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A surrogate-based optimization algorithm for network design problems Article

Meng LI, Xi LIN, Xi-qun CHEN

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 11,   Pages 1693-1704 doi: 10.1631/FITEE.1601403

Abstract: We adopt a surrogate-based optimization (SBO) framework to solve three featured categories of NDPs (continuousThe SBO approach presented in this paper is a general algorithm of solving other optimization problems

Keywords： Network design problem     Surrogate-based optimization     Transportation planning     Heuristics    

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Reliability-based design optimization of offshore wind turbine support structures using RBF surrogate

Frontiers of Structural and Civil Engineering   Pages 1086-1099 doi: 10.1007/s11709-023-0976-8

Abstract: Reliability-based design optimization of offshore wind turbine support structures using RBF surrogate

Keywords： RBF     surrogate model     turbine support structures    

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Synergistic optimization framework for the process synthesis and design of biorefineries

Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 2,   Pages 251-273 doi: 10.1007/s11705-021-2071-9

Abstract: We propose a novel framework incorporating superstructure optimization and simulation-based optimizationIn this context, several approaches for superstructure optimization based on different surrogate modelsThe results indicate that even though surrogate-based optimization approaches alleviate the underlyingto determine candidate process topologies and simulation-based optimization to consolidate the processThese findings invite for a critical assessment of surrogate-based optimization approaches and point

Keywords： biotechnology     surrogate modelling     superstructure optimization     simulation-based optimization     process    

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Method for solving the nonlinear inverse problem in gas face seal diagnosis based on surrogate models

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 3, doi: 10.1007/s11465-022-0689-z

Abstract: The presented work develops a surrogate-model-assisted method for solving the nonlinear inverse problemLatin hypercube design and then performs an iterative routine that benefits from the rapidity of the surrogate

Keywords： surrogate model     gas face seal     fault diagnosis     nonlinear dynamics     tribology    

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double-layer barrel vaults using genetic and pattern search algorithms and optimized neural network as surrogate

Frontiers of Structural and Civil Engineering 2023, Volume 17, Issue 3,   Pages 378-395 doi: 10.1007/s11709-022-0899-9

Abstract: This paper presents a combined method based on optimized neural networks and optimization algorithmsto solve structural optimization problems.The main idea is to utilize an optimized artificial neural network (OANN) as a surrogate model to reduceSubsequently, the main optimization problem is solved using the OANN and a population-based algorithmThe algorithms considered in this step are the arithmetic optimization algorithm (AOA) and genetic algorithm

Keywords： optimization     surrogate models     artificial neural network     SAP2000     genetic algorithm    

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approach for structural damage detection using meta-heuristic algorithms and group method of data handling surrogate

Hamed FATHNEJAT, Behrouz AHMADI-NEDUSHAN

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 4,   Pages 907-929 doi: 10.1007/s11709-020-0628-1

Abstract: performance of an efficient two-stage methodology which is applied in a damage detection system using a surrogatethe first stage, in order to locate the damage accurately, the performance of the modal strain energy basedof the three popular optimization algorithms including particle swarm optimization (PSO), bat algorithm( ) based on the modal property change vector has been proposed as an objective function.Finally, in order to achieve the most generalized neural network as a surrogate model, GMDH performance

Keywords： two-stage method     modal strain energy     surrogate model     GMDH     optimization damage detection    

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An assessment of surrogate fuel using Bayesian multiple kernel learning model in sight of sooting tendency

Frontiers in Energy 2022, Volume 16, Issue 2,   Pages 277-291 doi: 10.1007/s11708-021-0731-6

Abstract: Based on the database, a novel predicting model of YSI values for surrogate fuels was proposed with theThe BMKL model provides an accurate and low-cost approach to assess surrogate performances of diesel,Particularly, this model is one of the first attempts to predict the sooting tendencies of surrogateDuring surrogate formulation, the BMKL model can be used to shrink the surrogate candidate list in termsof sooting tendency and ensure the optimal surrogate has a satisfying matching level of soot behaviors

Keywords： sooting tendency     yield sooting index     Bayesian multiple kernel learning     surrogate assessment     surrogate    

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Reliability-based robust design optimization of vehicle components, Part I: Theory

Yimin ZHANG

Frontiers of Mechanical Engineering 2015, Volume 10, Issue 2,   Pages 138-144 doi: 10.1007/s11465-015-0333-2

Abstract:

The reliability-based design optimization, the reliability sensitivity analysis and robust designoptimization of vehicle components.A procedure for reliability-based robust design optimization of vehicle components is proposed.Application of the method is illustrated by reliability-based robust design optimization of axle anddesign optimization of vehicle components.

Keywords： vehicle components     reliability-based design optimization     reliability-based sensitivity analysis     multi-objectiveoptimization     robust design    

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Reliability-based robust design optimization of vehicle components, Part II: Case studies

Yimin ZHANG

Frontiers of Mechanical Engineering 2015, Volume 10, Issue 2,   Pages 145-153 doi: 10.1007/s11465-015-0334-1

Abstract:

The reliability-based optimization, the reliability-based sensitivity analysis and robust design methodare employed to propose an effective approach for reliability-based robust design optimization of vehicleApplications of the method are further discussed for reliability-based robust optimization of vehicleResults have shown the proposed method is an efficient method for reliability-based robust design optimization

Keywords： vehicle axles and springs     reliability-based design optimization     reliability-based sensitivity analysis     multi-objective optimization     robust design    

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Optimization of multi-objective integrated process planning and scheduling problem using a priority basedoptimization algorithm

Muhammad Farhan AUSAF,Liang GAO,Xinyu LI

Frontiers of Mechanical Engineering 2015, Volume 10, Issue 4,   Pages 392-404 doi: 10.1007/s11465-015-0353-y

Abstract: and scheduling, real world problems cannot be fully captured considering only a single objective for optimizationIn this paper, an optimization algorithm for solving MOIPPS problem is presented.To compare the results with other algorithms, a C-matric based method has been used.

Keywords： integrated process planning and scheduling (IPPS)     dispatching rules     priority based optimization algorithm     multi-objective optimization    

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Massively efficient filter for topology optimization based on the splitting of tensor product structure

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 4, doi: 10.1007/s11465-022-0710-6

Abstract: In this work, we put forward a massively efficient filter for topology optimization (TO) utilizing thewhich the sensitivity analysis is reformulated for the nodal design variables without altering the optimization

Keywords： topology optimization     isogeometric analysis     finite element analysis     tensor product structure     sensitivity    

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QPSO-ILF-ANN-based optimization of TBM control parameters considering tunneling energy efficiency

Frontiers of Structural and Civil Engineering 2023, Volume 17, Issue 1,   Pages 25-36 doi: 10.1007/s11709-022-0908-z

Abstract: However, the TBM control parameters set based on operator experience may not necessarily be suitableHence, a method to optimize TBM control parameters using an improved loss function-based artificial neuralnetwork (ILF-ANN) combined with quantum particle swarm optimization (QPSO) is proposed herein.Inspired by the regularization technique, a custom artificial neural network (ANN) loss function based

Keywords： tunnel boring machine     control parameter optimization     quantum particle swarm optimization     artificial    

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Hybrid Data-Driven and Mechanistic Modeling Approaches for Multiscale Material and Process Design Perspective

Teng Zhou, Rafiqul Gani, Kai Sundmacher

Engineering 2021, Volume 7, Issue 9,   Pages 1231-1238 doi: 10.1016/j.eng.2020.12.022

Abstract:

The world’s increasing population requires the process industry to produce food, fuels, chemicals, and consumer products in a more efficient and sustainable way. Functional process materials lie at the heart of this challenge. Traditionally, new advanced materials are found empirically or through trial-and-error approaches. As theoretical methods and associated tools are being continuously improved and computer power has reached a high level, it is now efficient and popular to use computational methods to guide material selection and design. Due to the strong interaction between material selection and the operation of the process in which the material is used, it is essential to perform material and process design simultaneously. Despite this significant connection, the solution of the integrated material and process design problem is not easy because multiple models at different scales are usually required. Hybrid modeling provides a promising option to tackle such complex design problems. In hybrid modeling, the material properties, which are computationally expensive to obtain, are described by data-driven models, while the well-known process-related principles are represented by mechanistic models. This article highlights the significance of hybrid modeling in multiscale material and process design. The generic design methodology is first introduced. Six important application areas are then selected: four from the chemical engineering field and two from the energy systems engineering domain. For each selected area, state-ofthe- art work using hybrid modeling for multiscale material and process design is discussed. Concluding remarks are provided at the end, and current limitations and future opportunities are pointed out.

Keywords： Data-driven     Surrogate model     Machine learning     Hybrid modeling     Material design     Process optimization    

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Analysis of energy saving optimization of campus buildings based on energy simulation

Dingding TONG, Jing ZHAO

Frontiers in Energy 2013, Volume 7, Issue 3,   Pages 388-398 doi: 10.1007/s11708-013-0273-7

Abstract: The traditional energy system design and operation for campus buildings is only based on the constantThe results proved that the strategy of set-point temperature optimization could efficiently reduce the

Keywords： campus buildings     set-point temperature     energy simulation     energy saving optimization    

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Ant colony optimization for assembly sequence planning based on parameters optimization

Zunpu HAN, Yong WANG, De TIAN

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 2,   Pages 393-409 doi: 10.1007/s11465-020-0613-3

Abstract: Second, an algorithm called SOS-ACO that combines symbiotic organisms search (SOS) and ant colony optimization

Keywords： assembly sequence planning     ant colony optimization     symbiotic organisms search     parameter optimization    

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Title Author Date Type Operation

A surrogate-based optimization algorithm for network design problems

Meng LI, Xi LIN, Xi-qun CHEN

Journal Article

Reliability-based design optimization of offshore wind turbine support structures using RBF surrogate

Journal Article

Synergistic optimization framework for the process synthesis and design of biorefineries

Journal Article

Method for solving the nonlinear inverse problem in gas face seal diagnosis based on surrogate models

Journal Article

double-layer barrel vaults using genetic and pattern search algorithms and optimized neural network as surrogate

Journal Article

approach for structural damage detection using meta-heuristic algorithms and group method of data handling surrogate

Hamed FATHNEJAT, Behrouz AHMADI-NEDUSHAN

Journal Article

An assessment of surrogate fuel using Bayesian multiple kernel learning model in sight of sooting tendency

Journal Article

Reliability-based robust design optimization of vehicle components, Part I: Theory

Yimin ZHANG

Journal Article

Reliability-based robust design optimization of vehicle components, Part II: Case studies

Yimin ZHANG

Journal Article

Optimization of multi-objective integrated process planning and scheduling problem using a priority basedoptimization algorithm

Muhammad Farhan AUSAF,Liang GAO,Xinyu LI

Journal Article

Massively efficient filter for topology optimization based on the splitting of tensor product structure

Journal Article

QPSO-ILF-ANN-based optimization of TBM control parameters considering tunneling energy efficiency

Journal Article

Hybrid Data-Driven and Mechanistic Modeling Approaches for Multiscale Material and Process Design

Teng Zhou, Rafiqul Gani, Kai Sundmacher

Journal Article

Analysis of energy saving optimization of campus buildings based on energy simulation

Dingding TONG, Jing ZHAO

Journal Article

Ant colony optimization for assembly sequence planning based on parameters optimization

Zunpu HAN, Yong WANG, De TIAN

Journal Article