基于混合粒子群算法的在线检测路径规划

针对加工中心在线检测中的路径规划问题，采用基于全局规划与局部优化相结合的策略，将光学寻优算法、遗传算法、混沌优化与基本粒子群算法结合，提出了一种新型混合离散粒子群算法，并将其用于检测路径的全局规划。首先简述了在线检测路径规划的原理及检测数据的获取，然后介绍了混合粒子群算法的原理、步骤及其流程，最后对算法进行了仿真研究，并且与蚁群算法进行了对比分析。经仿真验证，该算法具有搜索精度高、收敛速度快、稳定性好等优点，有较好的实际应用价值。

Path planning for on machine verification system based on hybrid particle swarm optimization algorithm

Path planning is an important aspect of on machine verification (OMV), in this article, a strategy that combine global planning and local adjustment is put forward. Additionally, this paper proposes a new type of hybrid discrete particle swarm optimization algorithm, which absorbs the advantages of optical optimization algorithm, genetic algorithm and chaos optimization. The algorithm is used for global planning. This paper firstly introduce the principle of path planning and how to acquire the testing data; then explain the principle, steps and flow chart of the algorithm detailed; at last, the simulation research of the algorithm is carried on and is compared with ant colony algorithm. From the simulation result, it is obvious that the algorithm possesses the advantages of high search precision, favorable stability and fast convergence rate, it has a good practical application value.