用无人机路径规划测试优化算法，发现进化类方法表现最佳。
Benchmarking global optimization techniques for unmanned aerial vehicle path planning
- 构建56个无人机路径规划实例，用于对比优化算法性能。
- 在不同维度和计算资源下，进化算法显著优于其他方法。
- 适合研究优化算法或无人机路径规划的开发者参考。
无人机路径规划是机器人领域中的复杂优化问题。本文探讨将其用于基准测试全局优化方法的可行性，设计了一个问题实例生成器，选取了56个代表性实例，并通过探索性景观分析验证其独特性。在计算比较中，选用了12种表现优异的全局优化技术，涵盖随机算法（如进化计算）和确定性算法（如分矩形法，DIRECT型方法），在不同维度和计算预算条件下进行实验。结果通过最佳解数量、平均相对误差和弗里德曼秩等指标分析，并使用统计检验确认显著性。最优方法几乎全部来自电气与电子工程师协会（IEEE CEC）近年数值优化竞赛中的顶尖进化算法。最后，讨论了该问题中维度可变性的特点，指出其仍鲜有深入研究。
原文摘要 · Abstract (English)
The Unmanned Aerial Vehicle (UAV) path planning problem is a complex optimization problem in the field of robotics. In this paper, we investigate the possible utilization of this problem in benchmarking global optimization methods. We devise a problem instance generator and pick 56 representative instances, which we compare to established benchmarking suits through Exploratory Landscape Analysis to show their uniqueness. For the computational comparison, we select twelve well-performing global optimization techniques from both subfields of stochastic algorithms (evolutionary computation methods) and deterministic algorithms (Dividing RECTangles, or DIRECT-type methods). The experiments were conducted in settings with varying dimensionality and computational budgets. The results were analyzed through several criteria (number of best-found solutions, mean relative error, Friedman ranks) and utilized established statistical tests. The best-ranking methods for the UAV problems were almost universally the top-performing evolutionary techniques from recent competitions on numerical optimization at the Institute of Electrical and Electronics Engineers Congress on Evolutionary Computation. Lastly, we discussed the variable dimension characteristics of the studied UAV problems that remain still largely under-investigated.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。