Simultaneous multi-loop PID tuning is a procedure for the simultaneous on-line tuning of PID gains in a cluster of PID controllers operating in a closed loop within a multi-variable process. In the previous work , a simplified closed-loop system consisting of a multi-input-multi-output (MIMO) nonlinear boiler/turbine model and a nonlinear cluster of six PID-type controllers was specified, with cross-coupling of the variables similar to that in a real power plant, and a local, gradient based, non-model-based method referred to as IFT (iterative feedback tuning) was tested on this system. Based on the figure of merit for the control system performance, the IFT was shown to deliver performance favorably comparable to that attained through empirical tuning by a tuning expert. However, being a gradient-based technique, IFT is capable of finding only local minima potentially located arbitrarily far from the global one. Therefore, it is of interest to investigate performance of model-based, global, non-gradient-based optimizers in simultaneous multi-loop PID tuning. However, before addressing tuning for the global minimum, it is of interest to assess the performance of the global non-gradient-based optimizers in the local simultaneous multi-loop PID tuning. This task is carried out in the present work, comparing the performance of IFT with that of three global optimization techniques: particle swarm optimization (PSO), simulated annealing (SA), and genetic algorithms (GA), using the same model. It is shown that each optimizer is capable of attaining performance comparable to that attained by IFT. The global techniques are compared, and PSO is found to be the least complex, while yielding the tuning performance comparable to that attained by the SA and GA techniques.