A hybrid quantum-inspired particle swarm evolution algorithm and SQP method for large-scale economic dispatch problems

Authors:
Qun Niu;Zhuo Zhou;Tingting Zeng
Affiliations:
Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China;Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China;Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China
Venue:
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
Year:
2011

Citing 3
Cited 0

A novel quantum swarm evolutionary algorithm and its applications

Neurocomputing
Quantum-inspired evolutionary algorithm for a class of combinatorial optimization

IEEE Transactions on Evolutionary Computation
Evolutionary programming techniques for economic load dispatch

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes a hybrid method QPSO-SQP, which combines a quantum-inspired particle swarm evolution algorithm(QPSO) and the sequential quadratic programming (SQP) method to solve large-scale economic dispatch problems(EDPs). Due to the combination of quantum rotation gates and the updating mechanism of PSO, the QPSO has strong search ability and fast convergence speed, therefore it is employed as a global searcher to obtain good solutions for EDPs. As SQP is a gradient-based nonlinear programming method, it is used as a local optimizer to fine tune the best result of the QPSO. The proposed QPSO-SQP is applied to two large-scale EDPs to validate its effectiveness. The experiment results show that the proposed QPSO-SQP can obtain high-quality solutions and produce a satisfactory performance among most existing techniques.