Krill herd algorithm (KHA) is an emerging nature-inspired approach that has been successfully applied to optimization. However, KHA may get stuck into local optima owing to its poor exploitation. In this paper, the orthogonal learning (OL) mechanism is incorporated to enhance the performance of KHAfor the first time, then an improved method named orthogonal krill herd algorithm (OKHA) is obtained. Compared with the existing hybridizations of KHA, OKHA could discover more useful information from historical data and construct a more promising solution. The proposed algorithm is applied to solve CEC2017 numerical problems, and its robustness is verified based on the simulation results. Moreover, OKHA is applied to tackle data clustering problems selected from the UCI Machine Learning Repository. The experimental results illustrate that OKHA is superior to or at least competitive with other representative clustering techniques.

中文翻译：

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一种具有正交性的磷虾群新算法及其在数据聚类中的应用

磷虾群算法（KHA）是一种受自然启发的新兴方法，已成功地应用于优化。但是，由于缺乏KHA，KHA可能会陷入局部最优状态。本文首次引入正交学习（OL）机制来提高KHA的性能，然后获得了一种改进的方法，称为正交磷虾群算法（OKHA）。与现有的KHA杂交技术相比，OKHA可以从历史数据中发现更多有用的信息，并构建出更有希望的解决方案。将该算法应用于解决CEC2017数值问题，并基于仿真结果验证了算法的鲁棒性。此外，OKHA还用于解决从UCI机器学习存储库中选择的数据聚类问题。