Based on grey relational analysis, this paper attempts to propose a grey-based particle swarm optimization (PSO). The central idea of the proposed approach is that the determination of the algorithm parameters (the inertia weight and the acceleration coefficients) for a particle is depended upon the grey relational grade of that particle. The algorithm parameters are varying over the generations. Also they may differ from different particles. Besides, the grey-based PSO algorithm is also applied to optimize the parameters of the proportional-integral-derivative (PID) controller. Simulation and experiment results are compared with the standard PSO algorithm and genetic algorithm to demonstrate the search performance of the proposed method.
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