基于改进蜻蜓算法的斗轮取料机多目标优化

Multi-objective Optimization of Bucket Wheel Reclaimer Based on Improved Dragonfly Algorithm

针对斗轮取料机系统能耗高、重量大、制造成本高、设计变量多等特点,提出一种改进的蜻蜓算法用以求解斗轮取料机的多目标优化问题。提出的改进蜻蜓算法基于自然现象和物理现象,采用空气阻力和库仑力混合组成的策略对传统蜻蜓算法进行改进,并通过测试函数验证了改进后蜻蜓算法的性能。然后建立考虑斗轮取料机可靠性和振动频率约束的质量与转动惯量的多目标优化模型,利用改进后的蜻蜓算法进行多目标求解,获得斗轮取料机的Pareto前沿解集,选择以质量与转动惯量合适权重比为例进行优化,验证开展斗轮取料机多目标优化的有效性。结果表明,优化得到的阶梯截面布局方案具有更小的质量和转动惯量值,同时可以有效避开斗轮取料机系统的共振问题,可以使斗轮取料机的性能得到有效改善,为未来的整机材料-结构-控制多学科一体化协同优化提供基础。

Aiming at the characteristics of high energy consumption, maximum gross weight, high manufacturing cost and multiple design variables of the bucket wheel reclaimer(BWR), an improved dragonfly algorithm is proposed to solve the multi-objective optimization problem of the BWR. A hybrid strategy composed of air resistance and Coulomb force is proposed to improve the traditional dragonfly algorithm(DA) based on the natural and physical phenomena. The performance of the improved DA is verified by the test functions. A multi-objective optimization model considering the mass and rotational inertia of the BWR’s reliability and frequency constraints is established. The improved DA is used for multi-objective solution to obtain the Pareto solution set of the BWR, and a suitable weight ratio of mass and rotational inertia is selected as an example for optimization research. The main purpose is to verify the effectiveness of the multi-objective optimization of the BWR. Results show that the optimized structure layout not only has smaller mass and rotational inertia values, but also can effectively avoid the resonance of the BWR. In addition, it can effectively improve the performance of the BWR, and provide the basis for the future integration of material-structure-control multidisciplinary design optimization.