Particle swarm optimization (PSO) is an iterative search method that moves a set of candidate solution around a search-space towards the best known global and local solutions with randomized step lengths. PSO frequently accelerates optimization in practical applications, where gradients are not available and function evaluations expensive. Yet the traditional PSO algorithm ignores the potential knowledge that could have been gained of the objective function from the observations by individual particles. Hence, we draw upon concepts from Bayesian optimization and introduce a stochastic surrogate model of the objective function. That is, we fit a Gaussian process to past evaluations of the objective function, forecast its shape and then adapt the particle movements based on it. Our computational experiments demonstrate that baseline implementations of PSO (i. e., SPSO2011) are outperformed. Furthermore, compared to, state-of-art surrogate-assisted evolutionary algorithms, we achieve substantial performance improvements on several popular benchmark functions. Overall, we find that our algorithm attains desirable properties for exploratory and exploitative behavior.

粒子群优化 (PSO) 是一种迭代搜索方法，它将搜索空间周围的一组候选解决方案移向具有随机步长的最著名的全局和局部解决方案。PSO 经常在实际应用中加速优化，在这些应用中，梯度不可用且函数评估很昂贵。然而，传统的 PSO 算法忽略了可以从单个粒子的观察中获得的目标函数的潜在知识。因此，我们借鉴了贝叶斯优化的概念，并引入了目标函数的随机代理模型。也就是说，我们将高斯过程拟合到目标函数的过去评估中，预测其形状，然后根据它调整粒子运动。我们的计算实验表明，PSO 的基线实现（即 SPSO2011）的表现优于其他算法。此外，与最先进的代理辅助进化算法相比，我们在几个流行的基准函数上实现了显着的性能改进。总的来说，我们发现我们的算法获得了探索性和剥削性行为的理想特性。