1.2 OTHER MODELS FOR OPTIMIZATION
The rest of the book focuses mainly on solving static and deterministic problems.
Demand is growing to solve real-world optimization problems where the data are noisy
or the objective function is changing dynamically. Finding robust solutions for some
design problems is another important challenge in optimization. A transformation to
deterministic and static problems is often proposed to solve such problems. Moreover,
some adaptations may be proposed for metaheuristics in terms of intensification and
diversification of the search to tackle this class of problems [414]. Chapter 4 deals
with another class of optimization problems characterized by multiple objectives: the
multiobjective optimization problems (MOP) class.