@inproceedings {pub3614,
	title = {Exploring the multi-modal fitness landscape of a realistic turbofan engine blade optimization},
	author = {Jakub Kmec AND Sebastian Schmitt},
	year = {2018},
	abstract = { Aerodynamic shape optimization has established itself as a valuable tool in the engineering
  design process to achieve highly efficient results. 
  A central aspect for such approaches is the mapping from the design parameters
  which encode the geometry of the shape to be improved to the quality criteria which
  describe its performance. The choices made in the setup of the optimization process
  strongly influence this mapping and thus are expected to have a profound influence on the
  achievable result. In this work we explore the influence of such choices on the effects on
  the shape optimization of a turbofan rotor blade as it can be realized within an aircraft engine design
  process. The blade quality is assessed by realistic three dimensional computational fluid dynamics
  (CFD) simulations.
  We compare the results from the covariance matrix adaptation evolutionary 
  strategy (CMA-ES) with the outcome of a particle swarm optimization (PSO). 
  We also investigate  the changes induced by a different initialization  of the CMA-ES and 
  a variation of its population size. 
  A particular focus is put on the changes in the results by increasing the number of parameters for the blade geometry representation.  
  For all such variations,  we  generally find that the achievable improvement of the blade 
  quality is comparable for most settings and thus rather insensitive to the details of the setup.
  On the other hand,  even supposedly minor changes in the settings, such as using
  a different random seed for the initialization of the optimizer algorithm, lead to very
  different shapes. Optimized shapes which show comparable performance usually differ quite
  strongly in their geometries over the complete blade shape.  
  Our analysis indicates that the fitness landscape for such a
  realistic turbofan rotor blade optimization is highly multi-modal with many local optima,
  where very different shapes show similar performance. 

},
	publisher = { Springer International Publishing AG},
	booktitle = {EngOpt 2018 - 6th International Conference on Engineering Optimisation}
}