Topological optimization is an innovative method to realize the lightweight design. This paper proposes a hybrid topology optimization method that combines the SIMP (solid isotropic material with penalization) method and genetic algorithm (GA), called the SIMP-GA method. In the method, SIMP is used to update the chromosomes, which can accelerate convergence. The filtering scheme in the SIMP method can filter unconnected elements to ensure the connectivity of the structure. We studied the influence of varying the filtering radius on the optimized structure. Simultaneously, in the SIMP-GA method, each element is regarded as a gene, which controls the population number to a certain extent, reduces the amount of calculation, and improves the calculation efficiency. The calculation of some typical examples proves that the SIMP-GA method can obtain a better solution than the gradient-based method. Compared with the conventional genetic algorithm and GA-BESO (Bi-directional Evolutionary Structural Optimization) method, the calculation efficiency of the proposed method is higher and similar results are obtained. The innovative topology optimization method could be an effective way for structural lightweight design.

Adaptive cockpit displays aim to support pilots' situation awareness by emphasizing information relevant to the current task. This requires estimating how priorities change across information categories as local events unfold. Predefined task-display mappings may miss these changes, while purely data-driven methods often lack explicit representations linking evidence sources and task constraints to information needs. This study proposes the Cognitive Information Graph for Dynamic Information Prioritization (CIG-DIP) model. A fixed graph represents admissible relations among task evidence, task constraints, cognitive demands, and information categories. Continuous multimodal observations update node states, edge gates, and path strengths. Sparse path competition and the joint aggregation of information node states and path contributions yield a priority distribution across six information categories. The model also outputs the principal supporting paths. The model was evaluated using controlled air-to-air task data from 20 participants, event-level soft labels from seven experts, and five-fold cross-validation with disjoint participant groups. CIG-DIP achieved a top-3 set accuracy of 0.9302 and a Jensen–Shannon divergence of 0.0087, with the best point estimates on all primary metrics among evaluated models. In a within-subject experiment with 10 independently recruited participants, the complete CIG-DIP-based adaptive display configuration improved situation awareness and reduced subjective workload relative to a static display (pHolm=0.032 and pHolm<0.001, respectively, after Holm correction). The proportion of fixation time allocated to the three categories ranked highest by experts also increased. These results support semantically traceable prioritization within the specified task contexts and demonstrate system-level benefits in controlled simulations.