Optimization in water systems is considerably more difficult than simulation of these systems. Typically, it is a constrained nonlinear search problem involving both continuous and discrete variables. Thus the problem is a mixed continuous and discrete constrained nonlinear optimization problem that is often highly dimensional. There may be many local optima in the search space and there is no single optimization model or search algorithm to solve the problem without compromising solution accuracy, computational efficiency and problem completeness

Classical methods of optimization involve the use of gradients or higher-order derivatives of the fitness function. But they are not well suited for many real world problems since they are not able to process inaccurate, noisy, discrete and complex data. Thus robust methods of optimization are often required to generate suitable results.

For the last decade, many researchers in the water field have shifted direction, leaving aside traditional optimization techniques based on linear and nonlinear programming and embarking on the implementation of Evolutionary Algorithms: Genetic Algorithms; Ant Colony Optimization; Simulated Annealing; Shuffled Complex Evolution; Harmony Search; Particle Swarm Optimization, among others.