“…In this context, different optimization techniques, such as cuckoo search algorithm (CSA) [14], biogeography-based optimization (BBO) [16], firefly algorithm (FA) [17], hybrid FA-pattern search [18], krill herd algorithm [19], flower pollination algorithm (FPA) [12], hybrid flower pollination algorithm (HFPA) [20], grey wolf optimization algorithm (GWO) [21], bacterial foraging algorithm [22], particle swarm optimization (PSO) [23], differential evolution [24], artificial bee colony (ABC) [25], whale optimization algorithm (WOA) [26], moth-flame optimization algorithm (MOA) [27], dragonfly algorithm (DA) [13] have been developed and can be used to compute the optimal PID controller gains of many dynamic systems. However, useful algorithms' performance depends on the adequate search procedure, which implies the correct objective function definition, search limits, the setting the search algorithm control parameters, and the complexity of the analysis system.…”