A new cuckoo search based levenberg-marquardt (CSLM) algorithm

Authors:
Nazri Mohd. Nawi;Abdullah Khan;Mohammad Zubair Rehman
Affiliations:
Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia (UTHM), Batu Pahat, Johor Darul Takzim, Malaysia;Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia (UTHM), Batu Pahat, Johor Darul Takzim, Malaysia;Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia (UTHM), Batu Pahat, Johor Darul Takzim, Malaysia
Venue:
ICCSA'13 Proceedings of the 13th international conference on Computational Science and Its Applications - Volume 1
Year:
2013

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Abstract

Back propagation neural network (BPNN) algorithm is a widely used technique in training artificial neural networks. It is also a very popular optimization procedure applied to find optimal weights in a training process. However, traditional back propagation optimized with Levenberg marquardt training algorithm has some drawbacks such as getting stuck in local minima, and network stagnancy. This paper proposed an improved Levenberg-Marquardt back propagation (LMBP) algorithm integrated and trained with Cuckoo Search (CS) algorithm to avoided local minima problem and achieves fast convergence. The performance of the proposed Cuckoo Search Levenberg-Marquardt (CSLM) algorithm is compared with Artificial Bee Colony (ABC) and similar hybrid variants. The simulation results show that the proposed CSLM algorithm performs better than other algorithm used in this study in term of convergence rate and accuracy.