University of Jyväskylä | JYX Digital Repository

  • English  | Give feedback |
    • suomi
    • English
 
  • Login
JavaScript is disabled for your browser. Some features of this site may not work without it.
View Item 
  • JYX
  • Artikkelit
  • Informaatioteknologian tiedekunta
  • View Item
JYX > Artikkelit > Informaatioteknologian tiedekunta > View Item

A Data-Driven Surrogate-Assisted Evolutionary Algorithm Applied to a Many-Objective Blast Furnace Optimization Problem

ThumbnailFinal Draft
View/Open
645.5 Kb

Downloads:  
Show download detailsHide download details  
Chugh, T., Chakraborti, N., Sindhya, K., & Jin, Y. (2017). A Data-Driven Surrogate-Assisted Evolutionary Algorithm Applied to a Many-Objective Blast Furnace Optimization Problem. Materials and Manufacturing Processes, 32(10), 1172-1178. https://doi.org/10.1080/10426914.2016.1269923
Published in
Materials and Manufacturing Processes
Authors
Chugh, Tinkle |
Chakraborti, Nirupam |
Sindhya, Karthik |
Jin, Yaochu
Date
2017
Discipline
TietotekniikkaMathematical Information Technology
Copyright
© 2017 Taylor & Francis. This is a final draft version of an article whose final and definitive form has been published by Taylor & Francis. Published in this repository with the kind permission of the publisher.

 
A new data-driven reference vector-guided evolutionary algorithm has been successfully implemented to construct surrogate models for various objectives pertinent to an industrial blast furnace. A total of eight objectives have been modeled using the operational data of the furnace using 12 process variables identified through a principal component analysis and optimized simultaneously. The capability of this algorithm to handle a large number of objectives, which has been lacking earlier, results in a more efficient setting of the operational parameters of the furnace, leading to a precisely optimized hot metal production process.
Publisher
Taylor & Francis Inc.
ISSN Search the Publication Forum
1042-6914
Keywords
blast furnace ironmaking metamodeling multi-objective optimization model management data-driven optimization Pareto optimality optimointi pareto-tehokkuus rautateollisuus
DOI
https://doi.org/10.1080/10426914.2016.1269923
URI

http://urn.fi/URN:NBN:fi:jyu-201707183318

Publication in research information system

https://converis.jyu.fi/converis/portal/detail/Publication/26405251

Metadata
Show full item record
Collections
  • Informaatioteknologian tiedekunta [1967]

Related items

Showing items with similar title or keywords.

  • Multiobjective shape design in a ventilation system with a preference-driven surrogate-assisted evolutionary algorithm 

    Chugh, Tinkle; Kratky, Tomas; Miettinen, Kaisa; Jin, Yaochu; Makkonen, Pekka (ACM, 2019)
    We formulate and solve a real-world shape design optimization problem of an air intake ventilation system in a tractor cabin by using a preference-based surrogate-assisted evolutionary multiobjective optimization algorithm. ...
  • A Surrogate-assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-objective Optimization 

    Chugh, Tinkle; Jin, Yaochu; Miettinen, Kaisa; Hakanen, Jussi; Sindhya, Karthik (Institute of Electrical and Electronics Engineers, 2018)
    We propose a surrogate-assisted reference vector guided evolutionary algorithm (EA) for computationally expensive optimization problems with more than three objectives. The proposed algorithm is based on a recently developed ...
  • Surrogate-assisted evolutionary biobjective optimization for objectives with non-uniform latencies 

    Chugh, Tinkle; Allmendinger, Richard; Ojalehto, Vesa; Miettinen, Kaisa (Association for Computing Machinery (ACM), 2018)
    We consider multiobjective optimization problems where objective functions have different (or heterogeneous) evaluation times or latencies. This is of great relevance for (computationally) expensive multiobjective optimization ...
  • Probabilistic Selection Approaches in Decomposition-based Evolutionary Algorithms for Offline Data-Driven Multiobjective Optimization 

    Mazumdar, Atanu; Chugh, Tinkle; Hakanen, Jussi; Miettinen, Kaisa (IEEE, 2022)
    In offline data-driven multiobjective optimization, no new data is available during the optimization process. Approximation models, also known as surrogates, are built using the provided offline data. A multiobjective ...
  • Treed Gaussian Process Regression for Solving Offline Data-Driven Continuous Multiobjective Optimization Problems 

    Mazumdar, Atanu; López-Ibáñez, Manuel; Chugh, Tinkle; Hakanen, Jussi; Miettinen, Kaisa (MIT Press, 2023)
    For offline data-driven multiobjective optimization problems (MOPs), no new data is available during the optimization process. Approximation models (or surrogates) are first built using the provided offline data and an ...
  • Browse materials
  • Browse materials
  • Articles
  • Conferences and seminars
  • Electronic books
  • Historical maps
  • Journals
  • Tunes and musical notes
  • Photographs
  • Presentations and posters
  • Publication series
  • Research reports
  • Research data
  • Study materials
  • Theses

Browse

All of JYXCollection listBy Issue DateAuthorsSubjectsPublished inDepartmentDiscipline

My Account

Login

Statistics

View Usage Statistics
  • How to publish in JYX?
  • Self-archiving
  • Publish Your Thesis Online
  • Publishing Your Dissertation
  • Publication services

Open Science at the JYU
 
Data Protection Description

Accessibility Statement

Unless otherwise specified, publicly available JYX metadata (excluding abstracts) may be freely reused under the CC0 waiver.
Open Science Centre