钻床路径优化:人工智能算法的特性及面向实际用户的实验系统

I. Pozniak-Koszalka, L. Koszalka, A. Kasprzak, G. Chmaj, D. Zydek
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引用次数: 1

摘要

所考虑的钻床路线寻找问题是TSP的一个改进版本。修改包括循环更换钻头的必要性,这需要在执行一定数量的钻孔后返回到起点-取决于特定工艺的物理参数。为了找到最佳路线,人工智能方法已经被应用。本文研究了基于模拟退火、遗传思想和蚁群优化的实现算法的特性。实现了以蛮力算法为参考算法和模拟蜂群算法。通过对两阶段仿真实验结果的处理,对算法的性能进行了分析。实验采用所创建和实现的实验系统进行。该系统还可以为实际用户解决实际问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Route Optimization for Drilling Machine: Properties of AI Algorithms and An Experimentation System for the Practical Users
The considered problem of finding drilling machine route is a modified version of TSP. The modification consists in necessity of cyclic drill change which requires returning to starting point after performing a certain number of bores - depending on physical parameters of the particular process. In order to find the best route AI approaches have been applied. In the paper the properties of the implemented algorithms based on Simulated Annealing, Genetic ideas, Ant Colony Optimization have been studied. The Brute Force as reference algorithm as well as Simulated Bee Colony algorithm was implemented, either. The analysis of properties of algorithms was made by processing the results of two-stage simulation experiments. The experiments were carried out with the created and implemented experimentation system. The system also can be used by the practical users for solving their problems.
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