Global Optimization with Non-Convex Constraints

Presents a new approach to global non-convex constrained optimization. Problem dimensionality is reduced via space-filling curves and to economize the search, constraint is accounted separately (penalties are not employed). The multicriteria case is also considered. All techniques are generalized for (non-redundant) execution on multiprocessor systems.