For expensive black-box optimization problems, surrogatemodel based approaches like Efficient Global Optimization are frequently used in continuous optimization. Their main advantage is the reduction of function evaluations by exploiting cheaper, data-driven models of the actual target function. The utilization of such methods in combinatorial or mixed search spaces is less common. Efficient Gl...

This paper treats the solution of nonlinear optimization problems involving discrete decision variables, also known as generalized disjunctive programming (GDP) or mixed-integer nonlinear programming (MINLP) problems, that arise in process engineering. The key idea is to eliminate the discrete decision variables by adding a set of continuous variables and constraints that represent the discrete...

Optimum design of structures is achieved while the design variables are continuous and discrete. To reduce the computational work involved in the optimization process, all the functions that are expensive to evaluate, are approximated. To approximate these functions, a semi quadratic function is employed. Only the diagonal terms of the Hessian matrix are used and these elements are estimated fr...

In this paper, we investigate the performance of introducing competition in cooperative coevolutionary algorithms to solve large-scale fully-separable continuous optimization problems. It may seem that solving large-scale fully-separable functions is trivial by means of problem decomposition. In principle, due to lack of variable interaction in fullyseparable problems, any decomposition is viab...

While Bayesian optimization (BO) has achieved great success in optimizing expensive-to-evaluate black-box functions, especially tuning hyperparameters of neural networks, methods such as random search (Li et al., 2016) and multifidelity BO (e.g. Klein et al. (2017)) that exploit cheap approximations, e.g. training on a smaller training data or with fewer iterations, can outperform standard BO a...

In large federated and shared-nothing databases, resources can exhibit widely uctuating characteristics. Assumptions made at the time a query is submitted will rarely hold throughout the duration of query processing. As a result, traditional static query optimization and execution techniques are ine ective in these environments. In this paper we introduce a query processing mechanism called an ...

2 (First order) Descent methods, rates 2 2.1 Gradient descent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.2 What can we achieve? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.3 Second order methods: Newton’s method . . . . . . . . . . . . . . . . . 7 2.4 Multistep first order methods . . . . . . . . . . . . . . . . . . . . . . . . 8 2.4.1 Heavy ball method . ...

two main goals are being pursued in this paper. first, a brief summary of the structural optimization methods is presented. although most of these methods are general and are used in analysis and design of skeletal as well as continuous structures, discussion is more often directed towards the shape optimization .the second second goal of this paper is to present a new method for sensitivity an...

In this chapter we introduce a new ant colony algorithm aimed at continuous and dynamic problems. To deal with the changes in the dynamic problems, the diversification in the ant population is maintained by attributing to every ant a repulsive electrostatic charge, that allows to keep ants at some distance from each other. The algorithm is based on a continuous ant colony algorithm that uses a ...