Abstract
In this paper, two algorithms enhanced differential evolution (EDE) and adaptive EDE (AEDE) are proposed. The proposed algorithms improve the robustness of the IoT network without changing the degree distribution of nodes. The EDE algorithm maintains the diversity in a solution space through the tri-vector mutation operation and explores the hidden areas. The crossover phase makes the algorithm's convergence fast towards the global optima. The AEDE dynamically changes the probabilities of multiple operations of the EDE with the changing environment. Also, it maintains the balance between the diversity of solution space and the convergence speed through adaptive probabilities. The EDE performs 7.13%, 31.6% and 41.8% better as compared to GA, SA and HA, respectively. The AEDE outperforms the GA, SA and HA with 11%, 35.3% and 45.4% better efficiency, respectively. The proposed algorithms outperform existing algorithms in terms of robustness and convergence speed.