摘要
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。
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.
出处
《重庆邮电大学学报（自然科学版）》
CSCD
北大核心
2013年第1期138-142,共5页
Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition)
基金
上海市教育委员会重点学科建设项目(J51301)~~
关键词
遗传算法
优化问题
模拟退火
0
1背包
自适应遗传退火算法
genetic algorithm
optimization problem
simulated annealing
0/1 knapsack
adaptive genetic annealing algorithm
