Multi-Objective Path Planning for Mobile Robots Using an Enhanced NOA Algorithm

In order to establish a reasonable path evaluation criterion in practice and reduce errors, this paper chooses an appropriate function, such as Manhattan distance or Chebyshev distance. A new heuristically adaptive path optimization strategy is proposed that combines swap and insert operations for better solutions diversity; it incorporates a bidirectional heuristic crossover operator to increase the path's quality and speed up convergence. In addition, a dynamic fitness selection function based on the number of iterations was introduced to improve the overall exploration-exploitation trade-off ability in all stages of the algorithm, which can reduce the occurrence of early stopping. Simulation experiments were performed in a complicated environment with 12 obstructions and 42 objective points; there were 20 repetitions. Compared with the results from sparrow search algorithm (SSA), grey wolf optimizer (GWO), and the traditional nutcracker optimization algorithm (NOA) algorithm, the proposed algorithm achieved an average reduction in the optimal path length of about 8.6%, around 14%, and more than half as much as a 38.73%.