基于改进模拟退火遗传算法的路径规划问题研究

Research on path planning problem based on improved simulated annealing genetic algorithm

为了解决复杂环境中的路径规划问题,通过引入并改进模拟退火算法与遗传算法相结合的混合优化策略,以克服传统路径规划算法在全局搜索能力、收敛速度及避免局部最优解方面的局限性,提出了一种基于改进模拟退火遗传算法的路径规划方法。在遗传算法框架内,通过编码方式表示路径,并利用选择、交叉和变异等遗传操作生成新的路径种群。为增强全局搜索能力和跳出局部最优解的能力,引入了模拟退火机制,在遗传算法的交叉和变异操作中融入模拟退火的概率接受准则,允许以一定概率接受较差的解,从而增加种群的多样性。研究过程中,首先设计并实现了改进的模拟退火遗传算法,并设置了对比实验,包括单独使用遗传算法、模拟退火算法以及模拟退火遗传算法进行对比分析。实验结果表明,与单独使用遗传算法和模拟退火算法相比,改进模拟退火遗传算法在复杂环境中的路径规划问题上展现出了显著的优势,有效提升了算法的全局搜索能力、最优解准确度和收敛速度,同时增强了算法对复杂环境的适应能力。

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,convergence speed and avoidance of local optimal solutions by introducing and improving the hybrid optimization 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 solution,this paper introduces the simulated annealing mechanism,and integrates the probability acceptance 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 increase 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,including the genetic algorithm alone,the simulated annealing algorithm and the simulated annealing genetic algorithm for comparative analysis.Experimental results show that compared with the genetic algorithm and simulated annealing algorithm alone,the improved simulated annealing genetic algorithm proposed in this paper shows significant advantages in path planning in complex environments,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.