改进的自适应复制、交叉和突变遗传算法  

Improved Genetic Algorithms for Adaptive Replication Crossover and Mutation

作　　者：陈琳[1] 王子微 莫玉良 潘海鸿[1] CHEN Lin;WANG Zi-wei;MO Yu-liang;PAN Hai-hong(College of Mechanical Engineering,Guangxi University,Nanning Guangxi 530004,China)

机构地区：[1]广西大学机械工程学院,广西南宁530004

出　　处：《计算机仿真》2022年第8期323-326,362,共5页Computer Simulation

基　　金：国家自然科学基金(51465005);桂科(AA18118002,2017AA24012,AB16380237);南宁市重点研发计划项目(20181018-1,20181018-3)。

摘　　要：使用自适应遗传算法求解复杂多极值函数最优解时,常存在陷入局部最优解的早熟现象。针对上述问题提出一种改进的自适应复制、交叉和突变遗传算法。算法在原有自适应遗传算法的基础上,提出复制率、交叉率和突变率根据种群规模、种群中个体的分布情况和遗传迭代不同阶段进行自适应变化的方法,并给出遗传算子计算公式。使用常用遗传算法测试函数F_(7)函数进行50次重复实验测试,实验结果表明,上述算法不易陷入局部最优解,收敛速度快,精度高,证明所提算法的有效性。When using the adaptive genetic algorithm to solve the optimal solution of a complex multi-extremum function,there is often the premature phenomenon of falling into the local optimal solution.Aiming at this problem,an improved adaptive replication,crossover and mutation genetic algorithm is proposed.Based on the original adaptive genetic algorithm,this algorithm proposes a method of adaptively changing the replication rate,crossover rate,and mutation rate according to the population size,the distribution of individuals in the population,and the different stages of genetic iteration,and provides genetic operator calculations formula.Using the commonly used genetic algorithm test function F_(7) function to carry out 50 repeated experimental tests,the experimental results show that the algorithm is not easy to fall into the local optimal solution,the convergence speed is fast,and the accuracy is high,which proves the effectiveness of the algorithm.

关 键 词：复杂多极值函数 早熟现象 自适应 复制率 全局最优 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]