Title :
Quantum-Behaved Particle Swarm Optimization with Cooperative Coevolution for Large Scale Optimization
Author_Institution :
Dept. of Educ. Technol., Jiangnan Univ., Wuxi, China
Abstract :
Quantum-behaved particle swarm optimization (QPSO) has successfully been applied to unimodal and multimodal optimization problems. However, with the emerging and popular of big data and deep machine learning, QPSO encounters limitations with high dimensions. In this paper, QPSO with cooperative co evolution (QPSO_CC) is used to decompose the high dimensional problems into several lower dimensional problems and optimize them separately. The numerical experimental results show that QPSO_CC has comparative or even better performance than other algorithms.
Keywords :
"Particle swarm optimization","Optimization","Context","Quantum computing","Sun","Genetic algorithms","Benchmark testing"
Conference_Titel :
Distributed Computing and Applications for Business Engineering and Science (DCABES), 2015 14th International Symposium on
DOI :
10.1109/DCABES.2015.28
