优化过程知识引导的挖掘机动臂结构优化设计新策略

Novel structural optimization design strategy for excavator booms using knowledge-guided optimization

针对现有智能优化算法求解挖掘机动臂结构优化问题效率低、易陷入局部最优等问题,提出一种优化过程知识挖掘、处理和利用策略.构建基于任务知识指导的优化过程知识挖掘、处理和利用机制,探讨了运用优化过程知识引导遗传算法数值优化搜索的途径,并建立群体成员分组的知识利用算子以及选择、交叉、变异操作的知识利用算子.以中型液压挖掘机动臂结构优化任务为例,以所构建的知识利用算子引导遗传算法的数值优化搜索,通过与现有遗传算法进行对比,表明优化过程知识的利用可以有效提高优化效率并改善优化效果.

To solve such intelligent optimization algorithms for excavator booms as low-efficiency of structural optimization and ease of local optimum, a knowledge mining, processing and utilization strategy is first proposed during optimization process. By constructing the task-based knowledge-guided mechanism, an optimized searching method is then investigated for genetic algorithm. Finally, the knowledge-utilization operators of member grouping, together with those of selection, crossover and mutation operations, are established. In comparison with existing genetic algorithms, the knowledge utilization during optimization process can enhance optimization efficiency and improve optimization effectiveness.