Individual-based Management of Meta-models for Evolutionary Optimization with Application to Three-Dimensional Blade Optimization

To reduce the number of expensive fitness function evaluations in evolutionary optimization, individual-based and generation-based strategies for metamodel management (evolution control) have been proposed. In this work, four individual-based frameworks for meta-model management are investigated. A feedforward neural network is employed to construct an approximation model of the fitness function. Structure optimization of the neural network is used to reduce the approximation error. In an attempt to adapt the number of controlled individuals, adaptation mechanisms are suggested based on the model error, selection error, rank correlation, and fitness correlation. Preliminary results indicated that the adaptation mechanisms do not work well as expected. Two of the frameworks are implemented in 3D blade design optimization. The results showed that individual-based meta-model management is promising, though further efforts are still needed to improve the performance of the evolutionary algorithms with meta-models for fitness estimation.