为解决码头结构健康监测领域的传感器优化布置问题，提出了一种基于改进多目标粒子群的传感器优化布置算法。针对传统方法寻优效率低、优化目标单一，难以同时满足模态识别、损伤识别等复杂的健康监测需求的问题，构建了以损伤敏感性和冗余性、损伤识别不适定性以及模态线性独立性的多目标优化函数；改进了多目标粒子群算法（IMOPSO）获取Pareto解集，利用TOPSIS熵权法确定最优传感器布置方案。在某高桩码头实验表明：与有效独立法和有效独立-模态动能法相比，IMOPSO得到的布设方案测点分布更均匀，在灵敏度矩阵条件数、MAC最大非对角元、损伤冗余性指标分别优化了45%、90%、5%以上；多种工况下的损伤位置和程度识别准确率在不同噪声下平均提高5%和7%以上。 

In order to solve the problem of optimal sensor placement in the field of wharf structure health monitoring, an optimal sensor placement algorithm based on improved multi-objective particle swarm optimization was proposed. In order to solve the problems of low optimization efficiency and single optimization objective of traditional methods, which was difficult to meet the complex health monitoring requirements such as modal identification and damage identification at the same time, a multi-objective optimization function based on damage sensitivity and redundancy, damage identification ill-posedness and modal linear independence was constructed, and the multi-objective particle swarm optimization algorithm (IMOPSO) was improved to obtain Pareto solution set, and the optimal sensor placement scheme was determined using TOPSIS entropy method. The test on a high-piled wharf shows that compared with effective independence method and effective independence-modal kinetic energy method, the distribution of measuring points using IMOPSO is more uniform, and the condition number of sensitivity matrix, the maximum non-diagonal element of MAC and the index of damage redundancy are optimized more than 45%, 90% and 5% respectively; The accuracy of damage location and degree identification under various working conditions is improved more than 5% and 7% respectively.