TY - GEN
T1 - A particle swarm optimization algorithm based on orthogonal design
AU - Yang, Jie
AU - Bouzerdoum, Abdesselam
AU - Phung, Son Lam
PY - 2010
Y1 - 2010
N2 - The last decade has witnessed a great interest in using evolutionary algorithms, such as genetic algorithms, evolutionary strategies and particle swarm optimization (PSO), for multivariate optimization. This paper presents a hybrid algorithm for searching a complex domain space, by combining the PSO and orthogonal design. In the standard PSO, each particle focuses only on the error propagated back from the best particle, without "communicating" with other particles. In our approach, this limitation of the standard PSO is overcome by using a novel crossover operator based on orthogonal design. Furthermore, instead of the "generating-and-updating" model in the standard PSO, the elitism preservation strategy is applied to determine the possible movements of the candidate particles in the subsequent iterations. Experimental results demonstrate that our algorithm has a better performance compared to existing methods, including five PSO algorithms and three evolutionary algorithms.
AB - The last decade has witnessed a great interest in using evolutionary algorithms, such as genetic algorithms, evolutionary strategies and particle swarm optimization (PSO), for multivariate optimization. This paper presents a hybrid algorithm for searching a complex domain space, by combining the PSO and orthogonal design. In the standard PSO, each particle focuses only on the error propagated back from the best particle, without "communicating" with other particles. In our approach, this limitation of the standard PSO is overcome by using a novel crossover operator based on orthogonal design. Furthermore, instead of the "generating-and-updating" model in the standard PSO, the elitism preservation strategy is applied to determine the possible movements of the candidate particles in the subsequent iterations. Experimental results demonstrate that our algorithm has a better performance compared to existing methods, including five PSO algorithms and three evolutionary algorithms.
UR - http://www.scopus.com/inward/record.url?scp=79959452614&partnerID=8YFLogxK
U2 - 10.1109/CEC.2010.5586126
DO - 10.1109/CEC.2010.5586126
M3 - Conference contribution
AN - SCOPUS:79959452614
SN - 9781424469109
T3 - 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010
BT - 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010
T2 - 2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010
Y2 - 18 July 2010 through 23 July 2010
ER -