Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/16785
Title: Feedback controlled particle swarm optimization and its application in time-series prediction
Authors: Wong, WK 
Leung, SYS 
Guo, ZX
Keywords: Feedback control
Particle swarm optimization
Time-series prediction
Issue Date: 2012
Publisher: Pergamon Press
Source: Expert systems with applications, 2012, v. 39, no. 10, p. 8557-8572 How to cite?
Journal: Expert systems with applications 
Abstract: Particle swarm optimization (PSO) algorithm is an algorithmic technique for optimization by solving a wide range of optimization problems. This paper presents a new approach of extending PSO to solve optimization problems by using the feedback control mechanism (FCPSO). The proposed FCPSO consists of two major steps. First, by evaluating the fitness value of each particle, a simple particle evolutionary fitness function is designed to control parameters involving acceleration coefficient, refreshing gap, learning probabilities and number of the potential exemplars automatically. By such a simple particle evolutionary fitness function, each particle has its own search parameters in a search environment. Secondly, a local learning method using a competitive penalized method is developed to refine the solution. The FCPSO has been comprehensively evaluated on 18 unimodal, multimodal and composite benchmark functions with or without rotation. Compared with various state-of-the-art algorithms, including traditional PSO algorithms and representative variants of PSO algorithms, the performance of FCPSO is promising. The effects of parameter adaptation, parameter sensitivity and local search method are studied. Lastly, the proposed FCPSO is applied to constructing a radial basis neural network, together with the K-means method for time-series prediction.
URI: http://hdl.handle.net/10397/16785
ISSN: 0957-4174
EISSN: 1873-6793
DOI: 10.1016/j.eswa.2012.01.126
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