scholarly journals Novel Back Propagation Optimization by Cuckoo Search Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Jiao-hong Yi ◽  
Wei-hong Xu ◽  
Yuan-tao Chen
Keyword(s):  
Search Algorithm ◽  
Back Propagation ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Prediction Performance ◽  
General Regression Neural Network ◽  
Parameter Study ◽  
Local Minima ◽  
Bp Network ◽  
General Regression

The traditional Back Propagation (BP) has some significant disadvantages, such as training too slowly, easiness to fall into local minima, and sensitivity of the initial weights and bias. In order to overcome these shortcomings, an improved BP network that is optimized by Cuckoo Search (CS), called CSBP, is proposed in this paper. In CSBP, CS is used to simultaneously optimize the initial weights and bias of BP network. Wine data is adopted to study the prediction performance of CSBP, and the proposed method is compared with the basic BP and the General Regression Neural Network (GRNN). Moreover, the parameter study of CSBP is conducted in order to make the CSBP implement in the best way.

Bioinspired multiobjective synthesis of X-band FSS via general regression neural network and cuckoo search algorithm

2015 ◽  
Vol 57 (10) ◽  
pp. 2400-2405 ◽  
Author(s):  
M. C. Alcantara Neto ◽  
J. P. L. Araújo ◽  
F. J. B. Barros ◽  
A. N. Silva ◽  
G. P. S. Cavalcante ◽  
...  
Keyword(s):  
Neural Network ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
General Regression Neural Network ◽  
X Band ◽  
General Regression

scholarly journals Optimization and synthesis of multilayer frequency selective surfaces via bioinspired hybrid techniques

2020 ◽  
Vol 8 (5) ◽  
pp. 542-561
Author(s):  
Wirlan Gomes Lima ◽  
Jasmine Priscyla Leite Leite de Araújo ◽  
Fabrício José Brito Barros ◽  
Gervásio Protásio Dos Santos Cavalcante ◽  
Cássio da Cruz Nogueira ◽  
...  
Keyword(s):  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
General Regression Neural Network ◽  
Electromagnetic Properties ◽  
Frequency Selective Surfaces ◽  
Hybrid Techniques ◽  
Operating Frequency Range ◽  
Frequency Selective ◽  
General Regression

In this study, two bioinspired computation (BIC) techniques are discussed and applied to the project and synthesis of multilayer frequency selective surfaces (FSS) within the microwave band, specifically for C, X and Ku bands. The proposed BIC techniques consist of combining an artificial, general regression neural network to a genetic algorithm (GA) and a cuckoo search algorithm, respectively. The objective is to find the optimal values of separation between the investigated FSS. Numerical analysis of the electromagnetic properties of the device is made possible with the finite integration method (FIT) and validated through the finite element method (FEM), utilizing both softwares CST Microwave Studio and Ansys HFSS respectively. Thus, the BIC-optimized devices present good phase / angular stability for angles 10°, 20°, 30° and 40°, as well as being polarization independent. The cutoff frequencies to control the operating frequency range of the FSS, referring to transmission coefficient in decibels (dB), were obtained at a threshold of –10dB. Numerical results denote good accordance with measured data.

scholarly journals Weight Optimization in Recurrent Neural Networks with Hybrid Metaheuristic Cuckoo Search Techniques for Data Classification

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Nazri Mohd Nawi ◽  
Abdullah Khan ◽  
M. Z. Rehman ◽  
Haruna Chiroma ◽  
Tutut Herawan
Keyword(s):  
Gradient Descent ◽  
Search Algorithm ◽  
Error Function ◽  
Back Propagation ◽  
Data Classification ◽  
Cuckoo Search ◽  
Recurrent Network ◽  
Training Algorithms ◽  
Local Minima ◽  
Classification Problems

Recurrent neural network (RNN) has been widely used as a tool in the data classification. This network can be educated with gradient descent back propagation. However, traditional training algorithms have some drawbacks such as slow speed of convergence being not definite to find the global minimum of the error function since gradient descent may get stuck in local minima. As a solution, nature inspired metaheuristic algorithms provide derivative-free solution to optimize complex problems. This paper proposes a new metaheuristic search algorithm called Cuckoo Search (CS) based on Cuckoo bird’s behavior to train Elman recurrent network (ERN) and back propagation Elman recurrent network (BPERN) in achieving fast convergence rate and to avoid local minima problem. The proposed CSERN and CSBPERN algorithms are compared with artificial bee colony using BP algorithm and other hybrid variants algorithms. Specifically, some selected benchmark classification problems are used. The simulation results show that the computational efficiency of ERN and BPERN training process is highly enhanced when coupled with the proposed hybrid method.

scholarly journals A Hybrid Forecasting Model Based on Empirical Mode Decomposition and the Cuckoo Search Algorithm: A Case Study for Power Load

2016 ◽  
Vol 2016 ◽  
pp. 1-28 ◽  
Author(s):  
Jiani Heng ◽  
Chen Wang ◽  
Xuejing Zhao ◽  
Jianzhou Wang
Keyword(s):  
Neural Network ◽  
Search Algorithm ◽  
Back Propagation ◽  
Cuckoo Search ◽  
Back Propagation Neural Network ◽  
Cuckoo Search Algorithm ◽  
Mode Decomposition ◽  
Power Load ◽  
Hybrid Forecasting

Power load forecasting always plays a considerable role in the management of a power system, as accurate forecasting provides a guarantee for the daily operation of the power grid. It has been widely demonstrated in forecasting that hybrid forecasts can improve forecast performance compared with individual forecasts. In this paper, a hybrid forecasting approach, comprising Empirical Mode Decomposition, CSA (Cuckoo Search Algorithm), and WNN (Wavelet Neural Network), is proposed. This approach constructs a more valid forecasting structure and more stable results than traditional ANN (Artificial Neural Network) models such as BPNN (Back Propagation Neural Network), GABPNN (Back Propagation Neural Network Optimized by Genetic Algorithm), and WNN. To evaluate the forecasting performance of the proposed model, a half-hourly power load in New South Wales of Australia is used as a case study in this paper. The experimental results demonstrate that the proposed hybrid model is not only simple but also able to satisfactorily approximate the actual power load and can be an effective tool in planning and dispatch for smart grids.

scholarly journals Optimized cuckoo search algorithm using tournament selection function for robot path planning

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142199613
Author(s):  
Kaushlendra Sharma ◽  
Shikha Singh ◽  
Rajesh Doriya
Keyword(s):  
Path Planning ◽  
Path Length ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Selection Function ◽  
Local Minima ◽  
Robot Path Planning ◽  
Tournament Selection ◽  
Robot Path

Acceptability of mobile robots in various applications has led to an increase in mobile robots’ research areas. Path planning is one of the core areas which needs to be improvised at a higher level. Optimization is playing a more prominent role these days. The nature-inspired algorithm is contributing to a greater extent in achieving optimization. This article presents the modified cuckoo search algorithm using tournament selection function for robot path planning. Path length and Path time are the algorithm’s parameters to validate the effectiveness and acceptability of the output. The cuckoo search algorithm’s fundamental working principle is taken as the baseline, and the tournament selection function is adapted to calculate the optimum path for robots while navigating from its initial position to final position. The tournament selection function is replacing the concept of random selection done by the cuckoo search algorithm. The use of tournament selection overcomes local minima for robots while traversing in the configuration space and increases the probability of giving more optimum results. The conventional cuckoo search algorithm whose random selection mechanism may lead to premature convergence may fall into the local minima. The use of tournament selection function increases the probability of giving better results as it allows all the possible solution to take part in the tournament. The results are analysed and compared with other relevant work like cuckoo search algorithm and particle swarm optimization technique and presented in the article. The proposed method produced a better output in terms of path length and path time.

scholarly journals A Real-time BOD Estimation Method in Wastewater Treatment Process Based on an Optimized Extreme Learning Machine

2019 ◽  
Vol 9 (3) ◽  
pp. 523 ◽  
Author(s):  
Ping Yu ◽  
Jie Cao ◽  
Veeriah Jegatheesan ◽  
Xianjun Du
Keyword(s):  
Wastewater Treatment ◽  
Real Time ◽  
Extreme Learning Machine ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Bp Network ◽  
Learning Machine ◽  
Hidden Layer ◽  
Improved Cuckoo Search Algorithm

It is difficult to capture the real-time online measurement data for biochemical oxygen demand (BOD) in wastewater treatment processes. An optimized extreme learning machine (ELM) based on an improved cuckoo search algorithm (ICS) is proposed in this paper for the design of soft BOD measurement model. In ICS-ELM, the input weights matrices of the extreme learning machine and the threshold of the hidden layer are encoded as the cuckoo's nest locations. The best input weights matrices and threshold are obtained by using the strong global search ability of improved cuckoo search algorithm. The optimal results can be used to improve the precision of forecasting based on less number of neurons of the hidden layer in ELM. Simulation results show that the soft sensor model has good real-time performance, high prediction accuracy, and stronger generalization performance for BOD measurement of the effluent quality compared to other modeling methods such as back propagation (BP) network in most cases.

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