Author:
Meng Xiangzhong,Ma Ying,Guo Qiang
Abstract
The adaptive quantum particle swarm optimization algorithm based on cloud model and the multi-island genetic algorithm [15] have obvious advantages in convergence speed to solve the sensor optimization problem, and can effectively achieve global optimization. Due to the installation of sensors and actuators, the electromechanical coupling coefficient of intelligent structures is changed, which affects the vibration energy of structures. In this paper, the reserved energy index of structural vibration control system is taken as the objective optimization function. The position, number, length and control gain of sensors and actuators of active vibration control system are optimized. The adaptive Quantum-behaved Particle Swarm Optimization algorithm in cloud model(CMQPSO) is used as the optimization strategy, and the cantilever beam is taken as an example. This approach is verified its effectiveness and feasibility. It is found that excellent optimization results are obtained.
Subject
Computational Mathematics,Computer Science Applications,General Engineering
Reference14 articles.
1. Research of active vibration control optimization based on genetic algorithm;Meng;Computer Measurement & Control,2008
2. K. Le, J.Q. Xing, J.T. Zhang, X.Z. Xu, M.F. Wu and K.Q. Zhao, Application of a Cloud Model-Set Pair Analysis in Efficacy Assessment for Diabetic Ulcers, Evidence-Based Complementary and Alternative Medicine, 2019.
3. Research on improvement of adaptive ant colony algorithm based on cloud model;Liu;Computer Engineering and Application,2016
4. Adaptive quantum particle swarm optimization algorithm based on normal cloud model;Yu;Joural of Jishou University,2019
5. Quantum-behaved particle swarm algorithm with self-adapting adjustment of inertia weight;Huang;Journal of Shanghai Jiaotong University,2012