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.
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Niu, Q., Zhou, Z., Zeng, T. (2012). A Hybrid Quantum-Inspired Particle Swarm Evolution Algorithm and SQP Method for Large-Scale Economic Dispatch Problems. In: Huang, DS., Gan, Y., Premaratne, P., Han, K. (eds) Bio-Inspired Computing and Applications. ICIC 2011. Lecture Notes in Computer Science(), vol 6840. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24553-4_29
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DOI: https://doi.org/10.1007/978-3-642-24553-4_29
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