Improving quantum-behaved particle swarm optimization by simulated annealing

Authors:
Jing Liu;Jun Sun;Wenbo Xu
Affiliations:
School of Information Technology, Southern Yangtze University, Wuxi, Jiangsu, China;School of Information Technology, Southern Yangtze University, Wuxi, Jiangsu, China;School of Information Technology, Southern Yangtze University, Wuxi, Jiangsu, China
Venue:
ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
Year:
2006

Citing 2
Cited 4

Genetic Algorithms and Simulated Annealing

Genetic Algorithms and Simulated Annealing
The particle swarm - explosion, stability, and convergence in amultidimensional complex space

IEEE Transactions on Evolutionary Computation

Quantum-Behaved Particle Swarm Optimization with Chaotic Search

IEICE - Transactions on Information and Systems
Quantum-Behaved Particle Swarm Optimization with Generalized Local Search Operator for Global Optimization

ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Quantum-inspired evolutionary algorithm: a multimodel EDA

IEEE Transactions on Evolutionary Computation - Special issue on evolutionary algorithms based on probabilistic models
A quantum particle swarm optimization used for spatial clustering with obstacles constraints

ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

Quantum-behaved Particle Swarm Optimization (QPSO) is a global convergence guaranteed search method, which introduced quantum theory into original Particle Swarm Optimization (PSO). While Simulated Annealing (SA) is another important stochastic optimization with the ability of probabilistic hill-climbing. In this paper, the mechanism of Simulated Annealing is introduced into the weak selection implicit in our QPSO algorithm, which effectively employs both the ability to jump out of the local minima in Simulated Annealing and the capacity of searching the global optimum in QPSO algorithm. The experimental results show that the proposed hybrid algorithm increases the diversity of the population in the search process and improves its precision in the latter period of the search.