###### Abstract  
This research introduces a hybrid approach combining bio- and nature-inspired metaheuristic algorithms to enhance scheduling efficiency and minimize energy consumption in Underwater Acoustic Sensor Networks (UASNs). Five hybridized algorithms are designed to efficiently schedule nodes, reducing energy costs compared to existing methods, and addressing the challenge of unscheduled nodes within the communication network. The hybridization techniques such as Elephant Herding Optimization (EHO) with Genetic Algorithm (GA), Firefly Algorithm (FA), Levy Firefly Algorithm (LFA), Bacterial Foraging Algorithm (BFA), and Binary Particle Swarm Optimization (BPSO) are used for optimization. To implement these optimization techniques, the Scheduled Routing Algorithm for Localization (SRAL) is introduced, aiming to enhance node scheduling and localization. This framework is crucial for improving data delivery, optimizing Route REQuest (RREQ) and Routing Overhead (RO), while minimizing Average End-to-End (AE2E) delays and localization errors. The challenges of node localization, RREQ reconstruction at the beacon level, and increased RO, along with End-to-End delays and unreliable data forwarding, have a significant impact on overall communication in underwater environments. The proposed framework, along with the hybridized metaheuristic algorithms, show great potential in improving node localization, optimizing scheduling, reducing energy costs, and enhancing reliable data delivery in the Internet of Underwater Things (IoUT)-based network. bio-inspired; genetic algorithm; beacon node localization; energy reduction; metaheuristic algorithms 202412347  
27 September 20243  
to find the optimal solution to a problem [2]. Metaheuristic optimization algorithms [3] are generally divided into two categories: population-based and individual-based. Population-based algorithms, such as Genetic Algorithms (GA), Firefly Algorithm (FA), Levy Firefly Algorithm (LFA), Bacterial Foraging Algorithm (BFA), etc., work by maintaining a population of candidate solutions and repeatedly applying genetic operators such as selection, crossover, and mutation to find the optimal solution [2; 3; 4; 5]. Individual-based algorithms such as Binary Particle Swarm optimization (BPSO), Ant Colony Optimization (ACO), etc. whichthat simulate annealing and particle swarm optimization, work by iteratively modifying a single candidate solution to find the optimal solution. Metaheuristic optimization algorithms have a wide range of applications including engineering, physics, and computer science [5]. They have been used to solve problems such as scheduling, logistics, object optimization, and ranking of nodes in wireless networks. One of the key advantages of metaheuristic optimization algorithms is their ability to find approximate solutions to complex problems, even in the presence of noise and uncertainty. They are also relatively easy to implement and can often be applied to problems with many variables or a complex and non-linear structure. However, bio-inspired approaches in computer science have gained wider adoption within the research community compared to other fields. Additionally, solutions to bio-related issues and computational challenges are increasingly reliable and effective, enhancing user satisfaction and computational performance [6]. In this study, metaheuristic algorithms are used in Underwater Acoustic Sensor Networks (UASNs) to optimize and further hybridized with Elephant Herding Optimization (EHO) [7] for various aspects of the network, such as scheduling/ranking of nodes, path routing, sensor deployment, and so on. The specific algorithms used include Genetic Algorithm (GA), Bacterial Foraging Algorithm (BFA), Firefly Algorithm (FA) and its latest version Levy Firefly Algorithm (LFA), and Binary Particle Swarm Optimization (BPSO), all of which are hybridized with Elephant Herding Optimization (EHO).