Abstract
Quantum-behaved particle swarm optimization (QPSO) is an efficient and powerful population-based optimization technique, which is inspired by the conventional particle swarm optimization (PSO) and quantum mechanics theories. In this paper, an improved QPSO named SQPSO is proposed, which combines QPSO with a selective probability operator to solve the economic dispatch (ED) problems with valve-point effects and multiple fuel options. To show the performance of the proposed SQPSO, it is tested on five standard benchmark functions and two ED benchmark problems, including a 40-unit ED problem with valve-point effects and a 10-unit ED problem with multiple fuel options. The results are compared with differential evolution (DE), particle swarm optimization (PSO) and basic QPSO, as well as a number of other methods reported in the literature in terms of solution quality, convergence speed and robustness. The simulation results confirm that the proposed SQPSO is effective and reliable for both function optimization and ED problems.
Highlights
Economic dispatch (ED) is considered to be one of the key functions in electric power system operation
The convergence of the algorithm depends heavily on the value of its control parameters. Taking advantage of both PSO mechanism and quantum mechanics, in 2004, a new version of PSO, quantum-behaved particle swarm optimization, named Quantum-behaved particle swarm optimization (QPSO), was proposed by Sun, Xu and Feng [8], which is inspired by quantum mechanics and trajectory analysis of PSO
Where FT is the total generation cost, n is the total number of generating unit, Pi is the power of the ith generator and Fi is its corresponding fuel cost, which is defined by the following equation as: Fi ( Pi ) ai bi Pi ci Pi 2 (2)
Summary
Economic dispatch (ED) is considered to be one of the key functions in electric power system operation. The convergence of the algorithm depends heavily on the value of its control parameters Taking advantage of both PSO mechanism and quantum mechanics, in 2004, a new version of PSO, quantum-behaved particle swarm optimization, named QPSO, was proposed by Sun, Xu and Feng [8], which is inspired by quantum mechanics and trajectory analysis of PSO. Based on the selective probability operator, pbest and gbest are used to generate the local attractor of QPSO, with user defined selective probability, to enhance the local search performance This modification on the original QPSO together with a recombination operator will maintain the best information of the swarm and, in the same time, exchange information between individuals to increase the population diversity.
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