基于混合IACO $_\mathbb{R}$的连续全局优化NLopt和最新算法的基准测试
Benchmarking NLopt and state-of-art algorithms for Continuous Global Optimization via Hybrid IACO$_\mathbb{R}$
摘要：
本文提出了一种增量式连续优化蚁群算法（$IACO_\mathbb{R}$）的性能进行了比较分析，在NLopt库中提供的不同算法。主要目标是了解NLopt库中的各种算法如何与多轨迹本地搜索（Mtsls 1）技术结合使用。通过使用允许Mtsls 1和NLopt算法之间的概率选择的参数，在局部搜索策略中引入了混合方法。在停滞的情况下，基于在先前迭代中使用的算法进行算法切换。本文对这些方法在软计算（SOCO）和演化计算大会（CEC）2014基准测试上的性能进行了详尽的比较。对于这两个基准测试，我们得出结论，表现最好的算法是Mtsls1与BFGS局部搜索的混合变体。
英文摘要：
This paper presents a comparative analysis of the performance of the Incremental Ant Colony algorithm for continuous optimization ($IACO_\mathbb{R}$), with different algorithms provided in the NLopt library. The key objective is to understand how the various algorithms in the NLopt library perform in combination with the Multi Trajectory Local Search (Mtsls1) technique. A hybrid approach has been introduced in the local search strategy by the use of a parameter which allows for probabilistic selection between Mtsls1 and a NLopt algorithm. In case of stagnation, the algorithm switch is made based on the algorithm being used in the previous iteration. The paper presents an exhaustive comparison on the performance of these approaches on Soft Computing (SOCO) and Congress on Evolutionary Computation (CEC) 2014 benchmarks. For both benchmarks, we conclude that the best performing algorithm is a hybrid variant of Mtsls1 with BFGS for local search.
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。
