Abstract

Particle Swarm Optimization (PSO) was proposed in 1995, and its appearance has attracted the attention and research of many scholars. This algorithm is different from traditional optimization algorithms. It is actually a new type of group optimization algorithm. Its main advantages are simple structure, few parameters, fast particle convergence and strong optimization ability. With the rise of cloud computing technology, software development, delivery, development and usage models have undergone tremendous changes. Software developers no longer use traditional design and development models, and software development models based on service composition technology have become a new trend. Developers do not need to be interested in implementing many basic network application technologies when delivering software, but should only focus on implementing business logic. Delivering the business logic of the application to the cloud-based network support platform with the idea of platform as a service can make it an expanding network application for large-scale users. We call this platform PaaS cloud computing platform. Scalability and low rental costs allow software providers to easily implement applications, thereby greatly reducing workload and maintenance. Users no longer need to purchase or develop applications, but can customize and use the software as a service model. Use software delivered to the platform to meet individual needs. Although the research on collaborative 5G PaaS platform management and control platform technology is constantly evolving, Cui still has many shortcomings. In order to solve the problem of the lack of a unified standard platform in the cloud computing environment, this paper uses the particle weight method and the discrete particle algorithm to study the collaborative 5G PaaS collaborative management control platform. Through the method in this paper, it can be concluded that the cloud-side collaborative 5G PaaS collaborative management platform technology based on particle swarm algorithm is 24% faster than the traditional method.

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