# Objectives

Objectives are metrics to be minimized or maximized in an optimization exploration. Minimizing mass to find a lightweight design is a common example.

## Single and Multi-Objective Optimization Problems

such that $gj(x)<0$

When dealing with multiple objectives $({f}_{1},{f}_{2},\mathrm{...})$ it is unlikely that one design will have minimum objective function values for all objectives. As a result, in MOO applications an optimal Pareto front is searched for instead of an optimal design. Optimal Pareto front is a collection of non-dominated designs. A non-dominated design has a lower objective function value than others with respect to at least one objective.

Usually the computational effort required to solve MOO problems are significantly more compared to single objective optimization problems. In cases where solving MOO problems are prohibitive, these problems have been converted to a single objective problem by summing all the objectives (Weighted Sum Method).

When dealing with probabilistic variables, the objective function also has an associated distribution. When doing robust optimization, instead of the deterministic objective, the objective function is the value of the objective distribution at a specified value of the cumulative distribution function (CDF). A minimization problem might use the 95% value of the CDF (default value), and a maximization problem might use the 5% value of the CDF.

## Objective Types

- Minimize and Maximize
- When creating an objective that is of type Minimize or Maximize, you can edit the Weighted sum field to create a weighted sum of all objectives. If the Weighted sum field is edited, only Adaptive Response Surface Method, Method of Feasible Directions, Genetic Algorithm, Sequential Quadratic Programming methods are available in the Specifications step. If multiple Minimize and/or Maximize type objectives are created, but no weights are defined, only Multi - Objective Genetic Algorithm and Global Response Search Method are available.
- System Identification
- Attempts to minimize the difference between the output response values
and the target values of selected objectives. Examples for typical
applications are experimental curve fitting or parameter fitting. The
objective function is formulated as a least squares formula, where
${\tilde{f}}_{i}$
is the target value of the ith output
response.$$\mathrm{min}{\displaystyle \sum {\left(\frac{{f}_{i}-{\tilde{f}}_{i}}{{\tilde{f}}_{i}}\right)}^{2}}$$
- MinMax and MaxMin
- Used to solve problems where the maximum (or minimum) of an output response is minimized (or maximized).