Identifying photovoltaic (PV) parameters accurately and reliably can be conducive to the effective use of solar energy. The grey wolf optimizer (GWO) that was proposed recently is an effective nature-inspired method and has become an effective way to solve PV parameter identification. However, determining PV parameters is typically regarded as a multimodal optimization, which is a challenging optimization problem; thus, the original GWO still has the problem of insufficient accuracy and reliability when identifying PV parameters. In this study, an enhanced grey wolf optimizer with fusion strategies (EGWOFS) is proposed to overcome these shortcomings. First, a modified multiple learning backtracking search algorithm (MMLBSA) is designed to ameliorate the global exploration potential of the original GWO. Second, a dynamic spiral updating position strategy (DSUPS) is constructed to promote the performance of local exploitation. Finally, the proposed EGWOFS is verified by two groups of test data, which include three types of PV test models and experimental data extracted from the manufacturer’s data sheet. Experiments show that the overall performance of the proposed EGWOFS achieves competitive or better results in terms of accuracy and reliability for most test models.

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Deep neural network (DNN) has replaced humans to make decisions in many security-critical senses such as face recognition and automatic drive. Essentially, researchers try to teach DNN to simulate human behavior. However, many evidences show that there is a huge gap between humans and DNN, which has raised lots of security concern. Adversarial sample is a common way to show the gap between DNN and humans in recognizing objects with similar appearance. However, we argue that the difference is not limited to adversarial samples. Hence, this paper explores such differences in a new way by generating fooling samples in 3D point cloud domain. Specifically, the fooling point cloud is hardly recognized by human vision but is classified to the target class by the victim 3D point cloud DNN (3D DNN) with more than 99.99 % confidence. Furthermore, to search for the optimal fooling point cloud, a new evolutionary algorithm named Multielites Harris Hawk Optimization (MEHHO) with enhanced exploitation ability is designed. On one hand, our experiments demonstrate that: (1) 3D DNN tends to learn high-level features of one object; (2) 3D DNN that makes decisions relying on more points is more robust; and (3) the gap is hardly learned by 3D DNN. On the other hand, the comparison experiments show that the designed MEHHO outperforms the SOTA evolutionary algorithms w.r.t. statistics and convergence results.