In this paper, a two-scale approach for elastic shape optimization of fine-scale structures in additive manufacturing is investigated. To end, free material performed on the macro-scale using elasticity tensors set microscopically realizable tensors. A database these and their cost values obtained with topology microscopic cells, working within fixed samples. This takes into account manufactura...

We propose a solution method for online vehicle routing, which integrates machine learning routine to improve tours’ quality. Our optimization model is based on the Bertsimas et al. (2019) re-optimization approach. Two separate routines are developed. The first one uses neural network produce realistic pick-up times customers serve. second relies Q-learning in addition random walks construction...

Abstract Multi-scale structures, as found in nature (e.g., bone and bamboo), hold the promise of achieving superior performance while being intrinsically lightweight, robust, multi-functional. Recent years have seen a rapid development topology optimization approaches for designing multi-scale but field actually dates back to seminal paper by Bendsøe Kikuchi from 1988 (Computer Methods Applied ...

Some optimization problems in scientific research, such as the robustness for Internet of Things and neural architecture search, are large-scale decision space expensive objective evaluation. In order to get a good solution limited budget optimization, random grouping strategy is adopted divide problem into some low-dimensional sub-problems. A surrogate model then trained each sub-problem using...

Many real-world optimization problems are large-scale in nature. In order to solve these problems, an optimization algorithm is required that is able to apply a global search regardless of the problems’ particularities. This paper proposes a self-adaptive differential evolution algorithm, called jDElscop, for solving large-scale optimization problems with continuous variables. The proposed algo...