Elite strategy-based improved NSGA-II algorithm for multi-robot task allocation in orbital bolting operations

Abstract

In order to improve the efficiency of rail bolt automation operation, this study proposes a non-dominated sorting genetic algorithm II (NSGA-II) based on the improvement of elite strategy for the multi-robot task allocation problem. First, a multi-objective optimization model is established by combining the actual demands of rail bolt operations. Then, the classical NSGA-II algorithm is improved by introducing an elite strategy to enhance its global search capability and convergence performance. Finally, the effectiveness and superiority of the improved algorithm in task assignment are verified by simulation experiments. The experimental results show that the improved NSGA-II algorithm has significant advantages in optimizing the efficiency of rail bolting operations and task balancing, which provides a strong support for task allocation in multi-robot systems.