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摘要翻译：
统计证据表明，邻域拓扑结构对粒子群优化算法性能的影响已在许多工作中显示出来。然而，很少有人做关于可能有渗流阈值的含义，以确定这个邻域的拓扑结构。这项工作为像机器人一样能够感知周围有限区域的个体解决了这个问题。基于渗流阈值的概念，更确切地说，基于二维圆盘渗流模型，我们证明了当个体偶尔询问他人的最佳访问位置时，在半径较小的情况下可以获得更好的结果，从而降低了计算复杂度。另一方面，由于渗流阈值是一个普遍的测度，因此比较不同混合粒子群算法的性能具有很大的意义。
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英文标题：
《On how percolation threshold affects PSO performance》
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作者：
Blanca Cases, Alicia D'Anjou, Abdelmalik Moujahid
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最新提交年份：
2012
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
Statistical evidence of the influence of neighborhood topology on the performance of particle swarm optimization (PSO) algorithms has been shown in many works. However, little has been done about the implications could have the percolation threshold in determining the topology of this neighborhood. This work addresses this problem for individuals that, like robots, are able to sense in a limited neighborhood around them. Based on the concept of percolation threshold, and more precisely, the disk percolation model in 2D, we show that better results are obtained for low values of radius, when individuals occasionally ask others their best visited positions, with the consequent decrease of computational complexity. On the other hand, since percolation threshold is a universal measure, it could have a great interest to compare the performance of different hybrid PSO algorithms.
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PDF链接：
https://arxiv.org/pdf/1204.3844
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