CAJ | 학술논문

对遗传算法和模拟退火算法的特点进行了比较，阐述了遗传算法与模拟退火算法集合的必要性。提出了一个用于求解TSP问题的改进的模拟退火和遗传算法。利用遗传算法的全局搜索能力弥补了模拟退火算法容易陷入局部最优的问题。在遗传算法中改进了传统的交叉机制，利用父代染色体与子代染色体进行交叉，解决了传统遗传算法中存在的“早熟”问题。针对模拟退火算法收敛速度慢等问题，提出了新的解生成机制和改良算法，提高了算法的收敛速度。实验测试的结果表明，该方法具有较好的收敛效果和更高的稳定性。
대유전산법화모의퇴화산법적특점진행료비교，천술료유전산법여모의퇴화산법집합적필요성。제출료일개용우구해TSP문제적개진적모의퇴화화유전산법。이용유전산법적전국수색능력미보료모의퇴화산법용역함입국부최우적문제。재유전산법중개진료전통적교차궤제，이용부대염색체여자대염색체진행교차，해결료전통유전산법중존재적“조숙”문제。침대모의퇴화산법수렴속도만등문제，제출료신적해생성궤제화개량산법，제고료산법적수렴속도。실험측시적결과표명，해방법구유교호적수렴효과화경고적은정성。
The characteristics of genetic algorithm and simulated annealing algorithm are compared, it elaborates the necessity of the combination of genetic algorithm and simulated annealing algorithm. An improved simulated annealing and genetic algo-rithm for solving TSP is proposed. The global random searching ability of genetic algorithm makes up the question of the simu-lated annealing algorithm that easy to fall into the local optimal solution. The crossover method of genetic algorithm is changed, the parent chromosomes and offspring chromosomes are crossed, it solves the problems of traditional genetic algorithm“prema-ture”. It proposes new solution generation mechanisms and improved algorithm for that the simulated annealing algorithm con-verges slowly, the method improves the speed of convergence of the algorithm. Experimental test results show that the new algo-rithm has faster convergence and better stability.