求解0/1背包问题的自适应遗传退火算法

针对标准遗传算法易早熟收敛以及收敛速度慢的问题,提出一种自适应遗传退火算法用于解决高维约束优化问题.该算法采用轮盘赌和最优保存策略相结合的选择机制,并结合自适应交叉、变异概率,继而引入模拟退火算法,加快迭代后期算法的收敛速度.最后,比较了标准遗传算法和自适应遗传算法的实验结果,证明了自适应遗传退火算法在0/1背包应用中的高效性和精确性.

Adaptive genetic annealing algorithm of solving 0/1 knapsack

For the problem of premature convergence and slow convergence about the standard genetic algorithm, this paper proposes an adaptive genetic annealing algorithm used to slove the high-dimensional optimization constrained problem. It combines roulette with the optimal preservation strategy which combines adaptive crossover with mutation probability, then introduces simulated annealing algorithm so as to speed up the convergence rate of interactive post. Finally, the experiment compares the results of the two genetic algorithms and represents that adaptive genetic annealing algorithm is more accurate and efficient in resolving 0/1 knapsack problem.