Abstract:

To address the issue of local oscillations in the slime mould algorithm (SMA) during path planning within complex environments, a multi-strategy improved hierarchical guidance SMA (MHGSMA) is introduced to enhance the search performance of SMA. Based on a priori analysis of the early-stage search behavior of slime moulds, a hierarchical method is developed to achieve a more rational division between elite and general populations. While adhering to the inherent search mechanism of SMA, the guidance functions for each population are refined to expand the search scope. Additionally, a greedy selection strategy is incorporated further to enhance the search efficiency and accuracy of the SMA. Simulation experiments for path planning are conducted in four distinct obstacle environments by comparing the MHGSMA, SMA, and hierarchical guided SMA (HG-SMA) for smooth path planning. The analysis of convergence speed, global path length, and accuracy reveals that MHGSMA excels in path planning. In complex environments, it shows improvements of 58.6% and 42% in convergence speed, 9.3% and 6% in path length, and 38.5% and 63.6% in accuracy, significantly enhancing SMA’s search performance. At the same time, under the same conditions, MHGSMA also demonstrates strong competitiveness compared to particle swarm optimization and grey wolf optimizer.

Key words: slime mould algorithm (SMA), hierarchical method, guidance strategy, greedy selection, path planning