Performance Optimization of Elliptical Flexure Hinge Using a Modified Particle Swarm Algorithm

Chen, Guimin; Jia, Jianyuan; Han, Qi; Wang, Yingkun

doi:10.1109/icit.2005.1600814

Cited by 1 publication

(1 citation statement)

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“…In particle swarm optimization, the selection of inertia factor and the choice of acceleration constant determine whether the particle swarm will fall into local optimum, the convergence speed of the algorithm and whether there will be oscillation phenomenon [6][7][8]. This paper selects the nonlinear inertia factor change method to optimize Particle swarm algorithm.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Power load forecasting algorithm based on nonlinear inertial factor change pattern particle swarm optimization algorithm

2018

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Abstract. The common method of power load forecasting is the least squares support vector machine, but this method is very dependent on the selection of parameters. Particle swarm optimization algorithm is an algorithm suitable for optimizing the selection of support vector parameters, but it is easy to fall into the local optimum. In this paper, we propose a new particle swarm optimization algorithm, it uses non-linear inertial factor change that is used to optimize the algorithm least squares support vector machine to avoid falling into the local optimum. It aims to make the prediction accuracy of the algorithm reach the highest. The experimental results show this method is correct and effective.

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Section: Particle Swarm Optimizationmentioning

confidence: 99%