An enhanced QoS approach for multi-objective optimization using social spider optimization 5G enable IoMT network

Social Spider Optimization (SSO) is a swarm algorithm, based on the mating and cooperating behaviour of social spiders. This approach is basically used for global optimum search during allocation of resources and data compression in Internet of Things (IoTs) networks. Some of the Swaram Intelligence (SI) algorithm such as Particle Swarm Optimization (PSO) algorithm, Ant Colony Optimization (ACO), Social Spider Optimization (SSO), Parallel Social Spider Optimization (PSSO) are numerically simulating to decrease computation time for resource allocation, to improve the energy efficiency and data compression rate in IoT networks. However, still some deficiencies exist in the system. To overcome these deficiencies, an Adaptive Social Spider Optimization (ASSO) algorithm is suggested for global search optimum. This proposed model will update the both the female and male individuals’ positions, and each individual position can be calculated during the search process. This will help to strengthen the ability of the search performance. ASSO has been compared with the existing algorithm to obtain experimental results that shows the suggested ASSO conquered better result with an allocation rate of 96.90% and consumed energy 0.6mj that is comparatively better than PSSO.