Special Session “Multiobjective Optimization with Surrogate Models” (Pre-announcement)

Pre-Announcement: 2016 IEEE WCCI/CEC Special Session 

The following proposal was provisionally accepted by the IEEE WCCI/CEC organizers:

Title: Multiobjective Optimization with Surrogate Models

Organizers

Aim of this Special Session

Many real-world optimization problems involve multiple, often conflicting objectives and rely on computationally expensive simulations to assess these objectives. Such multiobjective optimization problems can be solved more efficiently if the simulations are partly replaced by accurate surrogate models. Surrogate models, also known as response surface models or meta-models, are data driven models built to simulate the processes or devices that are subject to optimization. They are used when more precise models, such as those based on the finite element method or computational fluid dynamics, spend too much time and resources. While surrogate models allow for fast simulation and assessment of the optimization objectives, they also represent an additional source of impreciseness. In multiobjective optimization, this may constitute a particular challenge when comparing candidate solutions. The aim of this special session is to bring together researchers and practitioners working with surrogate-based multiobjective optimization algorithms to present recent achievements in the field and discuss directions for further work.

Scope and topics

Prospective authors are invited to submit their original and unpublished work on all aspects of surrogate-assisted multiobjective optimization. The scope of the special session covers, but is not limited to the following topics:

  • State-of-the-art in multiobjective optimization with surrogate models
  • Theoretical aspects of surrogate-assisted multiobjective optimization
  • Novel surrogate-based multiobjective optimization algorithms
  • Comparative studies in multiobjective optimization with surrogates
  • Benchmark problems and performance measures for multiobjective optimization with surrogates
  • Real-world applications of multiobjective optimization using surrogates

Short biographies of the organizers

Bogdan Filipič

Bogdan Filipič

Bogdan Filipic received his Ph.D. degree in Computer Science from the University of Ljubljana, Slovenia, in 1993. He is now a senior researcher and head of Computational Intelligence Group at the Department of Intelligent Systems of the Jozef Stefan Institute, Ljubljana. He is also an associate professor of Computer Science at the Jozef Stefan International Postgraduate School and at the University of Ljubljana. His research interests include stochastic optimization, evolutionary computation and intelligent data analysis. Recently he has been focusing on parallelization, use of surrogate models and visualization of results in evolutionary multiobjective optimization. He is also active in promoting evolutionary computation in practice and has led optimization projects for steel plants, car manufacturing and energy distribution. He co-chaired a series of BIOMA conferences and served as the general chair of PPSN 2014. He was a guest lecturer at the University of Oulu, Finland, and VU University Amsterdam, The Netherlands, and was giving tutorials on industrial applications of evolutionary algorithms at WCCI and CEC.

Thomas Bartz-Beielstein

Thomas Bartz-Beielstein

Thomas Bartz-Beielstein received his Ph.D. degree in Computer Science from TU Dortmund. Since 2006, when he became a professor for TH Köln, Dr. Bartz-Beielstein has built a research team of international status and visibility. He is head of the research center CIplus, and initiator of the SPOTSeven team. His expertise lies in optimization, simulation, and statistical analysis of complex real-world problems. He is one of the leaders in the field of statistical analysis of optimization algorithms and the inventor and the driving force behind the sequential parameter optimization technology (SPOT). Prof. Bartz-Beielstein has more than 100 publications on computational intelligence,  optimization, simulation, and experimental research. His books on experimental research are considered as milestones in this emerging field. He chairing the prestigious track “Evolutionary Computation in Practice” at GECCO and presented several tutorials on experimental research at GECCO, PPSN and WCCI.

Carlos Artemio Coello Coello

Carlos Artemio Coello Coello

Carlos A. Coello received a PhD in Computer Science from Tulane University in 1996. He is currently Professor with Distinction (Investigador Cinvestav 3F) at CINVESTAV-IPN, in Mexico City. His research interests include evolutionary multiobjective optimization and constraint-handling techniques for evolutionary algorithms. He is an IEEE Fellow and was General Chair of the 2013 IEEE Congress on Evolutionary Computation. He currently serves as associate editor of the IEEE Transactions on Evolutionary Computation. He has also received several awards, including the 2012 National Medal of Science and Arts in the area of Physical, Mathematical and Natural Sciences (this is the highest award that a scientist can receive in Mexico) and the prestigious 2013 IEEE Kiyo Tomiyasu Award “for pioneering contributions to single- and multiobjective optimization techniques using bioinspired metaheuristics”. To date, he has co-authored over 450 publications, which currently report over 29,000 citations (his h-index is 67).