For these reasons, for the last decade, many researchers in the field have changed direction, leaving aside traditional optimization techniques based on linear and nonlinear programming and embarked on the implementation of Evolutionary Algorithms: Genetic Algorithms [3–6]; Ant Colony Optimization [7,8]; Simulated Annealing [9]; Shuffled Complex Evolution [10]; and Harmony Search [11], among others.