An improved grasshopper optimization algorithm (GOA) is proposed in this paper, termed as SGOA, which combines simulated annealing (SA) mechanism with the original GOA that is a natural optimizer widely used in finance, medical and other fields, and receives more promising results based on grasshopper behavior. To compare performance of the SGOA and other algorithms, an investigation to select CEC2017 benchmark function as the test set was carried out. Also, the Friedman assessment was performed to check the significance of the proposed method against other counterparts. In comparison with ten meta-heuristic algorithms such as differential evolution (DE), the proposed SGOA can rank first in the CEC2017, and also ranks first in comparison with ten advanced algorithms. The simulation results reveal that the SA strategy notably improves the exploration and exploitation capacity of GOA. Moreover, the SGOA is also applied to engineering problems and parameter optimization of the kernel extreme learning machine (KELM). After optimizing the parameters of KELM using SGOA, the model was applied to two datasets, Cleveland Heart Dataset and Japanese Bankruptcy Dataset, and they achieved an accuracy of 79.2% and 83.5%, respectively, which were better than the KELM model obtained other algorithms. In these practical applications, it is indicated that the proposed SGOA can provide effective assistance in settling complex optimization problems with impressive results.

本文提出了一种改进的蚱hopper优化算法（GOA），称为SGOA，它将模拟退火（SA）机制与原始GOA相结合，GOA是在金融，医疗和其他领域广泛使用的自然优化器，并收到了更可喜的结果基于蚱hopper的行为 为了比较SGOA和其他算法的性能，进行了调查以选择CEC2017基准功能作为测试集。此外，进行了弗里德曼评估，以检查该方法相对于其他方法的重要性。与差分演化（DE）等十种元启发式算法相比，拟议的SGOA在CEC2017中排名第一，与十种高级算法相比也排名第一。仿真结果表明，SA策略显着提高了GOA的勘探开发能力。此外，SGOA还应用于内核极限学习机（KELM）的工程问题和参数优化。使用SGOA对KELM参数进行优化后，将该模型应用于克利夫兰心脏数据集和日本破产数据集这两个数据集，它们的准确率分别达到79.2％和83.5％，优于KELM模型获得的其他算法。在这些实际应用中，表明拟议的SGOA可以为解决复杂的优化问题提供有效的帮助，并获得令人印象深刻的结果。SGOA还应用于内核极限学习机（KELM）的工程问题和参数优化。使用SGOA对KELM参数进行优化后，将该模型应用于克利夫兰心脏数据集和日本破产数据集这两个数据集，它们的准确率分别达到79.2％和83.5％，优于KELM模型获得的其他算法。在这些实际应用中，表明拟议的SGOA可以为解决复杂的优化问题提供有效的帮助，并获得令人印象深刻的结果。SGOA还应用于内核极限学习机（KELM）的工程问题和参数优化。使用SGOA对KELM参数进行优化后，将该模型应用于克利夫兰心脏数据集和日本破产数据集这两个数据集，它们的准确率分别达到79.2％和83.5％，优于KELM模型获得的其他算法。在这些实际应用中，表明拟议的SGOA可以为解决复杂的优化问题提供有效的帮助，并产生令人印象深刻的结果。