Integrated multi-strategy sand cat swarm optimization for path planning applications

Abstract

An integrated multi-strategy sand cat swarm optimization algorithm is proposed to address the shortcomings of the sand cat swarm algorithm, such as inefficient solutions, insufficient optimization accuracy, and a tendency to fall into local optimal solutions. The algorithm introduces an improved circle chaotic mapping to balance the population distribution, water wave dynamic convergence factor to maintain population diversity, and a lens opposition-based learning to enhance the global optimization capability. Additionally, the golden sine strategy is incorporated to improve the local search ability. Experiments on 23 test functions demonstrate the new algorithm's optimal average performance on 18 of them. It was further applied to 9 2D path planning instances and 2 3D path planning instances, all of which were able to find the shortest path. The results show that the improved algorithm is less prone to local optimization, exhibits high stability, and can effectively solve path planning problems.