To tackle the challenge of combat resource allocation problem (CRAP), especially under resource constraints, the dilemma between the efficiency of combat resource utilization and the efficiency of problem-solving. We propose a novel genetic algorithm that integrates a restricted tournament selection strategy with sociological principles, named the restricted tournament selection social genetic algorithm (RTS2GA). Initially, we develop a comprehensive model for the allocation of combat resources that takes into account multiple constraints. Then, building upon the traditional Genetic Algorithm, we introduce a novel selection strategy known as restricted tournament selection to enhance the diversity of the algorithm’s population. In addition, we innovatively incorporate the concept of ‘group effect’ from sociology, adding a socialization operator to the algorithm to accelerate convergence and improve the quality of optimal solutions. Comprehensive evaluation confirms RTS2GA’s trade-off profile: though incurring added computational costs, it achieves competitive convergence speed (marginally behind PSO/MPSO; comparable to GA/DE/GA-APSO) while establishing definitive superiority in global optimization across all five benchmarks.

Sentence classification is one of the basic tasks of natural language processing. Convolution neural network (CNN) has the ability to extract n-grams features through convolutional filters and capture local correlations between consecutive words in parallel, so CNN is a popular neural network architecture to dealing with the task. But restricted by the width of convolutional filters, it is difficult for CNN to capture long term contextual dependencies. Attention is a mechanism that considers global information and pays more attention to keywords in sentences, thus attention mechanism is cooperated with CNN network to improve performance in sentence classification task. In our work, we don't focus on keyword in a sentence, but on which CNN's output feature map is more important. We propose a Squeeze-and-Excitation Convolutional neural Network (SECNN) for sentence classification. SECNN takes the feature maps from multiple CNN as different channels of sentence representation, and then, we can utilize channel attention mechanism, that is SE attention mechanism, to enable the model to learn the attention weights of different channel features. The results show that our model achieves advanced performance in the sentence classification task.