Recently, swarm intelligence algorithms have received much attention because of their flexibility for solving complex problems in the real world. Recently, a new algorithm called the colony predation algorithm (CPA) has been proposed, taking inspiration from the predatory habits of groups in nature. However, CPA suffers from poor exploratory ability and cannot always escape solutions known as local optima. Therefore, to improve the global search capability of CPA, an improved variant (OLCPA) incorporating an orthogonal learning strategy is proposed in this paper. Then, considering the fact that the swarm intelligence algorithm can go beyond the local optimum and find the global optimum solution, a novel OLCPA-CNN model is proposed, which uses the OLCPA algorithm to tune the parameters of the convolutional neural network. To verify the performance of OLCPA, comparison experiments are designed to compare with other traditional metaheuristics and advanced algorithms on IEEE CEC 2017 benchmark functions. The experimental results show that OLCPA ranks first in performance compared to the other algorithms. Additionally, the OLCPA-CNN model achieves high accuracy rates of 97.7% and 97.8% in classifying the MIT-BIH Arrhythmia and European ST-T datasets.

Geomagnetic field observation data are valuable resources for seismic geomagnetic research. However, the construction of subways, light railroads, and high-voltage direct current transmission lines in recent years has seriously affected the output data of geomagnetic field observation instruments. To accurately and efficiently classify high-voltage direct current interference events in geomagnetic field observation data, this paper proposed a novel model named HVDC-DNN-MFO. This model integrated convolutional neural network, convolutional block attention module, and the gated recurrent unit, a variant of recurrent neural network, to automatically extract fine features of high-voltage direct current interference events. Additionally, the moth-flame optimization algorithm was introduced to optimize the model’s hyperparameters. This study used manually pre-processed records of high-voltage direct current interference events and the z-component second data of geomagnetic field observation data from January 1, 2014, to January 1, 2019, to make samples, and the homothetic transformation method was applied to unify the length of high-voltage direct current samples with different duration events to 7200s. The experimental results demonstrated that the F1-score, accuracy rate, and recall rate of the HVDC-DNN-MFO model proposed in this study reached 0.99, 99.05%, and 98.59%, respectively, outperforming the state-of-the-art methods.