论文标题
基于共识的全球优化与个人最佳
Consensus-Based Global Optimization with Personal Best
论文作者
论文摘要
在本文中，我们提出了一种基于共识的全局优化（CBO）方法的变体，该方法使用个人最佳信息来计算非凸的全局最小值，本地Lipschitz的连续功能。所提出的方法是由原始的粒子蜂群算法激励的，其中粒子相对于个人最佳，当前的全球最佳和一些添加剂噪声调整了其位置。在加权平均值的帮助下包括个人轨迹的个人最佳信息。由于其累积结构，可以非常有效地计算此加权均值。它通过附加的漂移术语进入动力学。我们用玩具示例说明了性能，分析相应的内存依赖性随机系统，并将性能与原始CBO与组件噪声进行比较，以解决几个基准问题。所提出的方法对于具有较小粒子的计算实验具有较高的成功率，而初始粒子分布相对于整体最小值不利。
In this paper we propose a variant of a consensus-based global optimization (CBO) method that uses personal best information in order to compute the global minimum of a non-convex, locally Lipschitz continuous function. The proposed approach is motivated by the original particle swarming algorithms, in which particles adjust their position with respect to the personal best, the current global best, and some additive noise. The personal best information along an individual trajectory is included with the help of a weighted mean. This weighted mean can be computed very efficiently due to its accumulative structure. It enters the dynamics via an additional drift term. We illustrate the performance with a toy example, analyze the respective memory-dependent stochastic system and compare the performance with the original CBO with component-wise noise for several benchmark problems. The proposed method has a higher success rate for computational experiments with a small particle number and where the initial particle distribution is disadvantageous with respect to the global minimum.
