In this paper, a novel comprehensive learning particle swarm optimization algorithm, which is based on the Bayesian iteration method and named as Bayesian comprehensive learning particle swarm optimization (BCLPSO), is proposed. In the original PSO, the flying direction of each particle is based on its own historical best position and global optimum. This updating mechanism, however, easily falls into the local optimum, and the potential optimum solution may be ignored in the iteration and update process. Therefore, the BCLPSO is designed to facilitate discovering potential solution and avoid the problem of premature convergence. In the BCLPSO algorithm, the exemplar of the swarm is not the global best position but the particle location with the largest posterior probability based on the Bayesian formula. The posterior probability is developed by historical prior information. This means that the posterior probability can inherit the historical information of particles that may be exploited. In this way, the swarm diversity can be preserved to prevent premature convergence. The BCLPSO is experimentally validated on the CEC2017 benchmark functions and compared with other state-of-the-art particle swarm optimization algorithms. The results show that BCLPSO outperforms other comparative PSO variants on the CEC 2017 test suite. Furthermore, the algorithm is applied to the quality control process of an automated welding production line for the automobile body and is found to exhibit superior performance.

提出了一种基于贝叶斯迭代方法的综合学习粒子群优化算法，即贝叶斯综合学习粒子群优化算法（BCLPSO）。在原始PSO中，每个粒子的飞行方向都是基于其自身的历史最佳位置和全局最佳位置。但是，此更新机制很容易陷入局部最优，并且在迭代和更新过程中可能会忽略潜在的最优解。因此，BCLPSO旨在帮助发现潜在的解决方案并避免过早收敛的问题。在BCLPSO算法中，基于贝叶斯公式，群集的示例不是全局最佳位置，而是后验概率最大的粒子位置。后验概率是由历史先验信息得出的。这意味着后验概率可以继承可能被利用的粒子的历史信息。这样，可以保留群多样性以防止过早收敛。BCLPSO已在CEC2017基准函数上进行了实验验证，并与其他最新的粒子群优化算法进行了比较。结果表明，BCLPSO在CEC 2017测试套件上优于其他比较PSO变体。此外，该算法被应用于汽车车身的自动焊接生产线的质量控制过程，并被证明具有优越的性能。这样，可以保留群多样性以防止过早收敛。BCLPSO已在CEC2017基准函数上进行了实验验证，并与其他最新的粒子群优化算法进行了比较。结果表明，BCLPSO在CEC 2017测试套件上优于其他比较PSO变体。此外，该算法被应用于汽车车身的自动焊接生产线的质量控制过程，并被证明具有优越的性能。这样，可以保留群多样性以防止过早收敛。BCLPSO已在CEC2017基准功能上进行了实验验证，并与其他最新的粒子群优化算法进行了比较。结果表明，BCLPSO在CEC 2017测试套件上优于其他比较PSO变体。此外，该算法被应用于汽车车身的自动焊接生产线的质量控制过程，并被证明具有优越的性能。