A multi-objective cross-entropy optimization algorithm and its application in high-speed train lateral control

Abstract

In this paper a novel improved multi-objective cross-entropy optimization (MOCEO+) algorithm is proposed. We seek to provide a low-cost, fast and effective solution for complex multi-objective problems. Firstly, a population size segmentation mechanism is adopted to reduce the computational cost. This also helps to increase the exploration ability of the algorithm to the optimal Pareto front and the convergence rate is accelerated as well. Then, an individual selection mechanism based on hyper volume (HV) sorting strategy is proposed to retain the elite individuals in the evolution process. Finally, a recombination mechanism is provided to increase diversity of the population individuals and to avoid local optimum. The test results of 2-, 3-, 5-objective WFG test functions indicate that MOCEO+ offers better performance and faster convergence compared with six optimization algorithms. In order to verify feasibility and effectiveness of the MOCEO+ in engineering practice, it is applied to the parameter optimization of the repetitive learning controller for the high-speed train lateral suspension system. The simulation demonstrates that the suspension system optimized by MOCEO+ has better lateral stability compared with three other algorithms. Particularly, the lateral vibration is significantly decreased in the sensitive frequency range [1,2] Hz of human body.