## 2 Related Works  
Swarm Intelligence (SI) is a feature of subjects without intelligence or with simple intelligence exhibiting intelligent behavior through any form of aggregation and collaboration. It is an important branch of artificial intelligence (AI) [15]. Without centralized control and without providing a global model, swarm intelligence provides the basis for finding solutions to complex distributed problems. At present, many research achievements have been made in the field of underwater sensor network coverage control. This section will summarize the coverage control algorithm based on group intelligence optimization.  
[PERSON] [16] proposed an underwater sensor network positioning and deployment scheme based on the genetic algorithm of optimization technology, which determined the fewest number of nodes required to cover an area of interest (AOI). However, this kind of algorithm is obviously easy to fall into local optimum when the network connectivity is not high, and the influence of water flow is not considered. [PERSON] [17] proposed an optimal deployment algorithm based on an artificial fish swarm algorithm. This deployment algorithm simulates the preying and following behaviors of artificial fish in order to determine the maximum coverage value. The proposed artificial fish deployment algorithm improves the coverage performance of the common artificial fish algorithm. The inadequacy is that it does not take into account the self-adaptability of the search step size and the information sharing of all nodes in the network, so it is easy to fall into local optimum in the later stage. [PERSON] [18] proposed the max average coverage deployment (MACD) algorithm, which uses the grid model to simulate the monitoring area and completes node deployment by utilizing the greedy iterative strategy. The MACD can achieve higher network coverage and connectivity rates, even achieving full network coverage and connectivity. However, since high node density is needed for its successful deployment, this algorithm cannot be applied in situations with sparse underwater sensor network deployment.  
In response to the aforementioned shortcomings, [PERSON] [19] proposed a particle swarm-inspired underwater sensor self-deployment (PSSD) algorithm that fully utilizes the behavioral characteristics of particle swarms and effectively solves the network coverage problem. However, there exist two obvious disadvantages in this algorithm, one is that it only considers the network coverage of events, and it is difficult to obtain higher network connectivity rates. In addition, since nodes may move blindly when using this algorithm, given their limited energy and the large energy consumption in an underwater environment, underwater nodes will die due to the rapid exhaustion of energy. The other is that the PSSD algorithm was inspired by the classic group intelligence optimization algorithm-particle swarm optimization (PSO). For the traditional optimization algorithm, PSSD is a simple and effective optimization problem, with one obvious drawback, which is the tendency to fall into local extremes [20].  
Taking into consideration both the effectiveness and the limitations of the above PSSD algorithm, as well as the non-uniform deployment of underwater monitoring nodes, a distributed hybrid fish swarm optimization algorithm (DHFSOA) is proposed. The DHFSOA provides sensor nodes with an autonomous tendency to cover events by simulating fish foraging behavior and congestion control. Additionally, the concept of an \"information pool\" is introduced in order to expand the visual range of nodes and avoid blind movements, thus reducing node energy consumption during deployment.