In this paper, a cable aging state assessment method based on the combination of XGBoost and FocalLoss was proposed. Firstly, the FocalLoss function is used to deal with the problem of small cable sample data and serious imbalance in the ratio between classes. Secondly, the FocalLoss function is used as a custom loss function in the XGBoost algorithm, and the combination of the two can achieve effective assessment of cable aging status by extracting key features of cables. Finally, it is verified by example that the evaluation method can effectively deal with the problem of small number of samples and imbalance, and the accuracy of cable aging state evaluation is significantly improved, which can provide a new direction for cable aging state evaluation.
In the process of traditional weapon-target assignment, there are two critical issues that have not been well-studied: (1) the waste of firepower resources and (2) the lack of description of the relationship between targets. Towards this end, we propose an improved weapon-target assignment model, which combines the damage probability, the weapon resource consumption and the relationship coefficient between the targets. On this basic, we design a multi-objective whale optimization algorithm based on grid division (GDMOWOA). Specifically, the algorithm uses the grid partitioning method to sort the population non-dominated, selects the optimal individual by calculating the grid number and density, and introduces an external pareto archive to maintain the population diversity. Simulation experiments are conducted to verify the rationality and effectiveness of our solution. The results show that our algorithm has better effectiveness and superiority compared with other classical algorithms, and can effectively solve the problem of weapon-target assignment.
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