Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/24500
Title: Quantum-inspired particle swarm optimization for valve-point economic load dispatch
Authors: Meng, K
Wang, HG
Dong, Z
Wong, KP
Keywords: Economic load dispatch
Quantum-inspired particle swarm optimization
Issue Date: 2010
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on power systems, 2010, v. 25, no. 1, 5299292, p. 215-222 How to cite?
Journal: IEEE transactions on power systems 
Abstract: Economic load dispatch (ELD) is an important topic in the operation of power plants which can help to build up effective generating management plans. The ELD problem has nonsmooth cost function with equality and inequality constraints which make it difficult to be effectively solved. Different heuristic optimization methods have been proposed to solve this problem in previous study. In this paper, quantum-inspired particle swarm optimization (QPSO) is proposed, which has stronger search ability and quicker convergence speed, not only because of the introduction of quantum computing theory, but also due to two special implementations: self-adaptive probability selection and chaotic sequences mutation. The proposed approach is tested with five standard benchmark functions and three power system cases consisting of 3, 13, and 40 thermal units. Comparisons with similar approaches including the evolutionary programming (EP), genetic algorithm (GA), immune algorithm (IA), and other versions of particle swarm optimization (PSO) are given. The promising results illustrate the efficiency of the proposed method and show that it could be used as a reliable tool for solving ELD problems.
URI: http://hdl.handle.net/10397/24500
ISSN: 0885-8950
EISSN: 1558-0679
DOI: 10.1109/TPWRS.2009.2030359
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