Golden jackal optimization (GJO) is an effective metaheuristic algorithm that imitates the cooperative hunting behavior of the golden jackal. However, since the update of the prey’s position often depends on the male golden jackal and there is insufficient diversity of golden jackals in some cases, it is prone to falling into a local optimal optimum. In order to address these drawbacks of GJO, this paper proposes an improved algorithm, called a hybrid GJO and golden sine (S) algorithm (Gold-SA) with dynamic lens-imaging (L) learning (LSGJO). First, this paper proposes novel dual golden spiral update rules inspired by Gold-SA. These rules give GJO the ability to think like a human (Gold-SA), making the golden jackal more intelligent in the process of preying, and improving the ability and efficiency of optimization. Second, a novel nonlinear dynamic decreasing scaling factor is introduced into the lens-imaging learning operator to maintain the population diversity. The performance of LSGJO is verified through 23 classical benchmark functions and 3 complex design problems in real scenarios. The experimental results show that LSGJO converges faster and more accurately than 11 state-of-the-art optimization algorithms, the global and local search ability has improved significantly, and the proposed algorithm has shown superior performance in solving constrained problems.

The emergence of battery-free wireless sensor networks (benefiting from the ability to collect energy from the surroundings) has broken through the energy and lifetime limitations of traditional wireless sensor network systems, but also brings challenges to the sharing of network resources. In the multi-channel wireless communication environment, in particular, how to coordinate the communication time and occupied channels of a large number of sensor nodes from the perspective of optimizing the global network has become a research problem that must be solved. To reduce the transmission delay and the usage of wireless channels, a new multi-channel data aggregation scheduling method based on the chaotic firework algorithm is proposed in this paper. With the help of the generation function of feasible solutions, one scheduling set and a firework individual can be rapidly converted to each other. By the operations of firework explosions, the Gaussian mutation, and chaotic exploration, a sub-optimal scheduling set could be found during an acceptable time period. Finally, simulation results show that the new scheduling method has advantages in aggregation delay and occupied channels when compared with existing methods.

The images of surface defects of industrial products contain not only the defect type but also the causal logic related to defective design and manufacturing. This information is recessive and unstructured and difficult to find and use, which cannot provide an apriori basis for solving the problem of product defects in design and manufacturing. Therefore, in this paper, we propose an image semantic refinement recognition method based on causal knowledge for product surface defects. Firstly, an improved ResNet was designed to improve the image classification effect. Then, the causal knowledge graph of surface defects was constructed and stored in Neo4j. Finally, a visualization platform for causal knowledge analysis was developed to realize the causal visualization of the defects in the causal knowledge graph driven by the output data of the network model. In addition, the method is validated by the surface defects dataset. The experimental results show that the average accuracy, recall, and precision of the improved ResNet are improved by 11%, 8.15%, and 8.3%, respectively. Through the application of the visualization platform, the cause results obtained are correct by related analysis and comparison, which can effectively represent the cause of aluminum profile surface defects, verifying the effectiveness of the method proposed in this paper.