Name
Title | ||
|---|---|---|
Feedback controlled particle swarm optimization and its application in time-series prediction |
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.eswa.2012.01.126 | Expert Syst. Appl. |
Keywords | Field | DocType |
particle swarm optimization,local learning method,time-series prediction,pso algorithm,evolutionary fitness function,local search method,k-means method,optimization problem,competitive penalized method,simple particle,proposed fcpso,feedback control,time series prediction | Particle swarm optimization,Mathematical optimization,Computer science,Fitness function,Multi-swarm optimization,Fitness approximation,Artificial intelligence,Local search (optimization),Imperialist competitive algorithm,Optimization problem,Machine learning,Metaheuristic | Journal |
Volume | Issue | ISSN |
39 | 10 | 0957-4174 |
Citations | PageRank | References |
4 | 0.42 | 32 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| W. K. Wong | 1 | 957 | 49.71 |
| S. Y. S. Leung | 2 | 227 | 13.99 |
| Z. X. Guo | 3 | 80 | 6.54 |