In order to enhance the efficiency of offshore drilling rig and the space utilization of offshore platform, a modified particle swarm optimization ( PSO) was applied to the layout optimization of offshore drilling rig system. A mathematical model was established for the layout optimization of offshore drilling rig system, and the mathematical model was directly solved by multi-objective particle swarm optimization ( MOPSO) . Then the problem of multi-objective optimization was con-verted into a mono-objective one by linear weighting method. Aiming at the defects of the mono-objective particle swarm opti-mization, several improvement measures based on interference constraints, inertia weight, the selection and crossover opera-tor of genetic algorithm ( GA) were carried out in this paper. Then testing experiments for the above-mentioned improved al-gorithms were conducted. The test results show that a better solving speed and accuracy can be obtained by taking the con-straint condition as the objective function and adopting the hybrid thought of GA in the layout design. A modified PSO with dynamic inertia weight and crossover operator was proposed in this paper, which has a better solving performance. The ob-tained layout scheme satisfies the requirements of the marine drilling operation and takes up the less deck area.%为提高海洋钻机的工作效率和平台的空间利用率，应用改进的粒子群算法对海洋钻机系统进行布局优化研究。针对多目标、多约束的钻机系统布局优化问题，建立钻机系统布局优化数学模型，应用多目标粒子群算法直接求解，得出相应的最优解集。利用线性加权法将多目标转变为单目标进行求解分析，针对单目标粒子群算法的缺点，基于约束条件、惯性权重以及遗传算法的选择和杂交对粒子群算法进行改进，完成不同改进算法的测试实验。结果表明，在应用粒子群算法求解布局问题时将约束条件作为目标函数、单独引入遗传算法的杂交思想求解速度和精度更好。提出的基于杂交的动态惯性权重粒子群算法的布局优化问题求解性能更优，得到的优化方案符合海洋钻井作业要求且占用甲板面积较小。
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