论文标题
收敛指标：改进并完全表征了参数界限，以实现粒子群优化的实际收敛性
The Convergence Indicator: Improved and completely characterized parameter bounds for actual convergence of Particle Swarm Optimization
论文作者
论文摘要
粒子群优化（PSO）是用于连续黑盒优化问题的荟萃疗法。在本文中，我们关注粒子群的收敛性，即算法的剥削阶段。我们引入了一个新的收敛指标，该指标可用于计算粒子是否最终会收敛到单个点还是差异。使用此收敛指标，我们提供的实际边界完全表征了导致汇聚的参数区域。我们的边界扩展了参数区域，该区域与通常在文献中使用的融合方差相比，保证了收敛性。为了评估我们的标准，我们使用立方样条插值来描述数值近似。最后，我们提供了实验，表明我们的概念，公式和由此产生的收敛范围代表了PSO的实际行为。
Particle Swarm Optimization (PSO) is a meta-heuristic for continuous black-box optimization problems. In this paper we focus on the convergence of the particle swarm, i.e., the exploitation phase of the algorithm. We introduce a new convergence indicator that can be used to calculate whether the particles will finally converge to a single point or diverge. Using this convergence indicator we provide the actual bounds completely characterizing parameter regions that lead to a converging swarm. Our bounds extend the parameter regions where convergence is guaranteed compared to bounds induced by converging variance which are usually used in the literature. To evaluate our criterion we describe a numerical approximation using cubic spline interpolation. Finally we provide experiments showing that our concept, formulas and the resulting convergence bounds represent the actual behavior of PSO.
