摘要
针对现有遗传算法在求解多参数问题时出现收敛精度低、收敛速度慢、易陷入局部最优等问题,提出一种改进的自适应遗传算法。该算法引入复制算子、种群密集度函数和精英选择策略,提出根据种群迭代次数和个体适应度的自适应策略调节交叉概率和变异概率,很好地平衡了遗传算法的全局搜索能力和局部寻优能力。总结出具有代表意义的测试函数,通过求解测试函数和旅行商问题,证明改进的自适应遗传算法的收敛精度、收敛速度等均有明显的提高。
An improved adaptive genetic algorithm is proposed to solve multi-parameter problems with low convergence accuracy,slow convergence speed and easy to fall into local optimization.The algorithm introduces a replication operator,population density function and elite selection strategy,and proposes an adaptive strategy to adjust the crossover probability and mutation probability according to the population iteration times and individual fitness,which balances the global search ability and local optimization ability of the genetic algorithm.By solving the test function and traveling salesman problem,it is proved that the convergence accuracy and convergence speed of the improved adaptive genetic algorithm are significantly improved.
作者
黄涛
邓斌
何栋
许冠麟
HUANG Tao;DENG Bin;HE dong;XU Guan-lin(College of Mechanical Engineering,Southwest Jiaotong University,Chengdu Sichuan 610000,China)
出处
《计算机仿真》
2024年第3期347-351,464,共6页
Computer Simulation
关键词
复制算子
自适应交叉算子
自适应变异算子
种群密集度函数
测试函数
旅行商问题
Replication operator
Adaptive crossover operator
Adaptive mutation operator
Population density function
Test function
Travelling salesman problem
