Abstract

Multiobjective or multiple-objective optimization (MOO) employs previously discussed optimization techniques (deterministic and stochastic) and optimizer types (classical and nonclassical) (Figure 4.1). MOO increases optimization complexity somewhat. How much, depends on how many objectives are considered simultaneously. MOO methods help quantify trade-offs between conflicting objectives (two objectives conflict if one objective attainment cannot improve without harming attainment of the other objective). MOO methods commonly used in groundwater optimization are (1) e-constraint, (2) weighting, and (3) goal programming. Again, these methods employ already-discussed optimization techniques and optimizer types.