Titlebook: Engineering and Applied Sciences Optimization; Dedicated to the Mem Nikos D. Lagaros,Manolis Papadrakakis Book 2015 The Editor(s) (if appli

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https://doi.org/10.1007/978-3-319-95861-3thod for derivative-free optimization. We present different formulations for the surrogate problem considered at each search step of the Mesh Adaptive Direct Search (MADS) algorithm using a surrogate management framework. The proposed formulations are tested on two simulation-based multidisciplinary

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Simulation Optimization of Car-Following Models Using Flexible Techniques,improved methodological framework is suggested for the optimization of car-following models. Machine learning techniques, such as classification, locally weighted regression (loess) and clustering, are innovatively integrated. In this chapter, validation of the proposed methods is demonstrated on da

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Design of Tuned Mass Dampers via Harmony Search for Different Optimization Objectives of Structurese on both displacements and accelerations. But for acceleration objective, a small benefit for accelerations can be seen although the optimum mass of TMD is very heavy according to displacement objective.

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Hierarchical Topology Optimization for Bone Tissue Scaffold: Preliminary Results on the Design of alogy of each pore of the scaffold. In the first stage, an optimal material distribution is obtained to generate a stiffness match between implant and bone tissue. In the second stage, the optimal relative density distribution is used to interpolate target material properties at each location of the

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Blackbox Optimization in Engineering Design: Adaptive Statistical Surrogates and Direct Search Algothod for derivative-free optimization. We present different formulations for the surrogate problem considered at each search step of the Mesh Adaptive Direct Search (MADS) algorithm using a surrogate management framework. The proposed formulations are tested on two simulation-based multidisciplinary