“…Some evolutionary algorithms, including simulated annealing (SA) [ 20 ], genetic algorithm (GA) [ 21 ], particle swarm optimization (PSO) [ 22 , 23 ], differential evolution (DE) [ 24 ], and immune clonal optimization (ICO) [ 25 ], are employed to deal with this issue, with the help of their effective computational features in the swarm intelligence paradigm. Through the use of those algorithms, a satisfactory solution effect was achieved during the resource allocation in CRNs [ 20 , 21 , 22 , 23 , 24 , 25 ]. However, there is still room to further improve the optimization effect due to the inherent disadvantages of those algorithms in the practical engineering applications of IoT, such as the possible ease of falling into the local optimal solutions in some cases, the difficulty adjusting many key parameters during the implementation process of the algorithms, and many drawbacks [ 26 , 27 , 28 , 29 , 30 , 31 , 32 ].…”