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The non-dominated sorting genetic algorithm III (NSGA-III) has recently been proposed to solve many-objective optimization problems (MaOPs). While this algorithm achieves good diversity, its convergence is unsatisfactory. In order to improve the convergence, we propose an improved NSGA-III using a genetic K-means clustering algorithm (NSGA-III-GKM), which can also ensure diversity and automatically provide the number and direction vector of the subspaces. Compared with the NSGA-III, the proposed NSGA-III-GKM has two key features. First, the initial reference points are clustered using a GKM clustering algorithm, which realizes automatic learning of the number of clusters. Second, as the reference points are replaced by cluster centers, a penalty-based boundary intersection (PBI) aggregation function is introduced to replace the perpendicular distance. The proposed NSGA-III-GKM and other similar optimization algorithms (NSGA-III, MOEA/D, U-NSGA-III, DC-NSGA-III and B-NSGA-III) are tested on DTLZ test problems and UF test problems. The simulation results demonstrate that the NSGA-III-GKM exhibits better diversity and convergence performance than the other algorithms.

In order to overcome a series of difficulties faced by the preventive medicine industry, such as the small number of doctors and the difficulty for patients to get comprehensive and systematic rehabilitation treatment, this paper proposes a novel innovation system of preventive medicine based on big data technology. The innovation system fully combines the characteristics of big data technology and introduces big data thinking into the preventive medicine industry. Based on this, the innovation system can also build medical big data of patients, which can not only make patients and their families clearly understand the condition, reduce the workload of doctors, but also improve the efficiency of medical treatment. At the same time, the innovative system not only considers the patients, but also fully considers the physical problems of healthy people, and implements the concept of disease prevention. The results show that the innovation system can fundamentally improve the intelligence and scientificity of preventive medicine industry, and then realize the transformation from traditional preventive medicine to modern preventive medicine.

Education in computer science disciplines, such as Linux, always focuses on both theoretical and implementation level of technology. However, the performance of traditional teaching approach is not satisfied in practice. This paper firstly gives two important issues in teaching which are ldquotask-drivenrdquo and rdquoobjective-orientedrdquo, then discusses how to integrate them into a new education method, and finally describes the application of this method in Linux curriculum as a case study.