This paper presents chaotic bat algorithm to solve dynamic economic dispatch (DED) problem. Various load in 4 hours considering to solve the DED problem which focused on minimizing the total cost of generation power i...
详细信息
ISBN:
(纸本)9781538627082
This paper presents chaotic bat algorithm to solve dynamic economic dispatch (DED) problem. Various load in 4 hours considering to solve the DED problem which focused on minimizing the total cost of generation power in each hour. To demonstrate the effectiveness of the proposed method, the DED problem performed on 150 kV Mahakam power system on East Kalimantan and compared with bat algorithm and particle swarm optimization. The result shows the proposed method provide a good and reliable result compared to the other method used in this paper.
This paper proposes an intelligent motor rotary fault diagnosis system using the dynamic structural neural network In order to improve the convergence efficiency, the terminal attractors integrated with the bat algori...
详细信息
ISBN:
(纸本)9781538666500
This paper proposes an intelligent motor rotary fault diagnosis system using the dynamic structural neural network In order to improve the convergence efficiency, the terminal attractors integrated with the bat algorithm is applied to the motor rotary fault detection. The optimization parameter, learning rate, is set as the positions of bats is optimized by the dynamic adjustment mechanism. Also, the MATLAB R2010b software is used to establish the bat algorithm based terminal attractor learning method. By conducting numerical simulations of training neural networks, the efficiency of the proposed algorithm and other methods are compared and analyzed. Finally, this work uses the method to fulfill the module of motor fault diagnosis. From the results of motor fault simulation and analysis, the proposed method increases the learning speed and reduce the learning error of fault diagnosis.
This paper proposes a hybrid bat-BP approach based on dissolved gas-in-oil data set (DGA) to optimize the structure of back propagation neural network (BPNN). BPNN is a multilayer feed forward neural network. The rule...
详细信息
ISBN:
(纸本)9781538660577
This paper proposes a hybrid bat-BP approach based on dissolved gas-in-oil data set (DGA) to optimize the structure of back propagation neural network (BPNN). BPNN is a multilayer feed forward neural network. The rule of local decline that BPNN used is easy to fall into local optimum. bat algorithm is a metaheuristic bionic algorithm with great local performance, which is adopted to optimize the initial value of BPNN. The recommended bat-BP method has been employed in power transformer fault diagnosis for the first time. To prove the proposed method has better ability of power transformer fault diagnosis, this paper compares the fitness of bat-BP with BPNN and other optimized approaches including PSO-BP, GA-BP based on the same DGA data set. The mean squared error (MSE) is used in this paper to evaluate the performance of the total four methods. The experimental results show the bat-BP has increased the fault diagnosis accuracy from 75.68% to 95.22%, which is higher than those optimized models.
The learning time of the synaptic weights for feedforward neural networks tend to be very long. In order to reduce the learning time, in this paper we propose a new learning algorithm for learning the synaptic weights...
详细信息
ISBN:
(纸本)9781450349390
The learning time of the synaptic weights for feedforward neural networks tend to be very long. In order to reduce the learning time, in this paper we propose a new learning algorithm for learning the synaptic weights of the single-hidden-layer feedforward neural networks by combining the upgraded bat algorithm with the extreme learning machine. The proposed approach can efficiently search for the optimal input weights as well as the hidden biases, leading to the reduced number of evaluations needed to train a neural network. The experimental results based on classification problems and comparison with other approaches from literature have shown that the proposed algorithm produces a satisfactory performance in almost all cases and that it can learn the weight factors much faster than the traditional learning algorithms.
The problem of system identification concerns with the design of adaptive infinite impulse response (IIR) system by determining the optimal system parameters of the unknown system on the minimization of error fitness ...
详细信息
The problem of system identification concerns with the design of adaptive infinite impulse response (IIR) system by determining the optimal system parameters of the unknown system on the minimization of error fitness function. The conventional system identification techniques have stability issues and problem of degradation in performance when modeled using a reduced-order system. Hence, a meta-heuristic optimization method is applied to overcome such drawbacks. In this paper, a new meta-heuristic optimization algorithm, called bat algorithm (BA), is utilized for the design of an adaptive IIR system in order to approximate the unknown system. bat algorithm is inspired from the echolocation behavior of bats combining the advantages of existing optimization techniques. A proper tuning of control parameter has been performed in order to achieve a balance between intensification and diversification phases. The proposed BA method for system identification is free from the problems encountered in conventional techniques. To valuate the performance of the proposed method, mean square error, mean square deviation and computation time are measured. Simulations have been carried out considering four bench-marked IIR systems using the same-order and reduced-order systems. The results of the proposed BA method have been compared to that of the well known optimization methods such as genetic algorithm, particle swarm optimization and cat swarm optimization. The simulation results confirm that the proposed system identification method outperforms the existing system identification methods.
Obtaining accurate location information is important in practical applications of wireless sensor networks (WSNs). The distance vector hop (DV-Hop) is a frequently-used range-free localization algorithm in WSNs, but i...
详细信息
Obtaining accurate location information is important in practical applications of wireless sensor networks (WSNs). The distance vector hop (DV-Hop) is a frequently-used range-free localization algorithm in WSNs, but it has low localization accuracy. Moreover, despite various improvements to DV-Hop-based localization algorithms, maintaining a balance between high localization accuracy and good stability and convergence is still a challenge. To overcome these shortcomings, we proposed an improved DV-Hop localization algorithm based on the bat algorithm (IBDV-Hop) for WSNs. The IBDV-Hop algorithm incorporates optimization methods that enhance the accuracy of the average hop distance and fitness function. We also introduce a nonlinear dynamic inertial weight strategy to extend the global search scope and increase the local search accuracy. Moreover, we develop an updated solutions strategy that avoids premature convergence by the IBDV-Hop algorithm. Both theoretical analysis and simulation results show that the IBDV-Hop algorithm achieves higher localization accuracy than the original DV-Hop algorithm and other improved algorithms. The IBDV-Hop algorithm also exhibits good stability, search capability and convergence, and it requires little additional time complexity and energy consumption.
bat algorithm (BA) is a new meta-heuristic optimization algorithm that is inspired by the echolocation characteristics of bats with varying pulse rates of emission and loudness. BA has been proven to be a powerful too...
详细信息
bat algorithm (BA) is a new meta-heuristic optimization algorithm that is inspired by the echolocation characteristics of bats with varying pulse rates of emission and loudness. BA has been proven to be a powerful tool in solving a wide range of global optimization problems. In this study, visual tracking is considered to be a process of searching for target by various bats in sequential images. A BA-based tracking architecture is proposed and the sensitivity and adjustment of the parameters in BA are studied experimentally. To demonstrate the tracking ability of the proposed tracker, comparative studies of tracking accuracy and speed of the BA-based tracker with three representative trackers, namely, particle filter, meanshift and particle swarm optimization are presented. Comparative results show that the BA based tracker outperforms the other three trackers. (C) 2015 Elsevier B.V. All rights reserved.
In this paper, design of fuzzy proportional derivative controller and fuzzy proportional derivative integral controller for speed control of brushless direct current drive has been presented. Optimization of the above...
详细信息
In this paper, design of fuzzy proportional derivative controller and fuzzy proportional derivative integral controller for speed control of brushless direct current drive has been presented. Optimization of the above controllers design is carried out using nature inspired optimization algorithms such as particle swarm, cuckoo search, and bat algorithms. Time domain specifications such as overshoot, undershoot, settling time, recovery time, and steady state error and performance indices such as root mean squared error, integral of absolute error, integral of time multiplied absolute error and integral of squared error are measured and compared for the above controllers under different operating conditions such as varying set speed and load disturbance conditions. The precise investigation through simulation is performed using simulink toolbox. From the simulation test results, it is evident that bat optimized fuzzy proportional derivative controller has superior performance than the other controllers considered. Experimental test results have also been taken and analyzed for the optimal controller identified through simulation. (C) 2016, Karabuk University. Publishing services by Elsevier B.V.
This present approach suggests a bio-inspired bat algorithm for optimal sizing of Distribution STATic COMpensator (DSTATCOM) to mitigate the total power loss of the system in the radial distribution systems (RDS). In ...
详细信息
This present approach suggests a bio-inspired bat algorithm for optimal sizing of Distribution STATic COMpensator (DSTATCOM) to mitigate the total power loss of the system in the radial distribution systems (RDS). In the present approach, a new voltage stability factor (VSF) is utilized to identify the optimal placement for installation of DSTATCOM and the proposed VSF is compared with other stability indices. bat algorithm (BA) is used to search the optimal size of DSTATCOM. The backward/forward sweep (BFS) algorithm is established for the power flow calculations. To verify the feasibility of the proposed work, it has been implemented on standard IEEE 33-bus RDS. The outcomes obtained using the proposed method shows that the optimal location of DSTATCOM in RDS adequately mitigates the loss at the same time enhances the bus voltages.
This paper proposes a novel complex-valued encoding bat algorithm (CPBA) for solving 0-1 knapsack problem. The complex-valued encoding method which can be considered as an efficient global optimization strategy is int...
详细信息
This paper proposes a novel complex-valued encoding bat algorithm (CPBA) for solving 0-1 knapsack problem. The complex-valued encoding method which can be considered as an efficient global optimization strategy is introduced to the bat algorithm. Based on the two-dimensional properties of the complex number, the real and imaginary parts of complex number are updated separately. The proposed algorithm can effectively diversify bat population and improving the convergence performance. The CPBA enhances exploration ability and is effective for solving both small-scale and large-scale 0-1 knapsack problem. Finally, numerical simulation is carried out, and the comparison results with some existing algorithms demonstrate the validity and stability of the proposed algorithm.
暂无评论