Grasshopper optimization algorithm (GOA) is a newly proposed meta-heuristic algorithm that simulates the biological habits of grasshopper seeking for food sources. Nonetheless, some shortcomings exist in the basic version of GOA. It may quickly drop into local optima and show slow convergence rates when facing some complex basins. In this work, an improved GOA is proposed to alleviate the core shortcomings of GOA and handle continuous optimization problems more efficiently. For this purpose, two strategies, including orthogonal learning and chaotic exploitation, are introduced into the conventional GOA to find a more stable trade-off between the exploration and exploitation cores. Adding orthogonal learning to GOA can enhance the diversity of agents, whereas a chaotic exploitation strategy can update the position of grasshoppers within a limited local region. To confirm the efficacy of GOA, we compared it with a variety of famous classical meta-heuristic algorithms performed on 30 IEEE CEC2017 benchmark functions. Also, it is applied to feature selection cases, and three structural design problems are employed to validate its efficacy in terms of different metrics. The experimental results illustrate that the above tactics can mitigate the deficiencies of GOA, and the improved variant can reach high-quality solutions for different problems.

蚱optimization优化算法（GOA）是一种新提出的元启发式算法，用于模仿寻找食物来源的蚱hopper的生物学习性。尽管如此，基本版本的GOA存在一些缺陷。当面对一些复杂的盆地时，它可能会迅速陷入局部最优状态并显示出缓慢的收敛速度。在这项工作中，提出了一种改进的GOA，以缓解GOA的核心缺点并更有效地处理连续优化问题。为此，在常规GOA中引入了两种策略，包括正交学习和混沌开发，以在勘探和开发核心之间找到更稳定的平衡。在GOA中添加正交学习可以增强代理的多样性，而混沌开发策略可以在有限的局部区域内更新蝗虫的位置。为了确认GOA的有效性，我们将其与在30种IEEE CEC2017基准功能上执行的各种著名的经典元启发式算法进行了比较。此外，它还应用于特征选择案例，并采用了三个结构设计问题以根据不同的指标来验证其有效性。实验结果表明，以上策略可以缓解GOA的不足，改进后的方案可以解决不同问题的高质量。并采用三个结构设计问题来验证其在不同指标方面的功效。实验结果表明，以上策略可以缓解GOA的不足，改进后的方案可以解决不同问题的高质量。并使用三个结构设计问题来验证其在不同指标方面的功效。实验结果表明，以上策略可以缓解GOA的不足，改进后的方案可以解决不同问题的高质量。