SPECIALSESSIONSENGINEERING APPLICATIONS OFEVOLUTIONARY COMPUTATION
Amir H Gandomi, Joong Hoon Kim

During the last three decades, evolutionary computation (EC) has been widely used forsolving complex real-world problems. These techniques are getting popular these days forengineering design and maintenance optimization as they are commonly deal with complex
problems without explicit formula. The main focus of this special session would be on the EC
techniques applications to engineering optimization, and maintenance.
Optimum design, maintenance and monitoring of engineering systems, such as buildings
and bridges, are challenging issues and involve several geometrical and mechanicalconstraints. In order to find a practical solution, most engineering design problems should beformulated as discrete or mixed variable optimization problems. Furthermore, finding
efficient and lower cost procedures for frequent monitoring and maintenance of the system
to increase their life span is crucially important. In this context, parameter identification,model updating, and sensor placements are some of the most important monitoring
optimization problems. Mining and interpretation of the response data are other major
issues that need advanced computation. Stochastic nature of most engineering system (e.g.
wind and earthquake loads) make the engineering analysis even more complex. While
several solutions are proposed to tackle the issues mentioned above, there is still a serious
need for more cost-effective approaches. Due to their complexity, the engineering andmonitoring problems are difficult to solve using derivative-based and local optimization
algorithm. A viable solution to cope with this limitation is to employ global optimization
algorithms, such as the EC techniques. In the recent past, EC and its branches have been usedin the engineering and health monitoring to solve complex problems that cannot be solved
using conventional methods. The other important issue is that several aspects can beconsidered to optimize systems simultaneously such as topology, configuration, stiffness,and displacement. Therefore, more than one objective should usually be considered for
optimizing a real-world engineering system. This is while there are usually conflicts between
the considered objectives, such as weight-displacement and cost-quality. In this case, themulti-objective optimization concept offers major advantages over the traditional
mathematical algorithms. More specifically, evolutionary multi-objective optimization (EMO)

Special Session Chair
Ke Tang · ketang@ustc.edu.chn

IEEE Conference on Evolutionary Computation
Donostia – San Sebastián, June 8-5
www.cec2017.org · info@cec2017.org

SPECIALSESSIONS

is known as a reliable way to handle these problems in the engineering domain.
This special session strives to gather the latest development of EC applications inengineering and health monitoring. On this basis, this special session include key
applications of EC on different engineering disciplines such as civil engineering, mechanicalengineering, electrical engineering, biomedical engineering, etc. Topics to be included areevolutionary optimization and multi-objective algorithms, as well as evolutionary (big) data
mining algorithms.

Topics•Design optimization (topology, configuration, etc)•

Simulation optimization (grey/black box problems)•

Large scale engineering systems•

Multi and many objective engineering problems•

Expensive engineering problem (limited budget)•

Surrogate-assisted systems•

Highly constrained problems•

Embedding engineering knowledge•

Robust engineering optimization•Probabilistic design optimization•

Reliability-Based Optimization•Bi-level engineering optimization•

Engineering (big) data mining•

Uncertain and noisy systems

2/2