Many real-world design tasks involve optimizing a vector of objective functions on a feasible decision variable space. These objective functions are often non-
commensurable and in competition with each other, and cannot be simply aggre-
aggregated into a scalar function for optimization. This type of problem is known as the
multiobjective (MO) optimization problem, for which the solution is a family of
points known as a Pareio optimal set, where each objective component of any
member in the set can only be improved by degrading at least one of its other ob-
objective components.