Benchmark sets are extremely important for evaluating and developing global optimization algorithms and related solvers. A new test set named PCC benchmark is proposed especially for optimization problem of nonlinear curve fitting for the first time, with the aspiration of investigating and comparing the performance of different global optimization solvers. Compared with the well-known classical nonlinear curve fitting benchmark set given by the National Institute of Standards and Technology (NIST) of USA, the most important features of the PCC benchmark are small problem dimensions, free search domain and high level of difficulty for obtaining global optimization solutions, which makes the PCC benchmark be not only suitable for validating the effectiveness of various global optimization algorithms, but also more ideal for verifying and comparing various solvers with global optimization solving capabilities. Based on PCC and NIST benchmark, seven of the world's leading global optimization solvers, including Baron, Antigone, Couenne, Lingo, Scip, Matlab GA and 1stOpt, are thoroughly tested and compared in terms of both effectiveness and efficiency. The results showed that the NIST benchmark is relatively simple and not suitable for global optimization testing, while the PCC benchmark is a unique, challengeable and effective test dataset for testing and verifying global optimization algorithms and related solvers. 1stOpt solver gives the overall best performance in both NIST and PCC benchmark.

翻译：基准测试集对于评估和开发全局优化算法及相关求解器至关重要。本文首次提出了一种专门针对非线性曲线拟合优化问题的新测试集——PCC基准，旨在研究并比较不同全局优化求解器的性能。与美国国家标准与技术研究院（NIST）提供的经典非线性曲线拟合基准集相比，PCC基准的最显著特点包括：问题维数小、搜索域无约束且获取全局优化解的难度极高。这使得PCC基准不仅适用于验证各类全局优化算法的有效性，更理想地用于检验和比较具有全局优化能力的各类求解器。基于PCC和NIST基准，本文对包括Baron、Antigone、Couenne、Lingo、Scip、Matlab GA和1stOpt在内的七款全球领先的全局优化求解器，从有效性和效率两个方面进行了全面测试与比较。结果表明，NIST基准相对简单，不适用于全局优化测试；而PCC基准则是测试和验证全局优化算法及相关求解器的独特、具有挑战性且有效的测试数据集。在NIST和PCC两个基准中，1stOpt求解器均展现出整体最佳性能。