Introduction
Process Integration
Design Optimization
- DOE
- Schedulers
- RSMs
- Single and Multi-Objectiveness
- Decision Support Tool
- Robust Design Optimization
Animated Demos
Single and Multiobjective Optimization
Generally speaking, optimization can be either single-objective or multiobjective. An attempt to optimize a design or system where there is only one objective usually entails the use of gradient methods where the algorithms search for either the minimum or maximum of an objective function, depending on the goal. One way of handling multiobjective 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 multiobjective optimization techniques overcome these problems by keeping the objectives separate during the optimization process. It should be kept in mind that in cases with opposing objectives (an example would be to try to minimize 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 that fact that the program is capable of performing true multiobjective optimization by establishing a Pareto Frontier.