This paper presents a parallel surrogate-based global optimization method for computationally expensive objective functions that is more effective for larger numbers of processors. To reach this goal, we integrated concepts from multi-objective optimization and tabu search into, single objective, surrogate optimization. Our proposed derivative-free algorithm, called SOP, uses non-dominated sort...

We demonstrate the applicability of a new PAINT method to speed up iterations of interactive methods in multiobjective optimization. As our test case, we solve a computationally expensive non-linear, five-objective problem of designing and operating a wastewater treatment plant. The PAINT method interpolates between a given set of Pareto optimal outcomes and constructs a computationally inexpen...

Conventional imaging only records photons directly sent from the object to detector, while non-line-of-sight (NLOS) takes indirect light into account. Most NLOS solutions employ a transient scanning process, followed by physical based algorithm reconstruct scenes. However, detection requires sophisticated apparatus, with long time and low robustness ambient environment, reconstruction algorithm...

This article explores convolutional generative networks as an alternative to iterative reconstruction algorithms in medical image reconstruction. The task of involves mapping projection domain data collected from the detector domain. is done typically through which are time consuming and computationally expensive. Trained deep learning provide faster outputs proven various tasks across computer...

The seismic analysis of reinforced concrete (RC) structures generally requires significant computational effort, which can be challenging or at least time-consuming also for the modern computing systems. Particularly, huge effort is required running optimisation procedures intended selecting “best” retrofitting solution among wide set technical feasible ones. Therefore, this paper proposes use ...

The strength of concrete is a function of the proportions of the constituent materials, namely, cement, water, fine and coarse aggregates. The conventional methods used to determine the mix proportions that will yield the desired strength, are laborious, time consuming and expensive. In this paper, a mathematical method based on modified regression theory is formulated for the prediction of con...

––The performance of pavements depends to a large extent on the strength and stiffness of the subgrades. Subgrade strength (CBR) plays a major role in pavement design. Since determination of CBR value in field requires need of equipment and also time consuming alternatively one can be predict CBR value of subgrade in field from other soil support tests namely Dynamic Cone Penetrometer Index (DC...

Integrating data-driven surrogate models and simulation models of di erent accuracies (or delities) in a single algorithm to address computationally expensive global optimization problems has recently attracted considerable attention. However, handling discrepancies between simulation models with multiple delities in global optimization is a major challenge. To address it, the two major contrib...

Purpose – The purpose of this paper is to propose a surrogate model-assisted optimization algorithm which effectively searches for the optimum at the earliest opportunity, avoiding the need for a large initial experimental design, which may be wasteful. Design/methodology/approach – The methodologies of two-stage and one-stage selection of points are combined for the first time. After creating ...