Yu Xinya, Zunu Parhat
School of Mining Engineering and Geology, Xinjiang Institute of Engineering, Urumqi 830023, China.
Biomimetics (Basel). 2025 Jul 25;10(8):494. doi: 10.3390/biomimetics10080494.
The Grasshopper Optimization Algorithm (GOA) has attracted significant attention due to its simplicity and effective search capabilities. However, its performance deteriorates when dealing with high-dimensional or complex optimization tasks. To address these limitations, this study proposes an improved variant of GOA, named Outpost Multi-population GOA (OMGOA). OMGOA integrates two novel mechanisms: the Outpost mechanism, which enhances local exploitation by guiding agents towards high-potential regions, and the multi-population enhanced mechanism, which promotes global exploration and maintains population diversity through parallel evolution and controlled information exchange. Comprehensive experiments were conducted to evaluate the effectiveness of OMGOA. Ablation studies were performed to assess the individual contributions of each mechanism, while multi-dimensional testing was used to verify robustness and scalability. Comparative experiments show that OMGOA has better optimization performance compared to other similar algorithms. In addition, OMGOA was successfully applied to a real-world engineering problem-lithology prediction from petrophysical logs-where it achieved competitive classification performance.
蚱蜢优化算法（GOA）因其简单性和有效的搜索能力而备受关注。然而，在处理高维或复杂优化任务时，其性能会下降。为了解决这些局限性，本研究提出了一种改进的GOA变体，称为前哨多群体GOA（OMGOA）。OMGOA集成了两种新颖的机制：前哨机制，通过引导智能体前往高潜力区域来增强局部开发；多群体增强机制，通过并行进化和受控信息交换促进全局探索并保持群体多样性。进行了全面的实验以评估OMGOA的有效性。进行了消融研究以评估每种机制的个体贡献，同时使用多维测试来验证鲁棒性和可扩展性。对比实验表明，与其他类似算法相比，OMGOA具有更好的优化性能。此外，OMGOA成功应用于一个实际工程问题——根据岩石物理测井进行岩性预测——并在该问题上取得了具有竞争力的分类性能。
