A new heuristic for solving unconstrained continuous optimization problems is suggested. The heuristic is based on a generalized version of the Variable Neighborhood Search metaheuristic, where different neighborhoods and distributions are ranked and used to get random point in the shaking step. The suggested heuristic is extended to constrained optimization problems. Constraints are handled using exterior point penalty functions within an algorithm that combines sequential and exact penalty transformations. Numerical experiments on a set of standard test global optimization problems show encouraging results.
Group for Research in Decision Analysis