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In this paper, a two-level search strategy fused with an improved no-fit polygon algorithm and improved bat algorithm is proposed to obtain the layout points of multiple vehicles. Additionally, a space–time scheduling strategy is proposed using the Improved D*Lite Algorithm (ID*Lite) and improved Bezier curve to generate the trajectories of individual vehicles. Furthermore, a conflict resolution strategy is introduced to address the collision conflict problem during multi-vehicle scheduling. The proposed strategies aim to achieve the objectives of shortest scheduling time and smallest array space in order to reduce the time and space cost of vehicle loading on ro-ro carriers. Through a comparison with other algorithms (e.g., at high loading pressure, Scenario 3), the strategies presented in this paper demonstrate advantages such as higher deck utilization (92.234%), shorter path length, fewer collision conflicts, and more balanced weighting. Mainstream methods only focus on a certain part of vehicle transportation, we not only use more stable parking spot generation algorithm and parking order generation algorithm, but also more high-speed multi-vehicle path-finding algorithm, path optimization strategy, multi-vehicle scheduling strategy, which innovatively form a complete vehicle planning process, and at the same time, as an algorithmic framework that can be generalized to any 2d or 3d scenarios. [ABSTRACT FROM AUTHOR]

Reliable and effective medium- to long-term power demand forecasting serves as a crucial foundation for power generation and transmission. With the rapid development of China's renewable energy sector, the impact of wind and solar power volatility cannot be overlooked. Consequently, ensuring that future power system planning can economically and efficiently adapt to varying demand scenarios has become a topic of high concern. Here, we propose an integrated evaluation model for predictive dispatch based on the Extreme Learning Machine (ELM) optimized by the Bat Algorithm (BA), alongside the introduction of fuzzy parameters in the cooperative source-load-storage operation algorithm. Moreover, an analysis and research study has been conducted in northwest China as an example. The results show that this model can accurately forecast power demand under diverse development scenarios and provides scientific guidance for optimizing the planning of source-load-storage resources. [ABSTRACT FROM AUTHOR]

This study aims to solve a second-order Boundary Value Problem (BVP) using Bat Algorithm (BA) created by Xin-She Yang, who took inspiration from the echolocation habits of microbats with varying emission and loudness pulse rates. By considering two numerical instances, the problem formulation is based on a finite difference approximation of the discretized derivative. When compared to the exact solution, the algorithm results, and the finite difference method, the experimental results demonstrate that the BA yields a satisfactorily precise approximation of the solutions, confirming the usefulness and effectiveness of the proposed method and demonstrating its applicability for solving BVP. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Secure image transfer is a difficult topic in the age of communication technology because millions of people utilize and share images online for personal and professional reasons. Encryption algorithms, such as cipher images, help achieve secure transfer through networks. Despite attackers having decryption keys, they cannot retrieve the original image. To ensure integrity assurance, prevent changes to medical images that could lead to a misdiagnosis, transmit patient medical records in a private and secure manner, and prevent falling victim to cyberattacks, a high-performance, effective method of encrypting medical images must be developed. Encrypting medical images is common in telemedicine, making secure image transfer essential. The medical dataset includes personal data about the health of a patient. All essential information, including medical images, is now kept on picture and communication servers because of the growing interest in inpatient records across the world. In this study, we presented a unique approach to medical picture encryption that combines the Triple data encryption algorithm (3DES) and advanced encryption standard (AES) methods with three chaotic maps (Logistic, Arnold CAT, and Baker). The BAT optimization algorithm is also used to accomplish the task of key generation. Finally, the Least Significant Bit (LBS) is used to hide encrypted medical images before sending them to the server by TCP/IP protocol. The experiments yielded promising results in entropy of 5.92, PSNR of 0.99, and MSE 0.0001. [ABSTRACT FROM AUTHOR]

The research presented in this article aims at the modelling and optimization of hydrogen-fuel-cell-based distributed generation (HFC-DG) to minimize the effect of electric vehicle charging stations (EVCSs) in a radial distribution system (RDS). The key objective of this work is to address various challenges that arise from the integration of EVCSs, including increased power demand, voltage fluctuations, and voltage stability. To accomplish this objective, the study utilizes a novel spotted hyena optimizer algorithm (SHOA) to simultaneously optimize the placement of HFC-DG units and EVCSs. The main goal is to mitigate real power loss resulting from the additional power demand of EVCSs in the IEEE 33-bus RDS. Furthermore, the research also investigates the influence of HFC-DG and EVCSs on the reliability of the power system. Reliability is crucial for all stakeholders, particularly electricity consumers. Therefore, the study thoroughly examines how the integration of HFC-DG and EVCSs influences system reliability. The optimized solutions obtained from the SHOA and other algorithms are carefully analyzed to assess their effectiveness in minimizing power loss and improving reliability indices. Comparative analysis is conducted with varying load factors to estimate the performance of the presented optimization approach. The results prove the benefits of the optimization methodology in terms of reducing power loss and improvising the reliability of the RDS. By utilizing HFC-DG and EVCSs, optimized through the SHOA and other algorithms, the research contributes to mitigating power loss caused by EVCS power demand and improving overall system reliability. Overall, this research addresses the challenges associated with integrating EVCSs into distribution systems and proposes a novel optimization approach using HFC-DG. The findings highlight the potential benefits of this approach in terms of minimizing power loss, enhancing reliability, and optimizing distribution system operations in the context of increasing EV adoption. [ABSTRACT FROM AUTHOR]

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Because of the increasing growth of Electric Vehicle (EV) in India, more electricity is required to power such vehicles. It is also gaining popularity because of its low maintenance, improved performance, and zero carbon impact. As the usage of electric vehicles grows, the distribution system’s performance is impacted. As an outcome, the reliability of the distribution system (DS) is dependent on the position of the electric vehicle charging station (EVCS). The fundamental difficulty is the deterioration of the DS due to an incorrect EVCS location. The DS is linked to the charging station and works with the distribution static compensator (DSTATCOM) to minimize the impact of the EVCS. A new nature-inspired Bald Eagle Search Algorithm (BESA) based optimization technique was utilized to find the optimal allocation of DSTATCOM and EVCS in the DS. The proposed strategy for mitigating the real power loss has been tested on practical Indian 28-bus and 108-bus distribution networks. Power loss reduction optimizes the system’s net savings, voltage stability, and bus voltage. The test case findings show that the BESA-based optimization is more accurate regarding power loss mitigation, bus voltage enhancement, and annual net saving improvement than the BA-based optimization in the DS.

Swarm Intelligence algorithms are computational intelligence algorithms inspired from the collective behavior of real swarms such as ant colony, fish school, bee colony, bat swarm, and other swarms in the nature. Swarm Intelligence algorithms are used to obtain the optimal solution for NP-Hard problems that are strongly believed that their optimal solution cannot be found in an optimal bounded time. Travels Salesman Problem (TSP) is an NP-Hard problem in which a salesman wants to visit all cities and return to the start city in an optimal time. In this article we are applying most efficient heuristic based Swarm Intelligence algorithms which are Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Bat algorithm (BA), and Ant Colony Optimization (ACO) algorithm to find a best solution for TSP which is one of the most well-known NP-Hard problems in computational optimization. Results are given for different TSP problems comparing the best tours founds by BA, ABC, PSO and ACO.

Renewable Energy Sources are an effective alternative to the atmosphere-contaminating, rapidly exhausting, and overpriced traditional fuels. However, RESs have many limitations like their intermittent nature and availability at far-off sites from the major load centers. This paper presents the forecasting of wind speed and power using the implementation of the Feedforward and cascaded forward neural networks (FFNNs and CFNNs, respectively). The one and half year's dataset for Jhimpir, Pakistan, is used to train FFNNs and CFNNs with recently developed novel metaheuristic optimization algorithms, i.e., hybrid particle swarm optimization (PSO) and a Bat algorithm (BA) named HPSOBA, along with a modified hybrid PSO and BA with parameter-inspired acceleration coefficients (MHPSO-BAAC), without and with the constriction factor (MHPSO-BAAC-χ). The forecasting results are made for June–October 2019. The accuracy of the forecasted values is tested through the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The graphical and numerical comparative analysis was performed for both feedforward and cascaded forward neural networks that are tuned using the mentioned optimization techniques. The feedforward neural network was achieved through the implementation of HPSOBA with a mean absolute error, mean absolute percentage error, and root mean square error of 0.0673, 6.73%, and 0.0378, respectively. Whereas for the case of forecasting through a cascaded forward neural network, the best performance was attained by the implementation of MHPSO-BAAC with a MAE, MAPE and RMSE of 0.0112, 1.12%, and 0.0577, respectively. Thus, the mentioned neural networks provide a more accurate prediction when trained and tuned through the given optimization algorithms, which is evident from the presented results. [ABSTRACT FROM AUTHOR]

Bat algorithm (BA) is one of the well-established metaheuristic algorithms based on the echolocation characteristics of microbats. Literature studies demonstrate that BA provides good results in a wide range of optimization problems due to its simple structures, easy implementation, and effectiveness. However, the standard BA has two general shortcomings when applied to complex optimization problems. The first is the high probability of falling in local optima, and the second is insufficient population diversity, leading to unwanted premature convergence. This paper proposes an improved bat algorithm based on the Doppler effect (IBA-DE) to improve the performance of the standard BA. In the proposed IBA-DE, a new equation with some idealized rules is driven from the Doppler effect and adopted for updating the bat velocities in the algorithm body. The local search part of the standard BA is also improved via searching around the better bat individuals. This part is also equipped with Lévy flight to maintain the population diversity. The performance of IBA-DE is evaluated on three real-world engineering design problems. The results obtained by these problems show that IBA-DE is superior to the basic BA and other considered algorithms. For further investigation, IBA-DE is applied to the optimal design of four special truss structures. Comparing the optimization results found by the proposed algorithm with those of some other state-of-art metaheuristics indicates that the proposed IBA-DE can be an efficient optimizer for the optimal design of real-size structures. [ABSTRACT FROM AUTHOR]

Metadata extraction facilitates the process of indexing and improves information retrieval. Also automation of this process increases efficiency more than manual extraction. The example of the thesis metadata are names of students, professors, title, field, degree, abstract, keywords, etc. In this paper the aim is automatic boundary detection of metadata from the main body in Persian theses. Therefore, 250 theses collected from IRANDOC system. Features were extracted from paragraphs of each thesis then paragraphs were classified using support vector machine into 2 classes: metadata and body. In this study, Bat algorithm is used to set the parameter of SVM. The result reveals that the proposed method predicts type of paragraphs with 96.6 percent accuracy.

Background: This research studies a medical staff scheduling problem, which includes government regulations and hospital regulations (hard constraints) and the medical staff's preferences (soft constraints).Objective: The objective function is to minimize the violations (or dissatisfaction) of medical staff's preferences.Methods: This study develops three variants of the three-phase modified bat algorithms (BAs), named BA1, BA2, and BA3, in order to satisfy the hard constraints, minimize the dissatisfaction of the medical staff and balance the workload of the medical staff. To ensure workload balance, this study balances the workload among medical staff without increasing the objective function values.Results: Based on the numerical results, the BA3 outperforms the BA1, BA2, and particle swarm optimization (PSO). The robustness of the BA1, BA2, and BA3 is verified. Finally, conclusions are drawn, and directions for future research are highlighted.Conclusions: The framework of this research can be used as a reference for other hospitals seeking to determine their future medical staff schedule. [ABSTRACT FROM AUTHOR]

Software Product Lines (SPLs) are one of the ways to develop software products by increasing productivity and reducing cost and time in the software development process. In SPLs, each product has many features and it is necessary to consider the optimal and custom features of the products. In fact, selecting key features in SPLs is a challenging process. Feature selection in SPLs is an optimization problem and an NP-Hard problem. One way to select a feature is to use meta-heuristic algorithms modeled on nature, i.e., Bat Algorithm. By modeling bat behavior in prey hunting, a suitable meta-innovative algorithm is considered. This algorithm has important advantages that make it more accurate than conventional methods such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm. In this paper, to select software product features, idol binary algorithm and artificial neural network are used to identify important features of software products that reduce production costs and time. The experiments in MATLAB software and datasets related to software production lines show that the rate of reduction of target performance error or feature selection cost in software production lines in the proposed method has decreased by 64.17% with increasing population. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid metaheuristic optimization algorithm developed to solve the Economic Dispatch Problem (EDP) encountered in different combinations of power plants. The algorithm is developed by assimilating the prominent features of Particle Swarm Optimization (PSO) and Bat Algorithm (BA) and improves cost reduction and convergence with lesser computational time. The developed algorithm is employed for the solution of EDP consisting of only Renewable Energy Sources (RESs) implemented at various locations in Pakistan. The all RES based EDP consists of scenarios composed of sub-scenarios having no constraints, with time-varying loads and multi-area economic dispatch (MAED). The algorithm is also tested for three different combinations of power plants, comprising of RES integrated with thermal power plants (TPPs), the small-scaled thermal power system with constraints, and a large-scaled power system with Valve Point Loading (VPL) effect. The comparative analysis of the results for the developed metaheuristic algorithm with various existing techniques shows a reasonable reduction in the cost, improved computational time, and fast convergence.

Metadata extraction facilitates the process of indexing and improves information retrieval. Also automation of this process increases efficiency more than manual extraction. The example of the thesis metadata are names of students, professors, title, field, degree, abstract, keywords, etc. In this paper the aim is automatic boundary detection of metadata from the main body in Persian theses. Therefore, 250 theses collected from IRANDOC system. Features were extracted from paragraphs of each thesis then paragraphs were classified using support vector machine into 2 classes: metadata and body. In this study, Bat algorithm is used to set the parameter of SVM. The result reveals that the proposed method predicts type of paragraphs with 96.6 percent accuracy. [ABSTRACT FROM AUTHOR]

The research purpose was to improve the accuracy in identifying the prosthetic leg locomotion mode. Surface electromyography (sEMG) combined with high-order zero-crossing was used to identify the prosthetic leg locomotion modes. sEMG signals recorded from residual thigh muscles were chosen as inputs to pattern classifier for locomotion-mode identification. High-order zero-crossing were computed as the sEMG features regarding locomotion modes. Relevance vector machine (RVM) classifier was investigated. Bat algorithm (BA) was used to compute the RVM classifier kernel function parameters. The classification performance of the particle swarm optimization-relevance vector machine (PSO-RVM) and RVM classifiers was compared. The BA-RVM produced lower classification error in sEMG pattern recognition for the transtibial amputees over a variety of locomotion modes: upslope, downgrade, level-ground walking and stair ascent/descent. [ABSTRACT FROM AUTHOR]

The bat algorithm (BA) has fast convergence, a simple structure, and strong search ability. However, the standard BA has poor local search ability in the late evolution stage because it references the historical speed; its population diversity also declines rapidly. Moreover, since it lacks a mutation mechanism, it easily falls into local optima. To improve its performance, this paper develops a hybrid approach to improving its evolution mechanism, local search mechanism, mutation mechanism, and other mechanisms. First, the quantum computing mechanism (QCM) is used to update the searching position in the BA to improve its global convergence. Secondly, the X-condition cloud generator is used to help individuals with better fitness values to increase the rate of convergence, with the sorting of individuals after a particular number of iterations; the individuals with poor fitness values are used to implement a 3D cat mapping chaotic disturbance mechanism to increase population diversity and thereby enable the BA to jump out of a local optimum. Thus, a hybrid optimization algorithm—the chaotic cloud quantum bats algorithm (CCQBA)—is proposed. To test the performance of the proposed CCQBA, it is compared with alternative algorithms. The evaluation functions are nine classical comparative functions. The results of the comparison demonstrate that the convergent accuracy and convergent speed of the proposed CCQBA are significantly better than those of the other algorithms. Thus, the proposed CCQBA represents a better method than others for solving complex problems. [ABSTRACT FROM AUTHOR]

A new approach for the design of wideband multilayer stacked patch antennas is proposed based on the bat algorithm. Differential topology is described for multilayer stacked patch antennas to achieve the stable and symmetry radiation patterns as well as the high cross-polarization (XP) suppression within the design band. The fabricated antenna prototypes show 25.5% and 34.9% impedance bandwidth for the two-layer and three-layer designs, respectively. The measured results validated the design. [ABSTRACT FROM AUTHOR]

This paper proposes a novel robust power system stabilizer (PSS), based on hybridization of fractional order PID controller (PIλDμ) and PSS for optimal stabilizer (FOPID-PSS) for the first time, using a new metaheuristic optimization Bat algorithm (BA) inspired by the echolocation behavior to improve power system stability. The problem of FOPID-PSS design is transformed as an optimization problem based on performance indices (PI), including Integral Absolute Error (IAE), Integral Squared Error (ISE), Integral of the Time-Weighted Absolute Error (ITAE) and Integral of Time multiplied by the Squared Error (ITSE), where, BA is employed to obtain the optimal stabilizer parameters. In order to examine the robustness of FOPID-PSS, it has been tested on a Single Machine Infinite Bus (SMIB) power system under different disturbances and operating conditions. The performance of the system with FOPID-PSS controller is compared with a PID-PSS and PSS. Further, the simulation results obtained with the proposed BA based FOPID-PSS are compared with those obtained with FireFly algorithm (FFA) based FOPID-PSS. Simulation results show the effectiveness of BA for FOPID-PSS design, and superior robust performance for enhancement power system stability compared to other with different cases.

At present, China has the world's highest CO2 emissions. The reduction of China's CO2 emissions will have a direct effect on the world. Considering that CO2 emissions mainly come from the burning of fossil fuel, it is of great significance to accurately calculate and forecast China's energy‐related CO2 emissions. To improve the prediction accuracy of CO2 emissions, this paper proposed a new prediction model, which combines t‐distribution, Gaussian perturbations bat algorithm, and a least squares support vector machine, namely the TBAG‐LSSVM model. Furthermore, in order to ensure the rationality of factor selection, a stationary test, cointegration test, and Granger causality test were utilized to analyze factors affecting CO2 emissions. Through an empirical test, it was found that the proposed model has higher accuracy than LSSVM, BA‐LSSVM, WOA‐LSSVM, ELM, and BPNN. Therefore, three scenarios were put into the model to predict China's CO2 emissions up to 2030. The results indicate that CO2 emissions in 2030 will be 12 853.18, 11 378.27, and 12 008.19 million tons. According to the simulation results, industrial restructuring and the elimination of outdated production capacity should be pushed forward continuously to ensure that the Chinese government achieves emission reduction targets. © 2019 Society of Chemical Industry and John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

An application of bat algorithm (BA) as a metaheuristic optimization approach in materials and design to an engineering problem has been presented in this paper. The purpose of the case study was to develop a new float as a part of measurement system according to the setup configuration and test environment. With this regard, several materials such as Acrylic, PVC, Nylon, Teflon (PTFE), and low-density polyethylene as feasible options for the float body in terms of mechanical, thermal, and chemical properties were selected. Then, optimal concurrent design of the float system with selected materials based on structural and performance constraints was addressed. For this purpose, the design was formulated into a constrained optimization problem and BA was used to find the optimal solutions in order to minimize the float length. The convergence of the design variables and constraints to optimal values was also investigated. Generalized reduced gradient method was used as well for validation and comparison of the BA results. It was found that the new optimal float had a pretty good performance in the test measurement. The results showed that BA can be a quite efficient approach to solve constrained optimization problems in materials and design. It is also suggested that the new float problem can be considered as a benchmark problem in materials and design to validate the robustness of the optimization algorithms. [ABSTRACT FROM AUTHOR]

Security issue in cloud environment is one of the major obstacle in cloud implementation. Network attacks make use of the vulnerability in the network and the protocol to damage the data and application. Cloud follows distributed technology; hence it is vulnerable for intrusions by malicious entities. Intrusion detection systems (IDS) has become a basic component in network protection infrastructure and a necessary method to defend systems from various attacks. Distributed denial of service (DDoS) attacks are a great problem for a user of computers linked to the Internet. Data mining techniques are widely used in IDS to identify attacks using the network traffic. This paper presents and evaluates a Radial basis function neural network (RBF-NN) detector to identify DDoS attacks. Many of the training algorithms for RBF-NNs start with a predetermined structure of the network that is selected either by means of a priori knowledge or depending on prior experience. The resultant network is frequently inadequate or needlessly intricate and a suitable network structure could be configured only by trial and error method. This paper proposes Bat algorithm (BA) to configure RBF-NN automatically. Simulation results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The coastal area of South China is a frequent seismic activity area. To improve the seismic performance of architectural structures in the coastal areas of South China, this paper proposed a Niche Bat Algorithm (NBA) Wavelet Neural Network (WNN) constitutive model of Shape Memory Alloy (SMA), and compared it with WNN model and BA-optimized WNN (BA-WNN). The paper applied SMA to the seismic control of truss structure, and the obtained SMA control system can more effectively reduce the interlayer displacement of the structure and alleviate the seismic response of the structure, it provided a new method for seismic resistance of coastal structures. [ABSTRACT FROM AUTHOR]

Vector quantization (VQ) is a popular image compression technique with a simple decoding architecture and high compression ratio. Codebook designing is the most essential part in vector quantization. Linde–Buzo–Gray (LBG) is a traditional method of generation of VQ codebook which results in lower PSNR value. A codebook affects the quality of image compression, so the choice of an appropriate codebook is a must. Several optimization techniques have been proposed for global codebook generation to enhance the quality of image compression. In this paper, a novel algorithm called IDE-LBG is proposed which uses improved differential evolution algorithm coupled with LBG for generating optimum VQ codebooks. The proposed IDE works better than the traditional DE with modifications in the scaling factor and the boundary control mechanism. The IDE generates better solutions by efficient exploration and exploitation of the search space. Then the best optimal solution obtained by the IDE is provided as the initial codebook for the LBG. This approach produces an efficient codebook with less computational time and the consequences include excellent PSNR values and superior quality reconstructed images. It is observed that the proposed IDE-LBG find better VQ Codebooks as compared to IPSO-LBG, BA-LBG and FA-LBG. [ABSTRACT FROM AUTHOR]

In this paper discusses various algorithms on optimal tuning Fractional-Order Proportional Integral Derivative (FOPID) controller to govern the rotor speed of sensorless Brushless Direct Current (BLDC) motor. Bat Algorithm (BA) is used as the optimization algorithm for developing a novel approach to generate five degrees of freedom parameters namely k, k, p i λ and m of FOPID controller. A robust performance is achieved by using the FOPID closed loop speed controller using BA for optimal tuning along with a control on the desired speed. The study measured the time domain specifications of a dynamic system for unit step input to FOPID controller for speed response such as peak time (t), Percentage of overshoot r (PO), settling time (t), rise time (t) have been evaluated and the steady-state error (e) of sensorless speed control of BLDC s r ss motor. The characteristics are compared with the results from Artificial Bee Colony (ABC) and Modified Genetic Algorithm (MGA) for transient and steady state time domain. The results confirm that the proposed controller optimization using BA technique is more efficient in improving the transient characteristic performance and reducing steady state error for the FOPID. [ABSTRACT FROM AUTHOR]

To improve the accuracy of short‐term load forecasting of power systems, according to the nonlinearity and uncertainty of short‐term load sequence, a short‐term power load forecasting method combined with wavelet neural network (WNN) and adaptive mutation bat optimization algorithm (AMBA), which is based on the variance of the population's fitness, is proposed in this paper. The model determines the mutation probability of the current optimal individual based on the variance of the population's fitness and the current optimal solution, performs Gaussian mutation on the global optimal individual, and carries out the second optimization on the bat individuals after mutation. AMBA is employed to optimize the network parameters of WNN, improving the prediction accuracy of WNN and speeding up its training. Then the AMBA‐WNN forecasting model is built. The AMBA‐WNN model is used to predict short‐term load of a certain city in China as a case study. The results show that the model can effectively improve the accuracy of short‐term load forecasting and has good practical significance. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

In this article, a fuzzy logic based power system stabilizer (FPSS) is designed by tuning its input–output scaling factors. Two input signals to FPSS are considered as change of speed and change in power, and the output signal is considered as a correcting voltage signal. The normalizing factors of these signals are considered as the optimization problem with minimization of integral of square error in single-machine and multi-machine power systems. These factors are optimally determined with bat algorithm (BA) and considered as scaling factors of FPSS. The performance of power system with such a designed BA based FPSS (BA-FPSS) is compared to that of response with FPSS, Harmony Search Algorithm based FPSS (HSA-FPSS) and Particle Swarm Optimization based FPSS (PSO-FPSS). The systems considered are single-machine connected to infinite-bus, two-area 4-machine 10-bus and IEEE New England 10-machine 39-bus power systems for evaluating the performance of BA-FPSS. The comparison is carried out in terms of the integral of time-weighted absolute error (ITAE), integral of absolute error (IAE) and integral of square error (ISE) of speed response for systems with FPSS, HSA-FPSS and BA-FPSS. The superior performance of systems with BA-FPSS is established considering eight plant conditions of each system, which represents the wide range of operating conditions.

Linde–Buzo–Gray (LBG), a traditional method of vector quantization (VQ) generates a local optimal codebook which results in lower PSNR value. The performance of vector quantization (VQ) depends on the appropriate codebook, so researchers proposed optimization techniques for global codebook generation. Particle swarm optimization (PSO) and Firefly algorithm (FA) generate an efficient codebook, but undergoes instability in convergence when particle velocity is high and non-availability of brighter fireflies in the search space respectively. In this paper, we propose a new algorithm called BA-LBG which uses Bat Algorithm on initial solution of LBG. It produces an efficient codebook with less computational time and results very good PSNR due to its automatic zooming feature using adjustable pulse emission rate and loudness of bats. From the results, we observed that BA-LBG has high PSNR compared to LBG, PSO-LBG, Quantum PSO-LBG, HBMO-LBG and FA-LBG, and its average convergence speed is 1.841 times faster than HBMO-LBG and FA-LBG but no significance difference with PSO.

Nowadays, the development of smart grids has been the focus of attention due to its advantages for power systems. One of the aspects of smart grids defined by using distributed generation (DG) in a low voltage network is a microgrid (MG). Based on its operational states, MG can operate in different configurations such as grid-connected mode or off-grid mode. The switching between these states is one of the challenging issues in this technical area. The fault currents in different buses have higher value compared to islanded mode of MG when the MG is connected to the main grid, which influences the protection equipment. In this situation, some electrical devices may be damaged due to the fault currents. Application of a fault current limiter (FCL) is considered as an effective way to overcome this challenge. The optimal size of these FCLs can optimize the performance of an MG. In this paper, an index for FCL size optimization has been used. In addition, two optimization algorithms (Bat Algorithm and Cuckoo Search Algorithm) have been applied to the problem. The application of an FCL has been studied in grid-connected and islanded-mode. In addition, the application of the capacitor bank in both modes has been investigated. The results of simulations carried out by MATLAB have been presented and compared.

Given the stochastic nature of wind, wind power grid-connected capacity prediction plays an essential role in coping with the challenge of balancing supply and demand. Accurate forecasting methods make enormous contribution to mapping wind power strategy, power dispatching and sustainable development of wind power industry. This study proposes a bat algorithm (BA)–least squares support vector machine (LSSVM) hybrid model to improve prediction performance. In order to select input of LSSVM effectively, Stationarity, Cointegration and Granger causality tests are conducted to examine the influence of installed capacity with different lags, and partial autocorrelation analysis is employed to investigate the inner relationship of grid-connected capacity. The parameters in LSSVM are optimized by BA to validate the learning ability and generalization of LSSVM. Multiple model sufficiency evaluation methods are utilized. The research results reveal that the accuracy improvement of the present approach can reach about 20% compared to other single or hybrid models.

Facility location is a long-term strategic decision problem, but the location decision-making is confronted with various uncertain factors. Therefore, it is of strategic importance to design a reliable location network. This paper considers both demand uncertainty and facility failures, extends uncapacitated fix-charge location problem to establish reliability facility location robust optimization model under uncertainty and failure scenarios. A new mixed integer programming model is proposed based on Bertsimas and Sim robust method, the nonlinear robust optimization model is transformed into a robust correspondence model by introducing the auxiliary variable and dual transformation. W e develop bat algorithm (BA) to solve the model, our computational results show the feasibility of the presented model and algorithm. This paper provides model and method design for facility location decision. [ABSTRACT FROM AUTHOR]

Nature-inspired optimization algorithms are presented in this paper for the control of brushless direct current (BLDC) motor drive. These algorithms have a number of advantages over classical methods for solving complex optimization problems. The algorithms in this class are based on perceptive behaviour of well ordered members of the population. This type of nature can be found among birds, fishes and insects, such as ants, bees, and the like. These algorithms have mastery over classical methods for solving complex and badly corroborated problems. The key feature of the population algorithms is that the search strategy should be simple and independent. No agent should have dominance over the entire search process. The commutation is implemented electronically in this motor drive using power electronic switches to modify current in the windings based on rotor position. The BLDC motor is just a reversed DC commutator motor, as its conductors remain stationary while magnet rotates facilitating the internal or external position sensors in the motors to sense the actual position of rotor. The rotor position can also be found by the measurement of variations in the Back emf, which is known as sensorless control method. This method reduces the cost as well as the size of the motor as it does not require sensors for determining rotor position. Matlab/simulink is used for modelling drive system. The simulation results with the proposed optimization algorithms are effective in controlling the speed of the drive system. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this research is to compare the performance of Bat and Genetic Algorithm for the optimal conductor selection in radial distribution system for power loss minimization and voltage profile enhancement. Design/Methodology/Approach: The power flow model based on the load current, Bus Injected to Branch Current (BIBC), and Branch Current to Bus Voltage (BCBV) matrices was developed in MATLAB 2013b environment to compute the objective function. The implemented algorithms were validated on standard IEEE 33-bus radial distribution network, and tested on a 62-bus practical radial distribution network in Kaduna State Nigeria. Findings: The simulation results show that using Genetic Algorithm (GA) for an unbalanced distribution network, such as the one used in the case study, there is a reduced power loss of 19% and a voltage improvement of 1.3%. Using Bat Algorithm (BA), the reduced power loss is 26% with a voltage profile improvement of 1.9% when compared to the results of the network base-case. Research Limitation: The limitation of this research work is that it did not consider other IEEE bus systems like IEEE 4-bus and 62-bus systems. Originality/Value: The contribution of this research work is the use of BIBC and BCBV load flow analysis on BA and GA for the optimal conductor selection in IEEE 33-bus system and 62- bus system. The use has never been tested. The research findings contributes positively to the management of IEEE 33-bus radial distribution network. [ABSTRACT FROM AUTHOR]

In this study, sine cosine algorithm (SCA), as a new meta-heuristic method, is proposed for optimal design of power system stabilizer (PSS). For the PSS design problem, a new multi-objective function consisting of eigenvalue and time-domain based objective functions is considered and with the SCA technique, this proposed objective function was minimized and the optimal parameters of the stabilizer were found. The robustness and effectiveness of the stabilizer designed with SCA was tested to suppress the low-frequency oscillations in power systems, which are exposed to different disturbances. The proposed SCAbased PSS (SCAPSS) results were compared with bat algorithm based PSS (BAPSS) and symbiotic organisms search algorithm based PSS (SOSPSS). The superiority of the designed SCAPSS was verified by linear model stability analysis and nonlinear time-domain simulation. The results of the analysis have showed that the proposed novel approach provides faster damping and minimal overshoot of the low frequency oscillations in the rotor of the synchronous machine. In addition, SCA has performed better than the other two algorithms in terms of accuracy of final solution, convergence speed, computation time, damping ratio, and settling time of rotor angle. [ABSTRACT FROM AUTHOR]

Abstract The linear active disturbance approach is employed to deal with the load frequency control issue of a single area wind power system based on doubly fed induction generator, and the performance of the control law is optimized by using the bat-inspired algorithm. The load frequency control issue has become more challenging in a complex power system based on wind energy conversion system due to the varying feature of the wind penetration, and sustaining the balance between the power generation and demand by rejecting the internal uncertainties in the process model and the external disturbances simultaneously. In the framework of the presented linear active disturbance rejection control approach, by constructing an extended state observer, the total disturbance, including all the unmodelled dynamics in the process model and the external disturbances, can be estimated in real time and then compensated by a simple linear PD control law. The controller parameters tuning is then simplified into the optimization of the two bandwidths: observer bandwidth, and the controller bandwidth. Then, this issue can be achieved by employing the heuristic modified bat inspired algorithm based on the optimization of the proposed performance index. The effectiveness of the proposed approach is validated by the extensive simulation examples of the load frequency control issue involved in the single area power system, taking into account different wind penetration, as well as the external disturbances. The performance robustness of the proposed approach against the parameters perturbation in the process model is also demonstrated via the Monte-Carlo method. The performance superiority of the proposed approach over the conventional Proportional Integral and Fuzzy-Proportional Integral based controller even in the presence of external disturbances and uncertainty in power system parameters under different cases of high wind penetration is also validated from the simulation results. Highlights • LADRC is utilized for the LFC problem concerning high wind power penetration. • A modified bat inspired algorithm is used to optimize system performance. • The performance is compared with PI and fuzzy PI approach, and its robustness is also validated. [ABSTRACT FROM AUTHOR]

Wireless sensor network deployment in a 3-D environment with an optimal balance of system lifetime, cost, coverage, connectivity, and fault tolerance is a challenging task. Current studies on this task are still very few. In this letter, we propose a two-stage deployment strategy for this task. To implement the strategy, we propose a Bat Algorithm-based meta-heuristic algorithm named Smart Bat Algorithm (SBA). The SBA enhances the searching behaviors of artificial bats to be smarter and more efficient by leveraging decision theory and fuzzy logic techniques. The simulation results indicate that the SBA delivers better and more robust solutions compared with three other popular meta-heuristic algorithms. [ABSTRACT FROM AUTHOR]

Nowadays, the development of smart grids has been the focus of attention due to its advantages for power systems. One of the aspects of smart grids defined by using distributed generation (DG) in a low voltage network is a microgrid (MG). Based on its operational states, MG can operate in different configurations such as grid-connected mode or off-grid mode. The switching between these states is one of the challenging issues in this technical area. The fault currents in different buses have higher value compared to islanded mode of MG when the MG is connected to the main grid, which influences the protection equipment. In this situation, some electrical devices may be damaged due to the fault currents. Application of a fault current limiter (FCL) is considered as an effective way to overcome this challenge. The optimal size of these FCLs can optimize the performance of an MG. In this paper, an index for FCL size optimization has been used. In addition, two optimization algorithms (Bat Algorithm and Cuckoo Search Algorithm) have been applied to the problem. The application of an FCL has been studied in grid-connected and islanded-mode. In addition, the application of the capacitor bank in both modes has been investigated. The results of simulations carried out by MATLAB have been presented and compared. [ABSTRACT FROM AUTHOR]

Based on the bat algorithm (BA), this paper proposes a discrete BA (DBA) approach to optimize the disassembly sequence planning (DSP) problem, for the purpose of obtaining an optimum disassembly sequence (ODS) of a product with a high degree of automation and guiding maintenance operation. The BA for solving continuous problems is introduced, and combining with mathematical formulations, the BA is reformed to be the DBA for DSP problems. The fitness function model (FFM) is built to evaluate the quality of disassembly sequences. The optimization performance of the DBA is tested and verified by an application case, and the DBA is compared with the genetic algorithm (GA), particle swarm optimization (PSO) algorithm and differential mutation BA (DMBA). Numerical experiments show that the proposed DBA has a better optimization capability and provides more accurate solutions than the other three algorithms. [ABSTRACT FROM AUTHOR]