## 2 Related Work  
Many traditional techniques are proposed to handle the issue of scheduling the beacon nodes to some extent such as [10; 11; 12; 13; 14; 15] but in fact no one takes the inspiration from Bio-Inspired Algorithms (BIAs) particularly in the field of UASNs [6]. Energy-efficient, low-latency scheduling has been addressed in the past, but they only aim to optimize traffic in one direction (broadcast or converge cast). For tree-based networks, a prior study describes the low-latency two-way beacon scheduling (LTBS) issue [16], where beacon nodes provide broadcast and converge cast traffics. This scheme does not cover the localization error and ignores the latency issue with adopting the two-way scheduling. Many techniques like proportion of line-of-sight (LoS) beacon scheduling based on angle of information/arrival, Time Difference of Arrival (TDoA) based scheduling, etc. [17] worked well in WSNs but failed underwater due to compatibility issues. For node scheduling, underwater metaheuristic techniques are used to optimize the scheduling by introducing shiftable nodes. The major reasons are due to dynamic topology, local work optima, link quality, and support for multi-path options to reach the destination within the shortest time. Sufficient research focuses on ensuring the successful operation of UASNs in challenging conditions, including application, transportation, network, medium access, synchronization, and localization. This takes into account the difficulties caused by mobility and energy limitations [18]. A conventional oceanographic technology known as \"acoustic based localization\" uses either the Short Base Line (SBL) or Long Base Line acoustic-based procedures (LBL) [19; 20]. Due to the high complexity of the procedures, semi-centralized and distributed localization strategies for UASNs must be developed. Article [21] reviewed various range-based projection and localization phases in 3D-UASNs using Angle of Arrival (AoA) conversion to 2D-UASNs optimization-based good localization ratio and coverage while consuming less energy. Although using a wide array of beacon nodes increases the computational complexity, it also reduces the energy consumption, but because there are fewer unknown nodes, this approach may be used to locate more mobile nodes with less energy and less time expense than using a narrow array of beacon nodes. SOSNET (Smart Ocean Underwater Sensor Network) [22] introduced adaptive node clustering techniques that employ a moth flame optimizer (MFO) [23] for selecting the nearest optimal cluster. MFO is basically a bio-inspired technique that detects the movement of moths toward light (it is the same as firefly tendency to move towards light). SOSNET-FMO [22; 23] was compared with other algorithms, including Grey Wolf Optimization (GWO) [24] and Ant Colony Optimization (ACO) [25]. Transmission range, node density, and grid size are the comparison parameters that show the MFO outperformed the other schemes. To enhance the reliability of data transmission through a hybrid metaheuristic algorithm, GSLS was performed in [26]. This algorithm integrates a Global Search Algorithm (GSA) and a Local Search Algorithm (LSA). The GSA, inspired by the principles of Genetic Algorithms, and the LSA, derived from scattered search techniques, function concurrently to effectively explore the problem space. This approach not only reduces runtime but also improves the quality of the path taken. An innovative multi-objective optimization (MOO) agent that integrates Particle Swarm Grey Wolf Optimization (PSGWO) with inverse fuzzy ranking is proposed in [27]. The enhanced PSGWO model was then employed to create a population- and multi-criteria-based soft computing algorithm known as fuzzy PSGWO. Additionally, the results indicate lower average values for the fitness function and demonstrate the computational efficiency of the proposed algorithm. In general, the GA [28] is a bio-inspired optimization technique that mimics the process of natural selection.