* Please kindly forward to those who may be interested.
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IEEE Congress on Evolutionary Computation 2017, Donostia - San Sebastián, Spain, June 5-8, 2017
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Special Session on Model Reduction in Multi-Objective and Robust Design Optimization
https://sites.google.com/site/adloptimization/home
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Submission deadline: January 16, 2017.
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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 model reduction, particularly the aerospace engineering design Preliminary space mission design under uncertainty Multi-objective optimization in preliminary space mission design
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
Organizers
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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