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

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

Author(s) -

Bai Li

Publication year - 2014

Publication title -

computational intelligence and neuroscience

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.605

H-Index - 52

eISSN - 1687-5273

pISSN - 1687-5265

DOI - 10.1155/2014/270658

Subject(s) - benchmark (surveying) , roulette , computer science , artificial bee colony algorithm , convergence (economics) , artificial neural network , algorithm , artificial intelligence , selection (genetic algorithm) , fitness proportionate selection , scheme (mathematics) , mathematical optimization , machine learning , mathematics , genetic algorithm , geometry , geodesy , economic growth , economics , geography , mathematical analysis , fitness function

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.

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