An improved genetic algorithm in path planning for mobile robot

This paper proposed a new way of crossover and mutation for genetic algorithm to prevent the local optima and guarantee the feasibility of the mutated path. Improving the adaptive adjustment of crossover and mutation probability to improve the search efficiency of the algorithm optimization. Contrary to the disadvantages of genetic algorithm, such as it's easy to fall into the optimal local and premature, the Metropolis based on simulated annealing algorithm is used for the optimization of genetic algorithm. By using the improved genetic algorithm to different environment models and comparing with other genetic algorithms, the results show that the use of improved genetic algorithm has better convergence speed and optimization capabilities.