用于全局优化的新硬基准函数
New hard benchmark functions for global optimization
摘要：
在本文中，我们提出了一些新的单峰，多峰和噪声测试功能来评估全局优化算法的性能。所有的测试功能都是多维问题。所提出的函数的2维景观已经在3D空间中以图形方式呈现以显示它们的几何形状，然而这些函数在大于3的维度上更复杂。为了证明这些函数的硬度，我们用一些强大的算法进行了实验研究，例如CEC竞赛的获奖者：LSHADE，MadDe和LSHADE-SPACMA算法。除了新的算法，切线搜索算法（TSA）和它的修改切线搜索算法（mTSA）也被用于实验研究。结果表明，所提出的功能的硬度。所提出的测试函数的代码源可在Matlab Exchange网站上获得。https://www.mathworks.com/matlabcentral/fileexchange/106450-new-hard-benchmark-functions-for-global-optimization? s_tid=srchtitle
英文摘要：
In this paper, we present some new unimodal, multimodal, and noise test functions to assess the performance of global optimization algorithms. All the test functions are multidimensional problems. The 2-dimension landscape of the proposed functions has been graphically presented in 3D space to show their geometry, however these functions are more complicated in dimensions greater than 3. To show the hardness of these functions, we have made an experimental study with some powerful algorithms such as CEC competition winners: LSHADE, MadDe, and LSHADE-SPACMA algorithms. Besides the novel algorithm, Tangent search algorithm (TSA) and its modified Tangent search algorithm (mTSA) were also used in the experimental study. The results found demonstrate the hardness of the proposed functions. The code sources of the proposed test functions are available on Matlab Exchange website. https://www.mathworks.com/matlabcentral/fileexchange/106450-new-hard-benchmark-functions-for-global-optimization?s_tid=srchtitle
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。
