. This article proposes a new hybrid bat algorithm (HBA) to achieve the maximum ROP accurately. A data-driven ROP model is established by combining the wavelet filtering and optimized support vector regression according to the drilling characteristics. After that, five modifications, namely, iterative local search, stochastic inertia weight, pulse rate and loudness improvement, directional echolocation, and modified local random walk are combined to further improve the global optimization performance of the bat algorithm. Extensive experiment results on the IEEE Congress on Evolutionary Computation 2005 standard benchmark problems show that the proposed algorithm has successful outcomes compared with ten conventional algorithms. Additionally, in the application of the HBA to a real-world drilling process in Shennongjia area, Central China, the ROP has been improved by 34.84%, which is the largest compared with the conventional popular ROP optimization methods.

Bat algorithm and k-means techniques for classification performance improvement

This paper presents Bat Algorithm and K-Means techniques for classification performance improvement. The objective of this study is to investigate efficiency of Bat Algorithm in discrete dataset and to find the optimum feature in discrete dataset. In this study, one technique that comprise the discretization technique and feature selection technique have been proposed. Our contribution is in two process of classification: pre-processing and feature selection process. First, to proposed discretization techniques called as BkMD, where we hybrid Bat Algorithm technique and K-Means classifier. Second, to proposed BkMDFS as feature selection technique where Bat Algorithm is embed into BkMD. In order to evaluate our proposed techniques, 14 continuous dataset from various applications are used in experiment. From the experiment, results show that BkMDFS outperforms in most performance measures. Hence it shows that, Bat Algorithm have potential to be one of the discretization technique and feature selection technique.

Improved Hyper Elliptic Curve Cryptography with Hybrid Bat Algorithm for Tasks Replication to Meet Deadlines in Clouds

Improved Hyper Elliptic Curve Cryptography with Hybrid Bat Algorithm for Tasks Replication to Meet Deadlines in Clouds

Detection of Autism in Children by the EEG Behavior Using Hybrid Bat Algorithm-Based ANFIS Classifier

Autism spectrum disorder (ASD) is basically a varied neurodevelopmental disorder which affects the developmental curve in numerous behavioral domains. This includes the impairments of social communication, cognitive and also language abilities. Autism has an extensive gamut of severity and symptoms that are often utilized to categorize the ASD. Each of the syndromes under ASD is disparate as of the other. For instance, individuals suffering with Asperger syndrome have no considerable hindrance in language development. Kids with ASD might concurrently also encompass other issues, for instance, Tourette’s syndrome epilepsies, attention-deficit hyperactivity disorder, etc. So, there is a need to detect autism in children. ASD can be detected in children by analyzing the signals (EEG). Initially, the EEG signals are attained from the dataset. This signal undergoes preprocessing utilizing Kalman filter. Next, signal decomposition is done by the variable mode decomposition. This is followed by feature extraction. These are classified utilizing Hybrid Bat Algorithm with ANFIS classifier (HBA–ANFIS). The classified output is either a normal signal or an autism signal. To prove the proposed work’s efficiency, disparate performance metrics were determined and also analyzed. The proposed work shows superior performance in respect of all the evaluated metrics.

Prediction of drilling rate of penetration (ROP) using hybrid support vector regression: A case study on the Shennongjia area, Central China

Rate of penetration (ROP) prediction is crucial for the optimization and control in drilling process due to its vital role in maximizing the drilling efficiency. This paper proposes a novel intelligent model to predict the drilling ROP considering the process characteristics. First, the geological background and the drilling process of the case study are described. Based on the mechanism and frequency spectrum analysis, the strong nonlinearity and different low-frequency and high-frequency data noises between the data variables are detected. After that, the intelligent model is established via three stages. In the first stage, a wavelet filtering method is introduced to reduce these noises in the drilling data. In the next stage, the model inputs are determined by the mutual information method, which significantly decreased the model redundancy. In the last stage, a hybrid bat algorithm is proposed to optimize the hyper-parameters of the support vector regression model. Finally, the proposed model is validated by using the data from a drilling site in the Shennongjia area, Central China. The results demonstrate that the proposed method outperforms eight well-known methods and another three methods without different data preprocessing procedures in prediction accuracy.

Improved Hybrid Bat Algorithm with Invasive Weed and Its Application in Image Segmentation

As one of the most popular and effective image segmentation methods, multi-level thresholding is widely used. However, too much computation is needed to select the optimal thresholds with basic ergodic method. In order to solve this problem, a hybrid bat algorithm (IWBA) which incorporates bat algorithm with invasive weed optimization (IWO) is employed to choose the optimal thresholds. In IWBA algorithm, the local search ability is enhanced by integrating with IWO algorithm. Furthermore, a new inertia weight based on Lagrange interpolation is proposed to balance exploration and exploitation. In IWBA algorithm, scale parameter of normal distribution is adjusted according to the value of fitness. It is established that IWBA algorithm is able to segment the image in more efficient and accurate way than other algorithms. More importantly, IWBA algorithm can also be applied to other fields.

Optimal Image Compression based on Hybrid Bat Algorithm and Pattern Search

Optimal Image Compression based on Hybrid Bat Algorithm and Pattern Search

Hybrid Bat & Particle Swarm Algorithm for optimization of labyrinth spillway based on half & quarter round crest shapes

Optimization is an interesting method that finds the optimal response (i.e., objective function) by searching the decision variables in the search space. The present study deals with the optimal design of a labyrinth spillway (LS) having half-round or quarter-round crest shape. For optimal the design of the spillway geometry, an evolutionary Hybrid Algorithm (HA) of Bat Algorithm (BA) and Particle Swarm Optimization (PSO) algorithm was developed. By this procedure, the worst responses of one algorithm are replaced by the best responses of the other algorithm. This HA does not get trapped in local minima and has high convergence rate concerning the optimal absolute response. Results of using HA showed that LS with half-round crest requires less concrete, compared to quarter-round crest. Moreover, the discharge capacity for LS with half-round crest was more than the quarter-round crest. The HA significantly reduced the computational time for the optimal design of LS with half-round or quarter-round crest, as compared to BA or PSO.

Enhancement of vibration based piezoelectric energy harvester using hybrid optimization techniques

MEMS unimorph piezoelectric vibration energy harvester is designed and optimized based on Hybrid Optimization techniques [genetic algorithm (GA), bat algorithm (BA), grey wolf optimization (GWO), hybrid bat algorithm tuned genetic algorithm (HBAGA) and hybrid grey wolf optimizer tuned genetic algorithm (HGWOGA)] for performance improvement. In this research work, unimorph piezoelectric vibration energy harvester is modeled using analytical equations, which is derived to determine the amount of voltage and power, harvested from the piezoelectric energy harvester. These derived analytical equations are used as the fitness function of Hybrid optimization techniques, which is a design optimization technique to improve the amount of power and voltage harvested from the unimorph piezoelectric energy harvester, by optimizing the parameters and a comparison is made within these techniques. The effects of geometry optimization on natural frequency and stress are also studied. The un-optimized results are compared with the optimized results (GA, BA, GWO, HBAGA and HGWOGA) and the results obtained, proves that, the optimized results are more efficient in all aspects, which has significantly improved the efficiency of the piezoelectric harvester to a larger extent. Finally, a comparison is made within the optimization techniques and HGWOGA has proved to be more efficient than HBAGA, GWO, BA, GA and this work of MEMS piezoelectric vibration energy harvester is simulated using the MATLAB software.