2.5.2. The Self-Adaptive Differential Evolution (SADE) Algorithm

Differential evolution (*DE*) is a population-based search technique introduced by Storn and Price [53]. The technique is an outstanding tool used to handle stochastic global optimization problems, which requires tuning and varying a few parameters to get the optimized results. The governing parameters of *DE* depend significantly on the nature of the problem to be optimized. However, the optimization process, in which the controlling parameters are tuned using different strategies, is excessively time consuming, making the technique computationally expensive [54]. Qin and Suganthan [55] developed the self-adaptive differential evolution (*SADE*) technique to overcome this drawback. *SADE* is capable of self-adapting the controlling parameters in a much shorter time compared to *DE*. *SADE* is also superior to other optimization algorithms such as particle swarm optimization (PSO), especially for solving numerical problems with medium dimensions [56]. Detailed information on the workflow and the mathematical formulations used in this algorithm have been obtained by many researchers [54,55,57]. *SADE* has been successfully implemented in many application in the petroleum industry, such as oil production optimization [58] and prediction of spud mud rheology [59].
