Most of all modern engineering and scientific applications are concerned by big optimization problems in terms of number of variables (more than thousands), objectives, constraints, data, uncertainties and so on. The goal of this session is to come up with cutting-edge evolutionary and meta-heuristic approaches to deal with big optimization problems such as : parallel design and implementation, decomposition methods, model-based optimization, surrogate-based optimization, cross-domain, exa-scale and ultra-scale optimization, deep learning architectures, optimization under uncertainties, and mixed optimization.
Scope and Topics
The aim of this special session is to explore potential evolutionary algorithms and meta-heuristics to solve big optimization problems. For this purpose, this special session focuses on, but is not limited to, the following areas: