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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]

This paper presents a maximum power point tracking (MPPT) method for photovoltaic system under partial shading conditions using bat algorithm (BA). The bat algorithm is a swarm intelligence based method which was inspired by the echolocation behaviour of bats. BA have a high accuracy in the global optimisation and it can provide good dynamic performance and very quick convergence rate by automatically switching between exploration and exploitation stages during the MPPT process. To verify the performance of the proposed method, several simulations have been carried out in Matlab/Simulink environment for various shading patterns. The simulations results highlight the accuracy of the proposed scheme for optimal management of the energy available at the output of the photovoltaic panels. In addition, the comparison with the P & O and the PSO methods shows that the proposed method outperforms them in term of global search ability and dynamic performance. To verify the practical implementation of the proposed method, a modular reconfigurable architecture is designed using very high speed integrated circuit hardware description language (VHDL) and implemented on Xilinx Virtex-5 (XC5VLX50-1FFG676) Field Programmable Gate Array (FPGA). The use of FPGA for designing the MPPT controller provides high performance, increases the robustness and makes the hardware implementation more flexible. The algorithm is tested in real time application on a buck-boost converter using a real photovoltaic panel. Experimental results confirm the efficiency of the proposed method in the global peak tracking and its high accuracy to handle the partial shading. [ABSTRACT FROM AUTHOR]

Vitamin D deficiency is prevalent in the Arabian Gulf region, especially among women. Recent studies show that the vitamin D deficiency is associated with a mineral status of a patient. Therefore, it is important to assess the mineral status of the patient to reveal the hidden mineral imbalance associated with vitamin D deficiency. A well-known test such as the red blood cells is fairly expensive, invasive, and less informative. On the other hand, a hair mineral analysis can be considered an accurate, excellent, highly informative tool to measure mineral imbalance associated with vitamin D deficiency. In this study, 118 apparently healthy Kuwaiti women were assessed for their mineral levels and vitamin D status by a hair mineral analysis (HMA). This information was used to build a computerized model that would predict vitamin D deficiency based on its association with the levels and ratios of minerals. The first phase of the proposed model introduces a novel hybrid optimization algorithm, which can be considered as an improvement of Bat Algorithm (BA) to select the most discriminative features. The improvement includes using the mutation process of Genetic Algorithm (GA) to update the positions of bats with the aim of speeding up convergence; thus, making the algorithm more feasible for wider ranges of real-world applications. Due to the imbalanced class distribution in our dataset, in the second phase, different sampling methods such as Random Under-Sampling, Random Over-Sampling, and Synthetic Minority Oversampling Technique are used to solve the problem of imbalanced datasets. In the third phase, an AdaBoost ensemble classifier is used to predicting the vitamin D deficiency. The results showed that the proposed model achieved good results to detect the deficiency in vitamin D. [ABSTRACT FROM AUTHOR]

Support Vector Machine (SVM) parameters such as kernel parameter and penalty parameter ( C ) have a great impact on the complexity and accuracy of predicting model. In this paper, Bat algorithm (BA) has been proposed to optimize the parameters of SVM, so that the classification error can be reduced. To evaluate the proposed model (BA-SVM), the experiment adopted nine standard datasets which are obtained from UCI machine learning data repository. For verification, the results of the BA-SVM algorithm are compared with grid search, which is a conventional method of searching parameter values, and two well-known optimization algorithms: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The experimental results proved that the proposed model is capable to find the optimal values of the SVM parameters and avoids the local optima problem. The results also demonstrated lower classification error rates compared with PSO and GA algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes a novel robust power system stabilizer (PSS), based on hybridization of fractional order PID controller (PI λ D μ ) 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. [ABSTRACT FROM AUTHOR]

As a challenging high dimension optimization problem, three-dimensional path planning for Uninhabited Combat Air Vehicles (UCAV) mainly centralizes on optimizing the flight route with different types of constrains under complicated combating environments. An improved version of Bat Algorithm (BA) in combination with a Differential Evolution (DE), namely IBA, is proposed to optimize the UCAV three-dimensional path planning problem for the first time. In IBA, DE is required to select the most suitable individual in the bat population. By connecting the selected nodes using the proposed IBA, a safe path is successfully obtained. In addition, B-Spline curves are employed to smoothen the path obtained further and make it practically more feasible for UCAV. The performance of IBA is compared to that of the basic BA on a 3-D UCAV path planning problem. The experimental results demonstrate that IBA is a better technique for UCAV three-dimensional path planning problems compared to the basic BA model. [ABSTRACT FROM AUTHOR]

Abstract: Joint maximum likelihood (ML) estimation of multiple parameters is an important problem with wide-spread relevance in many domains. The high computational complexity involved in joint ML problems has led to the search for more efficient methods. Efficient heuristic algorithms for joint ML problems can be developed by exploiting the characteristics of the objective functions used in the estimation problem. This paper proposes a novel reformulation of existing heuristic algorithms, which considerably reduces their computational complexity with significant improvement in performance. The method is applicable for joint maximum likelihood estimation problems, with cost functions that exhibit asymptotic separability with increase in observation vector size. The proposed method is adopted to five recently discovered heuristic algorithms and consequently applied to a relevant recent signal processing problem in wireless communication. It is found that the reformulated algorithms deliver both reduced computational complexity as well as better mean square error (MSE) performance. The significant features of the proposed method are substantiated through extensive computer simulation studies. [Copyright &y& Elsevier]

• Implementation of an optimization method to achieve the minimum number of VWDs. • BA & DA can be successfully employed in the position optimization of VWDs. • A better structural response was obtained by optimized number and placement of VWDs. • Large damping can result in increased floor accelerations. Viscous wall dampers (VWDs) are one of the most effective energy dissipation devices for the seismic performance enhancement of buildings. The number of VWDs as well as their positions in the building directly affects the seismic performance. In this study, two of the metaheuristic algorithms were employed for the optimal placement of VWDs in a reinforced concrete (RC) special moment resisting frame (SMRF) in order to achieve a safe yet cost effective solution. Bat algorithm (BA) and dragonfly algorithm (DA) were adopted to achieve the minimum number of VWDs by placing them in the most suitable positions of a 15 story RC SMRF. The floor accelerations and inter-story drift ratios (ISDRs) are considered as the constraints for the position optimization of VWDs with the objective of minimum number of VWDs. The application-programming interface (API) was employed for the interaction and data transfer between the structural analysis program ETABS and the Matlab program for optimization operations. Nonlinear response history analyses (NRHA) were conducted using recorded earthquake ground motions. Optimal placement for the minimum number of VWDs was achieved by using metaheuristic search algorithms. The additional damping provided by VWDs is effective in reducing the max ISDRs. However, higher damping ratios do not always result in the minimum floor accelerations. Therefore, an optimum damping ratio provided by VWDs needs to be sought for achieving minimum floor accelerations. [ABSTRACT FROM AUTHOR]