A metaheuristic is generally a procedure designed to find a good solution to a difficult optimization problem. Known optimization search metaheuristics heavily rely on parameters, which are usually introduced so that the metaheuristic follows some supposedly related to the optimization problem natural process (simulated annealing, swarm optimization, genetic algorithms). Adjusting the parameter...

We describe a method for quantifying the importance of a blackbox function’s input parameters and their interactions, based on function evaluations obtained by running a Bayesian optimization procedure. We focus on high-dimensional functions with mixed discrete/continuous as well as conditional inputs, and therefore employ random forest models. We derive the first exact and efficient approach f...

Probably one of the most successful interfaces between operations research and computer science has been the development of discrete-event simulation software. The recent integration of optimization techniques into simulation practice, specifically into commercial software, has become nearly ubiquitous, as most discrete-event simulation packages now include some form of “optimization” routine. ...

In this paper we consider discrete-continuous scheduling problems defined in [7], where general results and methodology have been presented as well. These problems are characterized by the fact that each job simultaneously requires for its processing at a time discrete and continuous (i.e. continuouslydivisible) resources. We deal with a class of these problems where there are: one discrete res...

A probabilistic framework is proposed for the optimization of efficient switched control strategies for physical systems dominated by stochastic excitation. In this framework, the equation for the state trajectory is replaced with an equivalent equation for its probability distribution function in the constrained optimization setting. This allows for a large class of control rules to be conside...

The majority of the algorithms used to solve hard optimization problems today are population metaheuristics. These methods are often presented under a purely algorithmic angle, while insisting on the metaphors which led to their design. We propose in this article to regard population metaheuristics as methods making evolution a probabilistic sampling of the objective function, either explicitly...

Duality is an important notion for nonlinear programming (NLP). It provides a theoretical foundation for many optimization algorithms. Duality can be used to directly solve NLPs as well as to derive lower bounds of the solution quality which have wide use in other high-level search techniques such as branch and bound. However, the conventional duality theory has the fundamental limit that it le...

This paper improves constrained simulated annealing (CSA), a discrete global minimization algorithm with asymptotic convergence to discrete constrained global minima with probability one. The algorithm is based on the necessary and suucient conditions for discrete constrained local minima in the theory of discrete La-grange multipliers. We extend CSA to solve nonlinear continuous constrained op...