Friday, December 16, 2016

[DMANET] Reminder for submission:Special Session on Model Reduction in Multi-Objective and Robust Design Optimization ,IEEE Congress on Evolutionary Computation 2017


Submission deadline: January 16, 2017.


IEEE Congress on Evolutionary Computation 2017, Donostia - San
Sebastián, Spain, June 5-8, 2017


Special Session on Model Reduction in Multi-Objective and Robust
Design Optimization


Dear Colleagues,

We would like to kindly remind that the submission deadline for the
Special Session on Model Reduction in Multi-Objective and Robust
Design Optimization ,IEEE Congress on Evolutionary Computation 2017
(CEC2017), January 16, 2017, is getting closer.

**Important date**

Deadline for contribution paper submission: January 16, 2017.
Notification of acceptance: February 26, 2017
Final paper submission: March 12, 2017
Conference dates:
June 5-8, 2017

IEEE CEC 2017 is a world-class conference that aims to bring together
researchers and practitioners in the field of evolutionary computation
and computational intelligence from all around the globe. The
special session aims to promote research on theoretical and practical
aspects of multi-objective optimization, surrogate assisted
optimization and robust design optimization, etc.

*Scope and Motivations*

With model reduction, e.g. Proper Orthogonal Decomposition (POD, also
called Principal Components Analysis, PCA) based model reduction,
expenses of optimization process can be greatly reduced. Take the
standard ZDT series test functions for example, it takes around 20
iterations for the MOO with POD model reduction to search the true
Pareto front. The model reduction can also be used in surrogate
assisted optimization and evidence approximation. With model
reduction, surrogate and evidence computation can be constructed on a
reduced data set, thus the sample size can be greatly reduced.

The optimization with model reduction shows advantages over
conventional MOOs and can be potentially extended to the scenarios
such as optimization problems with many objectives. However, to
implement successfully the methods in design optimization with
expensive model under uncertainty, a series of issues such as evidence
approximation, model fidelity management, optimization algorithm and
the strategy to integrate them, etc. should be resolved. We therefore
propose the special issue on Model Reduction in Multi-objective and
Robust Optimization.

**Scope and Topic:**

The session seeks to promote discussion and presentation of related
novel works. Topics may include, but are not limited to:

Multi-objective optimization
Many-objective optimization
Robust design optimization
Multi-Fidelity optimization
Uncertainty modeling
Parameter reduction
Data mining in Multi-objective and Many-objective Optimization
Model fidelity management
Surrogate of expensive model
Model reduction in Multi-objective and Many-objective Optimization
Infill strategy of surrogate
Surrogate assisted optimization
Evidence approximation of epistemic uncertainty
Multi-objective robust optimization under uncertainty
Applications of design optimization with prarameter reduction,
particularly the aerospace engineering design
Preliminary space mission design under uncertainty
Multi-objective optimization in preliminary space mission design


Dr. Liqiang Hou, State Key Laoratory of Astronautic Dynamics, Xi'an
Staellite Control Center, Xi'an, China
Dr. Tapabrata Ray, School of Engineering and Information Technology
University of New South Wales, Canberra, Australia
Dr. Edmondo Minisci, Department of Mechanical & Aerospace Engineering
 of University of Strathclyde, Glasgow, UK

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