粒子更喜欢沿着轴行走：粒子群行为的实验研究
Particles Prefer Walking Along the Axes: Experimental Insights into the Behavior of a Particle Swarm
摘要：
粒子群优化（PSO）是一种广泛使用的自然启发式元启发式算法，用于解决连续优化问题。然而，当运行PSO算法时，会遇到所谓的停滞现象，这意味着在我们的上下文中，整个群体开始收敛到一个不是（甚至不是局部）最优的解决方案。这项工作的目标是指出为什么群体停滞在这些非最佳点的可能原因。为了实现我们的结果，我们使用新定义的群的潜力。总势在搜索空间的每个维度上都有一部分，当群接近收敛点时，总势下降。正如实验所证明的那样，群体很可能有时会进入“不平衡”状态，即。例如，几乎所有的电势都属于一个轴。因此，群体变得盲目，无法在任何其他方向上进行改进。最后，我们将展示如何在光的潜力和这些意见，一个稍微适应PSO重新平衡的潜力，从而提高质量的解决方案。
英文摘要：
Particle swarm optimization (PSO) is a widely used nature-inspired meta-heuristic for solving continuous optimization problems. However, when running the PSO algorithm, one encounters the phenomenon of so-called stagnation, that means in our context, the whole swarm starts to converge to a solution that is not (even a local) optimum. The goal of this work is to point out possible reasons why the swarm stagnates at these non-optimal points. To achieve our results, we use the newly defined potential of a swarm. The total potential has a portion for every dimension of the search space, and it drops when the swarm approaches the point of convergence. As it turns out experimentally, the swarm is very likely to come sometimes into "unbalanced" states, i. e., almost all potential belongs to one axis. Therefore, the swarm becomes blind for improvements still possible in any other direction. Finally, we show how in the light of the potential and these observations, a slightly adapted PSO rebalances the potential and therefore increases the quality of the solution.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
