基于高斯过程函数预测的有向粒子群算法
Directed particle swarm optimization with Gaussian-process-based function forecasting
摘要：
粒子群优化（PSO）是一种迭代搜索方法，它以随机步长将一组候选解在搜索空间中向最佳全局解和局部解移动。在实际应用中，粒子群算法经常会加速优化，因为在这些应用中，梯度是不可用的，函数求值也是昂贵的。然而，传统的粒子群算法忽略了潜在的知识，可能已经获得的目标函数的观察，个别粒子。因此，我们借鉴贝叶斯优化的概念，并引入一个随机代理模型的目标函数。也就是说，我们将高斯过程拟合到目标函数的过去评估，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，SPSO 2011）表现出色。此外，相比，国家的最先进的代理辅助进化算法，我们实现了几个流行的基准测试功能的性能大幅改善。总的来说，我们发现，我们的算法达到了理想的性质的探索和剥削行为。
英文摘要：
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.
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