Robot Path Planning Based on Multi-Strategy Sparrow Search Algorithm
The basic sparrow search algorithm(SSA)has been improved through various strategies to solve the global optimization accuracy and speed problems caused by the loss of population diversity in the later stage of sparrow search algorithm.First,the population was initialized by the improved infinite folding iterative mappin(ICMIC),and the adaptive segmented step factor was introduced into the position update formula of the sparrow detector,which made the fixed proportion coefficient of the observer of the sparrow search algorithm changed dynamically with the number of iterations.Second,the observer's position was combined with the new formula and sine cosine algorithm(SCA),and the previous observer's step size was interfered.And finally,the convergence and accuracy of the improved sparrow search algorithm(ISSA),sparrow search algorithm(SSA),whale algorithm(WOA),grey wolf algorithm(GWO),improved grey wolf algorithm(CGWO),sine cosine algorithm(SCA)and particle swarm optimization algorithm(PSO)were compared on the benchmark function,and applied to path planning.Experiments showed that the improved sparrow search algorithm had good optimization performance.
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