Exploring algorithmic solutions and network modelling to address optimisation challenges in IoT environments

Internet of things (IoT) is a transformative technology, reshaping the landscape of connectivity and information exchange. It represents an intricate network of physical devices, vehicles, appliances, and other objects embedded with sensors, software, and connectivity, enabling them to collect and exchange data effortlessly. The paper focuses on measuring the efficacy of optimisation algorithms, namely the hybrid simulated annealing-local search algorithm (SA-LSA), genetic algorithm (GA), differential evolution (DE), and simulated annealing (SA), in addressing multi-objective optimisation challenges and complex function minimisation scenarios. It aims to provide a comprehensive understanding of selecting appropriate algorithms for diverse optimisation challenges, considering factors such as solution space complexity, exploration-exploitation trade-off preferences, and convergence speed. The potential of this work lies in contributing valuable insights into the performances of optimisation algorithms, specifically in navigating trade-offs and converging towards optimal solutions. This work conducts a comparative analysis of algorithms, evaluating the overall performance to provide insights into their strengths and weaknesses, facilitating the selection of optimisation approaches for specific applications spanning multi-objective scenarios and complex function minimisation tasks.