Game Theory Approach to Engine Performance Optimization
2008-01-0871
04/14/2008
- Event
- Content
- Genetic Algorithms have proved to be very useful as global search methods for multi-dimensional optimization problems. One drawback, however, is that they are inefficient from the point of view of the number of function evaluations. This paper presents a two phase approach to optimization, using Game Theory in an initial step which provides a family of designs which are close to the Pareto frontier. The starting population for the genetic algorithm is then selected from the non-dominated designs produced in the first phase. This ensures that the genetic algorithm starts with a population of points which are already optimized to a large degree.
- Pages
- 9
- Citation
- Fateh, N., Parashar, S., and Silvestri, J., "Game Theory Approach to Engine Performance Optimization," SAE Technical Paper 2008-01-0871, 2008, https://doi.org/10.4271/2008-01-0871.