# An Enhanced Multi-Constraint Optimization Algorithm for Efficient Network Topology Generation
## 2 Related Work  
The optical satellite constellation network, a high-speed data transmission platform, relies significantly on its topology configuration. Enhancing the constellation's structure is crucial for improving the network's reliability, stability, and communication efficiency. Consequently, the study of the optical satellite constellation network's topology seeks to devise an optimized constellation structure that enhances efficient data transmission and communication services, thereby promoting productivity and fostering innovation. The study also assists in addressing inter-satellite connection issues, improving constellation coordination, and augmenting overall efficiency, thus providing valuable insights and technical support for satellite communication advancements. Numerous researchers have proposed various optimization algorithms to enhance network performance. The approach proposed by [PERSON] et al. [8] aimed to maximize system performance and reliability by considering the impact of satellite network topology on link allocation. However, a potential drawback of this method is that it may require substantial computational resources due to the use of Monte Carlo simulations. [PERSON] et al. [9] suggested a multi-path routing algorithm for satellite networks, based on ant colony optimization, which effectively reduces energy consumption while transmitting data efficiently. However, the algorithm's efficiency can be compromised due to the ant colony optimization limitations, satellite node count, and network topology. This optimization can be applied in low earth orbit satellite networks to achieve efficient load balancing [10]. [PERSON] et al. [11] proposed an ant colony optimization-based routing algorithm for medium-to-high earth orbit satellite constellation networks. Though it yields shorter average delays and better network performance, it is unsuitable for dynamically changing network topologies and traffic distribution. [PERSON] et al. [12] optimize airport BHTS design using the particle swarm optimization (PSO) algorithm and effectively solves the optimization problem. Among them, the self-adjusting PSO algorithm performs the best in terms of CPU time. In the optical communication satellite constellation topology design, similar optimization algorithms can also be used, such as applying the PSO algorithm to the inter-satellite link, as well as optimization--optimization of node placement or optimization of bandwidth allocation, etc. [PERSON] et al. [13] investigated the topology design of optical intersatellite links for future navigation satellite networks, introducing a topology design algorithm rooted in [PERSON]'s algorithm. The intersatellite links' topology structure is optimizedto form the minimum spanning tree, with dynamic programming being employed to optimize the bandwidth allocation of these links. However, further optimization of network performance necessitates the incorporation of a wider range of topology control strategies. [PERSON] et al. [14] introduced a topology optimization scheme for space information networks, which utilizes the generation tree algorithm to improve both network performance and stability. Particle swarm optimization algorithms have also been applied by researchers to optimize the topology structure of inter-satellite networks. [PERSON] et al. [15] and [PERSON] et al. [16] centered their research on using the particle swarm optimization algorithm to optimize the topology structure of inter-satellite networks in the Global Navigation Satellite System (GNSS), with the goal of maximizing system performance and reliability. Furthermore, these studies took into account the impact of satellite network topology on link allocation. [PERSON] et al. [17] proposed a novel topology optimization algorithm, reformulating the optimization problem of satellite-to-satellite links as an integer programming problem.