where the algorithms search for either the minimum or maximum of an objective function, depending on the goal. One way of handling multi-objective optimization is to incorporate all the objectives (suitably weighted) in a single function, thereby reducing the problem to one of single objective optimization again. This technique has the disadvantage, however, that these weights must be provided a priori, which can influence the solution to a large degree. Moreover, if the goals are very different in substance (for example cost and efficiency) it can be difficult, or even meaningless, to try to produce a single all-inclusive objective function.

True multi-objective optimization techniques overcome these problems by keeping the objectives separate during the optimization process. It should be noted that in cases with opposing objectives for example minimizing a beam’s weight, and also it’s deformation under load, there will frequently be no single optimum, since any solution will be a compromise. The role of the optimization algorithm is then to identify the solutions which lie on the trade-off curve, known as the Pareto Frontier (named after the Italian-French economist, Vilfredo Pareto). These solutions all have the characteristic that none of the objectives can be improved without prejudicing another.

The name modeFRONTIER^® reflects the fact that the program is capable of performing true multi-objective optimization by establishing a Pareto Frontier.