摘要
为了克服传统遗传算法在解决组合优化问题中存在的收敛速度慢、易陷入局部最优解等问题,引入自适应机制调整遗传算子改进遗传算法。根据当前代的进化状态设计遗传算子的自适应调整公式以选取最优遗传算子,从而提高算法的收敛速度和全局搜索能力;为了进一步加快自适应遗传算法的收敛速度并提高算法的执行效率,在算法实现的过程中增加了快速进化机制。实验结果表明,所提出自适应遗传算法在收敛速度、搜索全局最优解及执行速度方面具有较好的综合表现。
This paper proposed an adaptive-evolution-based genetic algorithm to solve the problems in conventional genetic al- gorithm, such as slow convergence speed and easy to fall into local optimal solution. Firstly, in order to choose the optimal genetic operator, this paper designed an adaptive adjust formula for the genetic operator according to the current evolution state to improve the convergence speed and the global searching capability of the algorithm. Secondly, in order to further improve the convergence speed of the proposed adaptive-evolution-based genetic algorithm, it designed a rapid evolution mechanism and added it into the algorithm. Experimental results show that the proposed algorithm can achieve better synthesized performances in convergence speed, searching global optimal solution and execution speed.
出处
《计算机应用研究》
CSCD
北大核心
2015年第11期3222-3225,3229,共5页
Application Research of Computers
基金
国家自然科学基金资助项目(61473179)
山东省优秀中青年科学家科研奖励基金资助项目(BS2013DX032)
关键词
组合优化
遗传算法
遗传算子
自适应
combinatorial optimization
genetic algorithm
genetic operator
auto adaptive
