# Dynamic Layered Dual-Cluster Heads Routing Algorithm Based on Krill Herd Optimization in UWSNs
## 1 Introduction  
Underwater wireless sensor networks (UWSNs) are network monitoring systems which consist of sensor nodes. They can achieve acoustic communication and computation in underwater environment. UWSNs have been used in the fields of water environment monitoring, strategic surveillance, and underwater exploration. Thus, they gain increasing attention from governments and research institutions in various countries. There is no doubt that the UWSNs become a popular research topic today [1]. Sensor nodes use their own battery to provide limited energy. When they stop working and run out of energy, network topology is affected. As such, energy saving becomes a key issue in the research of UWSNs. The design of routing algorithm whose function is to balance energy consumption and prolong the lifetime of networks in UWSNs also becomes a necessity. Traditional routing algorithms can be divided into plane routing algorithms and clustering routing algorithms [2; 3; 4]. The latter is widely preferred than the former because of its satisfactory performance in terms of energy saving [5]. As early as 2000, [PERSON] et al. proposed low-energy adaptive clustering hierarchy (LEACH) algorithm. The cluster heads of LEACH transport data to the base station in a single hop communication manner. This leads to a large amount of information to nodes from the base station, so nodes face \"premature death\" due to running out of energy. [PERSON] et al. proposed the distributed clustering routing algorithm. The algorithm makes the cluster heads generate further rationalization, but a large amount of information exchange leads to the loss of extra energy. As a computing technology that can quickly and efficiently solve complex problems, swarm intelligence has been applied to UWSNs clustering routing algorithm [6]. Swarm intelligence hasvarious techniques, such as the cat swarm optimization algorithm (CSO), the fish swarm algorithm, the ant colony algorithm (ACO), and the particle swarm optimization (PSO) algorithm, among others [7; 8; 9; 10; 11]. In general, swarm intelligence is easy to understand, has low complexity and strong commonality. [PERSON] et al. [7] proposed a clustering routing algorithm based on type-2 fuzzy logic and ACO. In particular, the type-2 fuzzy logic was used to solve the network uncertainty and balance the energy load. ACO was adopted to select the candidate cluster head to reduce the energy consumption. The algorithm presented by these researchers can prolong the network survival time. ACO is constructed solution path by a number of ants together. All of the ants improve the quality of the solution through the legacy and exchange of information in the solution path, and then it achieves the goals of optimization. As a general stochastic optimization method, ant colony algorithm has already been used successfully in a series of combinatorial optimization problems. It also achieved perfect results. However, because the algorithm is a typical probability algorithm, the parameters set in the algorithm is usually determined by experimental methods. This leads to the optimization of the performance that is closely related to people's experience. Thus, it is difficult to optimize the algorithm performance. [PERSON] et al. [8] developed an energy-aware routing algorithm based on cat swarm optimization (CSO). The design of the proposed algorithm was based on a ladder diffusion algorithm to avoid the generation of circle routes but to provide backup routes. In addition, CSO was integrated to effectively provide improved efficiency and reduce the execution time for finding the routing path. The cat swarm algorithm uses the combination rate of the search mode and tracking mode to solve complex optimization problems. The convergence speed of the cat swarm algorithm remains to be further improved. [PERSON] et al. [9] introduced a new dual-cluster heads clustering routing algorithm based on PSO (DC-PSO). This algorithm can share the energy consumption of cluster heads by setting the cluster routing algorithm.