Grasshopper optimization algorithm

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,While 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 differentproblems.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 differentproblems.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 differentproblems.

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蝗虫优化算法（GOA）是一种新提出的模拟蝗虫觅食行为的元启发式算法。然而，GOA的基本版本存在一些缺陷。当面对一些复杂的流域时，它可能很快陷入局部最优，收敛速度较慢。本文提出了一种改进的GOA算法，以克服GOA算法的核心缺陷，更有效地处理连续优化问题。为此，在传统的GOA算法中引入了正交学习和混沌开采两种策略，以期在勘探和开采核心之间找到一个更稳定的平衡点。在GOA中加入正交学习可以提高agent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。为了验证GOA的有效性，我们将其与在30个IEEE CEC2017基准函数上执行的各种著名的经典元启发式算法进行了比较。并将其应用到特征选择案例中，并以三个结构设计问题为例，从不同的度量标准来验证其有效性。实验结果表明，上述策略能有效地缓解GOA算法的不足，改进后的变量能够对不同的问题获得高质量的解决方案。<br>