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Computational Intelligence and Neuroscience
Volume 2014, Article ID 270658, 10 pages
Research Article

Research on WNN Modeling for Gold Price Forecasting Based on Improved Artificial Bee Colony Algorithm

School of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China

Received 29 October 2013; Revised 19 November 2013; Accepted 11 December 2013; Published 13 February 2014

Academic Editor: Jianwei Shuai

Copyright © 2014 Bai Li. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Gold price forecasting has been a hot issue in economics recently. In this work, wavelet neural network (WNN) combined with a novel artificial bee colony (ABC) algorithm is proposed for this gold price forecasting issue. In this improved algorithm, the conventional roulette selection strategy is discarded. Besides, the convergence statuses in a previous cycle of iteration are fully utilized as feedback messages to manipulate the searching intensity in a subsequent cycle. Experimental results confirm that this new algorithm converges faster than the conventional ABC when tested on some classical benchmark functions and is effective to improve modeling capacity of WNN regarding the gold price forecasting scheme.