Improved Sparrow Search Algorithm applied to path planning of mobile robot

Sparrow Search Algorithm(SSA) is a new swarm intelligent optimization algorithm that simulates the foraging behavior and anti-predation behavior of sparrows. It has the advantages of simple calculation, fast convergence, and few parameters that need to be adjusted. However, in the application of mobile robot path planning, there is insufficient population diversity in the later iterations and it’s easy to fall into a locally optimal solution. In order to solve these problems of the sparrow search algorithm in the path planning of mobile robots, an improved sparrow search algorithm is proposed. First, the algorithm uses opposition-based learning to optimize the initial population of sparrows, improves the quality of the initial solution, and enhances the local search ability of the algorithm. Second, it mixes the Metropolis criterion in the simulated annealing algorithm and makes the algorithm able to accept the new solution by judging whether to accept the new solution, so as Jumping out of the local optimum and strengthen the global search capability. Finally, the performance of the proposed improved algorithm is verified on 2D grid maps of different specifications built on the MATLAB platform. The simulation results show that the improved SSA has better optimization performance than the SSA and Particle Swarms Optimization and other traditional intelligent algorithms, and can effectively jump out of the local optimum and also plan a safe and feasible movement with the best cost and satisfying constraints in a stable and efficient manner.