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An Effective Ensemble-Based Method for Creating On-the-Fly Surrogate Fitness Functions for Multi-objective Evolutionary Algorithms
- Source :
- SYNASC
- Publication Year :
- 2013
- Publisher :
- IEEE, 2013.
-
Abstract
- The task of designing electrical drives is a multi-objective optimization problem (MOOP) that remains very slow even when using state-of-the-art approaches like particle swarm optimization and evolutionary algorithms because the fitness function used to assess the quality of a proposed design is based on time-intensive finite element (FE) simulations. One straightforward solution is to replace the original FE-based fitness function with a much faster-to-evaluate surrogate. In our particular case each optimization scenario poses rather unique challenges (i.e., goals and constraints) and the surrogate models need to be constructed on-the-fly, automatically, during the run of the evolutionary algorithm. In the present research, using three industrial MOOPs, we investigated several approaches for creating such surrogate models and discovered that a strategy that uses ensembles of multi-layer perceptron neural networks and Pareto-trimmed training sets is able to produce very high quality surrogate models in a relatively short time interval.
- Subjects :
- Mathematical optimization
Fitness function
Optimization problem
Computer science
Fitness approximation
business.industry
Evolutionary robotics
Evolutionary algorithm
Interactive evolutionary computation
Machine learning
computer.software_genre
Evolutionary computation
Surrogate model
Artificial intelligence
business
computer
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2013 15th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing
- Accession number :
- edsair.doi...........ac905d90ab38b383ab432f6d1010e5a7