Partial Multi-Label Learning (PML) deals with a practical scenario where each instance is associated with a set of candidate labels, among which only a subset corresponds to the ground-truth labels while the others are unrelated. Existing PML methods typically employ label association recovery as a structural disambiguation strategy to identify credible labels. However, these methods attempt to recover associations directly from candidate labels, which leads to unreliable disambiguation due to the distorted label structures. To this end, this paper proposes a novel PML method via dual label association recovery (PML-DLAR).The essential strategy is to first eliminate the spurious correlations in label space before recovering dual label associations. Specifically, a biorthogonal transformation is employed to decouple the instance-level and label-level association structures affected by noisy labels. Subsequently, reliable instance-level associations are reconstructed through global geometric structure alignment between feature and pseudo-label spaces. Finally, class specific feature representations are constructed through class prototypes to guide label-level semantic association recovery in label space. Comprehensive experiments validate the superior performance of PML-DLAR over state-of-the-art methods.

Aiming at the problems of low coverage, poor connectivity, and high energy consumption in 3D heterogeneous wireless sensor networks (3D HWSNs), this article constructs a multi-objective coverage optimization model that balances coverage, connectivity, and energy consumption. Meanwhile, this article proposes a multi-objective spider wasp optimizer (MOSWO) based on a spider wasp optimizer (SWO) to solve the problem. Firstly, incorporating an external archive allows MOSWO to obtain and store Pareto optimal solutions. Secondly, an important-objective trend operator and improving SWO’s mating behavior enhance the algorithm’s optimization ability. Finally, A spherical divergence operator is introduced to reduce the redundancy of sensor nodes. To compare the coverage optimization performance of the proposed algorithm, MOSWO MOAAO, MOEA/D, MOPSO, MOAHA, and NSGA-II are used to solve the MCO problem of 3D HWSNs in obstacle and noobstacle scenes. The experimental results show that the Pareto front of MOSWO is optimal compared to the Pareto fronts of MOAAO, MOEA/D, MOPSO, MOAHA, and NSGA-II. MOSWO performs effectively in improving coverage and network connectivity, reducing energy consumption.

Establishing an accurate equivalent model is a critical foundation to describe the energy conversion characteristics of a photovoltaic system, which can support the research of fault analysis, output power prediction, and performance analysis of the photovoltaic system. However, the widely used equivalent models are highly nonlinear and have many unknown parameters, making it difficult to identify these parameters accurately. Our previous work found that the gaining‐sharing knowledge‐based algorithm (GSK) shows promising performance in solving this problem. But its efficacy is not enough to achieve accurate parameters within a relatively limited computing resource. In this context, a dual‐population GSK algorithm (DPGSK), which introduces a dual‐population evolution strategy for more excellent searchability, is proposed to address this issue. In each iteration, the population splits equally and randomly into two subpopulations, one of which performs the junior gaining‐sharing phase while the other performs the senior gaining‐sharing phase. Then two updated subpopulations merge to form a new population. This allows for a grand reconciliation of convergence speed and population diversity, giving DPGSK powerful optimization performance. Afterward, DPGSK is applied to five photovoltaic models and validated for performance against other advanced metaheuristics. Besides, the impact of different components on DPGSK is also investigated. Results and comparisons show that either component is indispensable to DPGSK, and DPGSK strengthens the convergence and achieves accurate and reliable results, demonstrating its superiority over other algorithms in solving this studied problem.

Fault section diagnosis (FSD) is a critical part of the power system dispatching and control. To diagnose the faulty section(s) correctly, an improved binary variant of gaining-sharing knowledge-based algorithm (GSK) named LOBGSK is presented in this paper. It stretches the original GSK over binary search space so as to solve the 0-1 integer programming FSD problem. In LOBGSK, individuals are encoded by binary numbers and logical operations instead of real arithmetic operations are designed to update the individuals. By this, LOBGSK can avoid transcoding in solving the FSD problem. To validate the effectiveness of LOBGSK, it is first applied to a 4-substation test system considering various fault scenarios. Then it is further implemented to the IEEE 118-bus system and an actual fault event occurred in a practical power grid in Jilin province of China. In addition, the influence of three key parameters of LOBGSK is also investigated. Simulation results show that LOBGSK is robust against its key parameters and can offer a 100% successful rate to diagnose different faults quickly, which is demonstrated by the reported results of some published FSD methods. Furthermore, it outperforms seven state-of-the-art metaheuristic algorithms and the original GSK in solving the FSD problem of power systems.