关键词: 粒子群算法，全局搜索能力，局部极值，自适应二次粒子群算法，模型修正

Abstract: Particle swarm optimization(PSO) algorithm which has less parameters is widely used in optimisation area for its better global search ability and calculation efficiency, it's necessary to change some parameters of the formula to improve its search ability and avoid getting into local optimum. The inertia factor and optimal position in the velocity fomina of PSO were updated and the self-adaptive quadratic particle swarm optimization(SAQPSO) algorithm with simple form and high search efficiency was proposed, model updating of the five-layer steel frame structure confirms the validity and superiority of SAQPSO.

Key words: Particle swarm optimization(PSO) algorithm, Global search ability, Local optimum, Self-adaptive quadratic particle swarm optimi}ation(SAQPSO) algorithm,Modc1 updating