Knowledge Discovery in Multi-Objective Optimization

Special Session @ IEEE World Congress on Computational Intelligence WCCI 2020

Glasgow, Scotland, UK, July 19 - 24, 2020

Aim and Scope

Practical optimization problems often involve multiple objectives to be optimized simultaneously under multiple constraints and with respect to several variables. In recent years, several multi- and many-objective optimization algorithms have been proposed which are capable of generating a diverse set of Pareto-optimal solutions. While multi-objective optimization itself can be a challenging task, equally difficult is the ability to make sense of the obtained solutions. In this special session, we invite papers that deal with extracting knowledge from solutions generated during or after the multi-objective optimization process. When obtained after the optimization run, this knowledge is expected to provide deeper insights about the problem to the decision maker, thus assisting both decision making and future optimization runs. On the other hand, knowledge obtained during the optimization run, can be used to enhance convergence or used indirectly for interactive preference-based optimization. This special session attempts to showcase methods and applications related to knowledge discovery in a broader sense derived out of an optimization process.

Topics include, but are not limited to:

  • Statistical analysis of Pareto-optimal solutions
  • Visualization of Pareto-optimal solutions in decision space and/or objective space
  • Data mining of Pareto-optimal solutions for knowledge discovery
  • Multi-objective design space exploration
  • Dimensionality reduction of Pareto-optimal solutions in decision and/or objective space
  • Manifold learning in the objective or decision space
  • “Innovization” and automated “innovization”
  • Rule extraction from Pareto-optimal solutions
  • Machine learning or data mining based multi-objective optimization
  • Data-driven multi-objective optimization including surrogate modelling
  • Knowledge-driven multi-objective optimization
  • Expert systems for multi-objective optimization
  • Knowledge management in the context of multi-objective optimization
  • Integration of implicit or explicit knowledge in the optimization process
  • Interactive multi-objective optimization that involves expert knowledge
  • Real world applications of multi-objective optimization involving any of the above

Organizers

Dr. Sunith Bandaru

(https://scholar.google.com/citations?user=LH5v498AAAAJ)

Department of Production and Automation Engineering, University of Skövde, Sweden

Email: sunith.bandaru@his.se

Website: www.his.se/bans

Biography: Sunith Bandaru is an Associate Professor in the Department of Production and Automation Engineering at University of Skövde, Sweden. He obtained his PhD in 2013 from Indian Institute of Technology Kanpur within the areas of evolutionary computing and machine learning as applied to engineering problems. His main research interests are multi-objective optimization, simulation-based optimization, evolutionary algorithms, data mining, machine learning and knowledge discovery. His application domain spans mechanical, production and automation engineering.


Dr. Kalyanmoy Deb

(https://scholar.google.com/citations?user=paTAXiIAAAAJ)

Department of Electrical and Computer Engineering, Michigan State University, USA

Email: kdeb@msu.edu

Website: https://www.egr.msu.edu/~kdeb/

Biography: Kalyanmoy Deb is Koenig Endowed Chair Professor at Department of Electrical and Computer Engineering in Michigan State University. Prof. Deb's research interests are in evolutionary optimization and their application in multi-criterion optimization, modeling, and machine learning. He has published over 510 research papers with Google Scholar citation of over 130,000 with h-index 113. He is in the editorial board on 18 major international journals.


Dr. Amos H.C. Ng

(https://scholar.google.com/citations?user=JRfAdDUAAAAJ)

Department of Production and Automation Engineering, University of Skövde, Sweden

Email: amos.ng@his.se

Website: www.his.se/ngam

Biography: Amos Ng is a Professor in the Department of Production and Automation Engineering at University of Skövde, Sweden. He is also a Visiting Professor at Uppsala University since 2018. He obtained his PhD in 2003 from De Montfort University, UK. He has over 15 years of experience in running industrial research projects with Swedish manufacturing companies and participated in various EU projects. His main research interests include simulation-based optimization, production systems design and analysis, multi-objective optimization and knowledge-driven optimization.


Important Dates

15 Jan 2020 Paper Submission Deadline

15 Mar 2020 Paper Acceptance Notification Date

15 April 2020 Final Paper Submission and Early Registration Deadline

19-24 July 2020 IEEE WCCI 2020, Glasgow, Scotland, UK

Paper Submission

All papers for IEEE WCCI 2020 should be submitted electronically through the congress website (https://wcci2020.org/). Please follow updates on the website for submission guidelines and submission instructions.

Special session papers are treated the same as regular conference papers. During submission, please select 'Special Session on Knowledge Discovery in Multi-Objective Optimization' from the drop-down menu. Submitted papers will refereed by members of the program committee formed by the organizers. The papers will be reviewed based on the criteria of originality, significance, technical quality, relevance, and presentation clarity.


Can an author post his/her manuscript on a preprint server such as ArXiv?

Yes. As stated in https://www.ieee.org/documents/author_faq.pdf, the IEEE recognizes that many authors share their unpublished manuscripts on public sites. Once manuscripts have been accepted for publication by IEEE, an author is required to post an IEEE copyright notice on the pre-print. Upon publication, the author must replace the pre-print with either, (i) the full citation to the IEEE work with Digital Object Identifiers (DOI) or a link to the paper’s abstract in IEEE Xplore, or (ii) the accepted version only (not the IEEE published version), including the IEEE copyright notice and full citation, with a link to the final, published paper in IEEE Xplore.


What is the IEEE policy for papers not presented at the conference?

IEEE reserves the right to exclude a paper from distribution after the conference, including IEEE Xplore® Digital Library, if the paper is not presented by the author at the conference. Additional details can be found at: https://www.ieee.org/conferences_events/conferences/organizers/handling_nonpresented_papers.html

About IEEE WCCI

The IEEE World Congress on Computational Intelligence (IEEE WCCI) is the world’s largest technical event in the field of computational intelligence. WCCI 2020 features the flagship conference of the Computational Intelligence Society: The 2020 International Joint Conference on Neural Networks (IJCNN 2020), the 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2020), and the 2020 IEEE Congress on Evolutionary Computation (IEEE CEC 2020) under one roof. It encourages cross-fertilization of ideas among the three big areas and provides a forum for intellectuals from all over the world to discuss and present their research findings on computational intelligence.

For more information about IEEE WCCI 2020, please visit https://wcci2020.org/ .

IEEE WCCI 2020 will be held in Glasgow, Scotland, UK -one of Europe’s most dynamic cultural capitals and the “World’s Friendliest City”. Steeped in culture, rich in history and alive with an excitement you can sense as you walk through its elegant Victorian streets, squares, parks and gardens. The Conference will be held at the prestigious Scottish Event Campus (SEC), which was a key venue for the Glasgow Commonwealth Games 2014.