A learning-guided optimization approach, i.e., a $Q$-Learning-guided Memetic Algorithm (QLMA), is proposed to solve path planning problems in unknown environments. Traditional path planning algorithms in unknown environments have the disadvantages of low efficiency and stability problems. Integrating learning strategies into a memetic algorithm is a natural way to enhance search performance. The proposed algorithm can be divided into two stages: learning and optimization. A $Q$-learning algorithm implements the learning stage, while the optimization stage is implemented by hybridization of a Genetic Algorithm (GA) and Simulated Annealing (SA) algorithm, i.e., a memetic algorithm. The learning stage aims to acquire information from current search states. The $Q$-learning algorithm’s environmental learning capability is formed by rewards received from changes in distance to the target. Therefore, the path strategy could be adjusted continually, and effective paths could be identified. The optimization stage aims to utilize the obtained information and enhance the quality of known paths. A GA improves the global search ability, while an SA algorithm enhances the local search ability. The experimental study was conducted on 30 test scenarios with three types of index points. The proposed algorithm outperforms the other two algorithms significantly in the test scenarios.

Most existing classification algorithms for cardiovascular disease are limited to specific diseases and cannot categorize the severity of the diseases. These algorithms still need to be improved in terms of accuracy and generalizability. Therefore, a hybrid Improved Brain Storm Optimization with Support Vector Machine (IBSO-SVM) for cardiovascular disease classification is proposed. In this study, a knowledge-driven intelligent initialization method is proposed to enhance the optimization capability of IBSO and the accuracy of IBSO-SVM. Experimental evaluations are conducted on multiple real-world datasets, and the results demonstrate the superior performance of IBSO-SVM in cardiac disease datasets. The accuracy of BSO-SVM reaches 100% on the Heart Failure and Heart Disease datasets, and the accuracy of IBSO-SVM reaches 99% on the Stroke dataset and 88% on the Cardiovascular disease dataset.

Studies show that Graph Neural Networks (GNNs) are susceptible to minor perturbations. Therefore, analyzing adversarial attacks on GNNs is crucial in current research. Previous studies used Generative Adversarial Networks to generate a set of fake nodes, injecting them into a clean GNNs to poison the graph structure and evaluate the robustness of GNNs. In the attack process, the computation of new node connections and the attack loss are independent, which affects the attack on the GNN. To improve this, a Fake Node Camouflage Attack based on Mutual Information (FNCAMI) algorithm is proposed. By incorporating Mutual Information (MI) loss, the distribution of nodes injected into the GNNs become more similar to the original nodes, achieving better attack results. Since the loss ratios of GNNs and MI affect performance, we also design an adaptive weighting method. By adjusting the loss weights in real-time through rate changes, larger loss values are obtained, eliminating local optima. The feasibility, effectiveness, and stealthiness of this algorithm are validated on four real datasets. Additionally, we use both global and targeted attacks to test the algorithm’s performance. Comparisons with baseline attack algorithms and ablation experiments demonstrate the efficiency of the FNCAMI algorithm.