中文摘要：

本文通过对传统粒子群算法（PSO）的分析，在GPU（Graphic Process Unit）上设计了基于一般反向学习策略的粒子群算法，并用于求解大规模优化问题．主要思想是通过一般反向学习策略转化当前解空间，提高算法找到最优解的几率，同时使用GPU大量线程并行来加速收敛速度．对比数值实验表明，对于求解大规模高维的优化问题，本文算法比其他智能算法具有更好的精度和更快的收敛速度．

英文摘要：

Through an analysis of the traditional particle swarm algorithm, this paper presents particle swarm algorithm based on the generalized opposition-based particle （GOBL） swarm algorithm on Graphic Processing Unit （GPU）, and applies it to solve large scale optimization problem. The generalized opposi- tion learning strategies transforms the current solution space to provide more chances of finding better so- lutions, and GPU in parallel accelerates the convergence rate. Experiment shows that this algorithm has better accuracy and convergence speed than other algorithm for solving large-scale and high-dimensional problems.