CW-PSO及其在古建筑传感器优化配置中的应用研究

Particle swarm optimization based on cosine adaptive adjusting inertia weight and its application research in optimal sensor placement of historic architecture

针对粒子群优化算法容易陷入早熟收敛以及全局搜索和局部搜索平衡能力差等缺点,提出了基于余弦自适应调整惯性权重的粒子群优化算法(CW-PSO),并将其应用在木构古建筑传感器优化配置中。仿真结果表明,该算法在一定程度上避免了早熟收敛,提高了全局和局部搜索性能,又能得到较为精确的寻优结果。

Aiming at the premature convergence problem and unbalance of global search and local search in particle swarm opti- mization algorithm, this paper proposes a particle swarm optimization algorithm based on cosine adaptive adjusting inertia weight. The improved particle swarm optimization is applied in optimal sensor placement of wooden historic architecture. Simu- lation results show that it can avoid premature convergence to an extent, improve the global search ability and obtain accurate results of optimization by simulation experiment.