模糊逻辑遗传算法优化算法及其在装载机工作装置优化设计中的应用

摘要

固定参数的遗传算法容易陷入过早收敛，进入局部最优状态等缺陷。在遗传算法的基础上，提出一种基于模糊逻辑控制的算法，即利用种群平均适应值变化建立两个模糊逻辑控制器动态调整交叉率Pe变异率Pm，运用惩罚函数法通过设置不同的阈值改变因子大小来调整适应值函数，以提高算法的自适应性、收敛速度及获得全局最优解的能力。运用常规优化算法、简单遗传算法及改进优化算法对ZL50C型轮式装载机工作装置进行优化设计和对比。结果表明：基于模糊逻辑遗传算法优化设计方法，可使优化目标提高16．9％，具有优化个体进化过程，计算效率高和计算%Fixed parameters of genetic algorithm are easy to fall into premature convergence and local optimum situ- ation. In this paper, an improved genetic algorithm is proposed in order to improve the convergence speed and to obtain global optimal solution. It utilizes changes in the average fitness value of population to dynamically adjust the rates of crossover and mutation with two fuzzy logic controllers. In addition, the penalty function method is used to transform the fitness function by setting the threshold. The optimization results of ZLSOC Wheel Loader working de- vice by the conventional optimization algorithm, the simple genetic algorithm and the improved optimization algo- rithm proposed in this paper, were compared with each other. The analysis indicates that the optimization perform- ance of the improved genetic algorithm is increased by 16. 9%. It has advantages such as optimization of individual evolution and higher computational efficiency and design precision.