Seagull Optimization Algorithm for Underwater Sensor Network Localization

This paper presents a new technique to a position sensor in UWSNs with the recognition of the Seagull Optimization Algorithm. UWSNs are particularly useful for applications in environmental monitoring and military monitoring, but accurate sensor localization is difficult due to the signal loss in underwater environments and in the presence of external factors. Based on the Motivation Model, the SOA, motivated by the seagulls’ distributed foraging behavior, is a possible solution to the dilemma by striking a midway between exploration and exploitation. The synthesis of exact methodology is outlined here along with the creation of synthetic dataset and a proof of concept MATLAB code for the evaluation of the proposed SOA-based localization algorithm. A performance evaluation, which involved the use of computational simulations on a wide variety of antennas, clearly shows that the SOA achieves a higher localization accuracy in much shorter time, while consuming less power compared to other methods such as PSO, GA, or hybrid methods. The stability is one more strongest side of proposed algorithm which has been confirmed by robustness analysis, so it can be effectively used in dynamic underwater environment. The current research is not only useful in showing how the SOA can be used for the UWSN localization in specific domains, but also valuable to the development of the new UWSN localization method that is efficient and accurate, for the further expansion of the UWSN in the vital marine sphere.