Internet of Things (IoT) is a huge network and establishes ubiquitous connections between smart devices and objects. The flourishing of IoT leads to an unprecedented data explosion, traditional data storing or processing techniques have the problem of low efficiency, and if the data is used maliciously, the security loss may be further caused. Multi-cloud is a highperformance secure computing platform, which combines multiple cloud providers for data processing, and the distributed multicloud platform ensures the security of data to some extent. Based on multi-cloud and task scheduling in IoT, this paper constructs a many-objective distributed scheduling model, which includes six objectives of total time, cost, cloud throughput, energy consumption, resource utilization, and balancing load. Further, this paper presents a many-objective intelligent algorithm with sine function to implement the model, which considers the variation tendency of diversity strategy in the population is similar to the sine function. The experimental results demonstrate excellent scheduling efficiency and hence enhancing the security. This work provides a new idea for addressing the difficult problem of data processing in IoT.

While Industrial Internet of Things (IIoT) can support efficient control of physical world through large amounts of industrial data, data security has been a challenge due to various interconnections and accesses. Blockchain technology can support security and privacy preservation in IIoT data with its trusted and reliable security mechanism. Sharding technology can help improve the overall throughput and scalability of blockchain networks. However, the effectiveness of sharding is still challenging due to the uneven distribution of malicious nodes. By aiming to improve the performance of blockchain networks and reduce the possibility of malicious node aggregation, this paper proposes a many-objective optimization algorithm based on the dynamic reward and penalty mechanism (MaOEA-DRP) to optimize shard validation validity model. Then, an optimal blockchain sharding scheme is obtained. The simulation results demonstrate that our proposed algorithm can significantly improve throughput and validity of sharding for better security in blockchain-enabled IIoT.

Task scheduling problem refers to how to reasonably arrange many tasks provided by users in virtual machines, which is very important in the cloud computing. And the quality of the scheduling performance directly affects the customer satisfaction and the provider benefits. In order to describe the task scheduling problem of cloud computing more precisely and improve the scheduling performance. This paper establishes many-objective cloud model, including four objectives: minimizing time, minimizing costs, maximizing resource utilization, and balancing load. At the same time, a many-objective optimization algorithm based on hybrid angles (MaOEA-HA) is proposed to solve this model. Hybrid angle strategy is designed to optimize the algorithm better, which combines two angle strategies: individual-to-individual angle and individual-to-reference point angle. One by one elimination strategy was introduced to remain individuals with better performance. By comparing with five other advanced many-objective optimization algorithms, MaOEA-HA shows the best performance on the DTLZ test suite. Moreover, different algorithms are applied to solve the cloud task scheduling problem, and MaOEA-HA algorithm achieves best results.

As an emerging paradigm, Cyber-Physical Social System (CPSS) can provide humans with efficient, convenient and personalized services. However, due to the explosive growth of user data, massive and high-dimensional data has become a key issue for CPSS services. In the past, researchers mainly focused on big data analysis methods, ignoring the processing of high-dimensional data before analysis. In response to above problem, this paper focus on the redundant attributes in high-dimensional data. By using intelligent optimization technology, the vision model is optimized for four conflicting goals at the same time, so as to better solve the problem. In order to support this model, a collaboration mechanism (MaOEA-CM) was designed. The algorithm uses the combination of sum of objectives and shift-based density estimation (ISDE+) indicator as the criterion of gradual co-evolution, and guides individuals to search in the high convergence and multiple directions of CPSS. Experiments on four data sets show that the proposed algorithm and model can significantly improve the accuracy of data processing in CPSS and have higher performance than other feature selection models.