论文标题
基于分形分解的基于低维黑盒优化问题的元疗法的研究
Study of the Fractal decomposition based metaheuristic on low-dimensional Black-Box optimization problems
论文作者
论文摘要
本文分析了分形分解算法（FDA）元启发式化的性能，该元神经化应用于低维连续优化问题。该算法最初是专门为有效地处理高维连续优化问题而开发的，该算法通过构建基于分形的搜索树，其分支因子与尺寸的数量成正比。在这里，我们旨在回答FDA是否对低维问题同样有效的问题。为此，我们评估了对尺寸2、3、5、10、20和40的黑匣子优化基准（BBOB）在黑匣子优化基准（BBOB）上的性能。实验结果表明，当前形式的FDA总体表现不够好。在不同的功能组中，FDA在MISC上表现出最佳性能。中等和弱的结构功能。
This paper analyzes the performance of the Fractal Decomposition Algorithm (FDA) metaheuristic applied to low-dimensional continuous optimization problems. This algorithm was originally developed specifically to deal efficiently with high-dimensional continuous optimization problems by building a fractal-based search tree with a branching factor linearly proportional to the number of dimensions. Here, we aim to answer the question of whether FDA could be equally effective for low-dimensional problems. For this purpose, we evaluate the performance of FDA on the Black Box Optimization Benchmark (BBOB) for dimensions 2, 3, 5, 10, 20, and 40. The experimental results show that overall the FDA in its current form does not perform well enough. Among different function groups, FDA shows its best performance on Misc. moderate and Weak structure functions.
