Parameter identification of a nonlinear model using an improved version of simulated annealing - Xiaoxia Tian; Jingwen Yan; Yanchun Yang; Chi Xiao; Qi Zhou (International Journal of Distributed Sensor Networks, 2019)
This article aims to employ an improved simulated annealing algorithm to accurately and efficiently identify parameters of a nonlinear model which describes the nonlinear vortex-induced vertical force. In the general simulated annealing for vortex-induced vertical force models, the energy difference between the new and current solutions is very small so that the acceptance probability is close to 1. Almost all poorer solutions are accepted, which makes simulated annealing inefficient. To improve the performance of simulated annealing, an improved simulated annealing is proposed. First, the energy difference between the new and current solutions is amplified to put the acceptance probability in the interval of [0, 1]. Second, the length of the Markov chain is set as a function of the current temperature instead of the fixed value. Third, the generation criterion of the new solution is revised so that new solutions satisfy constraints and fully explore the neighborhood of the current solution. Simulation results show that improved simulated annealing has good performance in run-time and fitting. According to the results of Wilcoxon’s test, improved simulated annealing outperforms the other algorithms.