Genetic algorithms (GAs), particle swarm optimisation (PSO) and differential evolution (DE) have proven to be successful in engineering optimisation problems. The limitation of using these tools is their expensive computational requirement. The optimisation process usually needs to run the numerical model and evaluate the objective function thousands of times before converging to an acceptable solution. However, in real world applications, there is simply not enough time and resources to perform such a huge number of model runs. In this study, a computational framework, known as DE-kNN, is presented for solving computationally expensive optimisation problems. The concept of DE-kNN will be demonstrated via one novel approximate model using k-Nearest Neighbour (kNN) predictor. We describe the performance of DE and DE-kNN when applied to the optimisation of a test function. The simulation results suggest that the proposed optimisation framework is able to achieve good solutions as well as provide considerable savings of the function calls compared to DE algorithm. © 2010 Elsevier Ltd. All rights reserved.
- Differential evolution algorithm
- K-Nearest Neighbour