【摘要】 遗传算法易搜索到全局最优解,但局部寻优能力差且易发生早熟、随机漫游现象.基于对本文所采用的基本遗传算法的原理和实施过程介绍的基础上,针对其缺陷提出改进措施:利用混沌序列的“遍历性、随机性、规律性”的特点生成初始种群;采用最优个体储存、最差个体替换策略.在改进遗传算法的基础上,又引入自适应的交叉、变异概率公式,幅度系数调节交叉率、变异率形成自适应遗传算法.通过十五杆平面桁架的数值算例,自适应遗传算法的优化结果、优化进程与基本遗传算法、改进遗传算法进行了对比,验证自适应遗传算法的优越性能.更多还原

【Abstract】 Genetic algorithm searches global optimum solution easily,but its local search optimization ability is poor and premature convergence and random roam can easily take place.Based on basic genetic algorithm theory and its practice course that this paper uses,some improved measures to its shortcomings are presented: by means of the chaos serial’s properties of "ergodicity,randomness,regularity",original population is generated;the strategy that the best individual is saved and the worst individual is replaced is adopted.On the base of improved genetic algorithm,adaptive crossover and mutation rate formula are introdued.At the same time,the crossover rate and mutation rate are adjusted by extent coefficient to form adaptive genetic algorithm.By a numerical example of the fifteen bar truss,the optimal results and courses of the alyorithm are compared with that of basic genetic algorithm and improved genetic algorithm so that its advantage is demonstrated.更多还原