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首页> 外文期刊>Proceedings of the Institution of Mechanical Engineers >A novel stable deviation quantum-behaved particle swarm optimization to optimal piezoelectric actuator and sensor location for active vibration control
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A novel stable deviation quantum-behaved particle swarm optimization to optimal piezoelectric actuator and sensor location for active vibration control

机译：一种新颖的稳定偏差量子行为粒子群算法，用于优化压电致动器和传感器位置，以进行主动振动控制

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摘要

In this article, a novel stable deviation quantum-behaved particle swarm optimization for solving global optimization problem is presented. The proposed stable deviation quantum-behaved particle swarm optimization algorithm uses interpolation and stable deviation function. The method of recombination operator is employed to generate a new solution vector in the search space. Therefore, the chance of fast converging can be improved. The performance of the proposed stable deviation quantum-behaved particle swarm optimization algorithm is compared with a quantum-behaved particle swarm optimization and a quantum-behaved particle swarm optimization with quadratic interpolation recombination operator, which is an advanced variant of quantum-behaved particle swarm optimization. Finally, the proposed stable deviation quantum-behaved particle swarm optimization algorithm is applied to constrained optimal piezoelectric actuator and sensor location for active vibration control of a simply supported plate under the different periodic loads.
机译：本文提出了一种新的稳定偏差量子行为粒子群算法来解决全局优化问题。提出的稳定偏差量子行为粒子群优化算法利用插值和稳定偏差函数。重组算子方法用于在搜索空间中生成新的解向量。因此，可以提高快速收敛的机会。将所提出的稳定偏差量子行为粒子群优化算法的性能与量子行为粒子群优化和带有二次插值重组算子的量子行为粒子群优化进行了比较，这是量子行为粒子群优化的高级变体。 。最后，将所提出的稳定偏差量子行为粒子群优化算法应用于约束最优压电致动器和传感器位置，以在不同周期载荷下对简单支撑的板进行主动振动控制。

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