This work presents the usefulness of the algorithm-specific parameter-less optimization algorithms for single-objective optimization of a cloud radio access network (C-RAN). A case study is presented with the C-RAN power consumption minimization as the objective and remote radio head (RRH) transmission power and BBU processing frequency as variables with certain ranges and constraints. The algorithm-specific parameter-less optimization algorithms, like the teaching–learning-based optimization (TLBO) algorithm, the Jaya algorithm, and the three Rao algorithms, are used for optimizing the objective function. The results are contrasted with those of particle swarm optimization (PSO) and genetic algorithms (GA). The algorithm-specific parameter-less algorithms have been shown to be user-friendly and sufficiently efficient to address the C-RAN optimization challenges.