Optimization algorithm and application of sand cat swarm based on sparrow warning mechanism and spiral search strategy

A sand cat swarm optimization algorithm was presented, and it incorporates a sparrow warning mechanism and spiral search to overcome the challenges related to weak global exploration capabilities and susceptibility to local optima in traditional sand cat swarm optimization algorithms. Firstly, the integration of Cubic chaotic mapping results in a more uniformly distributed initial population, enhancing the varied nature of the sand cat population and expediting algorithm convergence. Secondly, the incorporation of a spiral search strategy during the prey search phase enables the sand cat to explore multiple paths for better positional adjustments, thereby enhancing the algorithm's ability to conduct extensive global exploration. Finally, the introduction of the sparrow warning mechanism facilitates quicker convergence speed for the sand cat swarm algorithm. The performance of the enhanced algorithm was assessed utilizing 4 core test functions from the CEC2017 benchmark set and compared against path planning experiments of PSO, GWO, AWOA, GA, and SCSO algorithms in complex maps. The improved algorithm achieved a 9.46%, 14.83%, 14.32%, 5.76%, and 1.06% reduction in the optimal value of the shortest path compared to the algorithms, respectively. The enhanced algorithm demonstrated remarkable search efficiency, the ability to avoid local optima, and advantages in convergence speed, accuracy, and optimization time.