This paper presents a surrogate-assisted framework to solve constrained black-box optimization problems, which is applied the optimal single-mixed refrigerant natural gas liquefaction processes design. approach uses kriging models as cheap-to-evaluate, reliable surrogates of process-simulator-dependent functions problem. From prediction and estimated error, probability feasible improvement acqu...

The purpose of this paper is to construct a conical branch-and-bound algorithm for solving linear programming problems with an additional reverse convex constraint. We propose an inexpensive bound-tightening procedure, which is based on the surrogate relaxation. We show that this procedure considerably tightens lower bounds yielded by the usual linear programming relaxation. We also report nume...

This paper presents an efficient methodology for accurate reliability assessment of electromagnetic devices. To achieve the goal, elaborate surrogated models to approximate constraint functions of interest are generated based on the dynamic Kriging method and a hypercube local window. Then, the Monte Carlo simulation scheme is applied to the surrogate models. This leads to reducing computationa...

We consider stochastic discrete optimization problems where the decision variables are non-negative integers and propose a generalized “surrogate problem” methodology that modi...es and extends previous work in [1]. Our approach is based on an on-line control scheme which transforms the problem into a “surrogate” continuous optimization problem and proceeds to solve the latter using standard gr...

Nonlinear Knapsack Problems (NKP) are the alternative formulation for the multiple-choice knapsack problems. A powerful approach for solving NKP is dynamic programming which may obtain the global op-timal solution even in the case of discrete solution space for these problems. Despite the power of this solu-tion approach, it computationally performs very slowly when the solution space of the pr...

This paper considers the problem of reconstructing sparse signals from one-bit quantized measurements. We employ a log-sum penalty function, also referred to as the Gaussian entropy, to encourage sparsity in the algorithm development. In addition, in the proposed method, the logistic function is introduced to quantify the consistency between the measured one-bit quantized data and the reconstru...

This paper deals with the development of effective techniques to automatically obtain the optimum management of petroleum fields aiming to increase the oil production during a given concession period of exploration. The optimization formulations of such a problem turn out to be highly multimodal, and may involve constraints. In this paper, we develop a robust Particle Swarm algorithm coupled wi...

We describe a computationally efficient simulation-driven design methodology for single-element airfoils at high-lift conditions. Direct optimization of an accurate but computationally expensive high-fidelity simulation model based on Reynolds-Averaged Navier-Stokes equations is replaced by iterative updating and re-optimization of a cheap surrogate. The surrogate model exploits a low-fidelity ...