Research on path planning problem based on improved simulated annealing genetic algorithm
This paper aims to solve the path planning problem in complex environment,and to overcome the limitations of traditional path planning algorithms in terms of global search ability,conver-gence speed and avoidance of local optimal solutions by introducing and improving the hybrid opti-mization strategy combining simulated annealing algorithm and genetic algorithm.In this paper,we propose a path planning method based on an improved simulated annealing genetic algorithm,in which the path is represented by coding within the framework of the genetic algorithm,and a new path population is generated by genetic operations such as selection,crossover,and mutation.In order to enhance the global search ability and the ability to jump out of the local optimal solu-tion,this paper introduces the simulated annealing mechanism,and integrates the probability ac-ceptance criterion of simulated annealing into the crossover and mutation operations of the genetic algorithm,so as to allow the poor solution to be accepted with a certain probability,so as to in-crease the diversity of the population.In the process of research,an improved genetic algorithm for simulated annealing was designed and implemented,and comparative experiments were set up,in-cluding the genetic algorithm alone,the simulated annealing algorithm and the simulated annealing genetic algorithm for comparative analysis.Experimental results show that compared with the ge-netic algorithm and simulated annealing algorithm alone,the improved simulated annealing genetic algorithm proposed in this paper shows significant advantages in path planning in complex envi-ronments,effectively improves the global search ability,optimal solution accuracy and convergence speed of the algorithm,and enhances the adaptability of the algorithm to complex environments.