A hybrid global optimization algorithm and its application to parameter estimation problems

Global optimization has been an area of active research for the past few decades. There are many different global optimization algorithms that have been proposed and studied. Among these, differential evolution (DE) is a promising population-based stochastic global optimization algorithm. It has been gaining popularity in the recent past due to its capability to handle non-convex, non-differentiable and multimodal objective functions. In this work, a hybrid global optimization (HGO) method with a novel stopping criterion is proposed. The proposed algorithm integrates DE with a taboo list of taboo search and self-adaptive strategies. The taboo list/check prevents revisiting the same area, thus increasing the population diversity and computational efficiency. The self-adaptive strategy reduces the algorithm parameters to be provided and makes the algorithm more robust. Furthermore, a novel stopping criterion based on the number of rejected points is developed. The effectiveness of the proposed stopping criterion and HGO is assessed on more than 30 benchmark problems with 2–30 variables. The performance of HGO is compared with some state-of-the-art global optimization algorithms recently published in the literature. The results show that the reliability of finding the global optimum by HGO is slightly better than that by the latest DE techniques in the literature, and the number of function evaluations (NFE) of HGO is significantly reduced. Finally, HGO is tested for parameter estimation in dynamic models. In conclusion, the proposed HGO algorithm with the novel stopping criterion is found to be reliable, efficient and suitable for application problems. Copyright © 2011 Curtin University of Technology and John Wiley & Sons, Ltd.