Multi-objective optimal design of a novel multi-function rescue attachment based on improved NSGA-II

摘要

A novel multi-function rescue attachment with tonging, shearing and grasping capabilities is proposed to improve the rescue operation efficiency and save time when switching between different attachments during rescue operations. The tonging and shearing form, as well as the grasping form, of the rescue attachment, is analyzed. Furthermore, the tonging diameter, shearing diameter and grasping force are simultaneously chosen as objective functions, and respective mathematical models are established for estimating the attachment's performance. The nondominated sorting genetic algorithm (NSGA-II) is used for multi-objective optimization of the attachment. To improve the population diversity and increase the search capability and uniformity of the distribution of Pareto fronts of NSGA-II, the elitist strategy, crossover operator and mutation operator are improved. The Pareto front is visualized in a specific polygon, and the best solution is then selected. More importantly, the simulation results indicate that the astringency, population diversity and search ability of the proposed algorithm can be improved. The uniformity of the Pareto front distribution can also be improved, which may lead to an increased number of available design schemes. The values of the three objectives of the best solution obtained via the improved algorithm are superior to those for other algorithms.