为了改善差分灰狼预测算法的早熟收敛、搜索能力不均衡、容易陷入局部最优等问题,提出了一种改进的混合灰狼优化(HGWO)预测算法,可自适应改进和调整差分进化中的变异算子、交叉算子和变异策略。嵌入具有分类预测功能的支持向量机(SVM),同时引入莱维飞行全局搜索更新狼群位置,优化SVM核函数参数γ和惩罚因子C,构建了HGWO-SVM预测算法预测推焦车大车道内物体的运动轨迹。结果表明,与已有算法相比,该算法对行人、自行车、电瓶车、电动三轮车、大中小型四轮汽车的位置预测相对实际值的误差分别降低了4.21、4.14、7.91、2.03、25.53个百分点,预测时间减少了8.8~10 s。可以克服焦炉恶劣的环境影响,准确预测推焦车车道内运动对象的轨迹,为推焦车无人化运行提供主动安全的预测控制方法。

In order to solve the problems of premature convergence, uneven search ability, and tendency to fall into local optimality in differential grey wolf prediction algorithm, an improved hybrid grey wolf optimization (HGWO) prediction algorithm is proposed, which can adaptively improve and adjust the mutation operator, crossover operator, and mutation strategy. Support vector machine (SVM) with classification prediction function is embedded, while Levy flight global search is used to update the position of the wolves, and the SVM kernel function parameter γ and penalty factor C are optimized. Thus, an HGWO-SVM prediction algorithm is built to predict the large lane of the coke pusher. The results show that, compared with the existing algorithms, the relative errors of position prediction of pedestrian, bicycle, battery car, electric tricycle, and large, medium and small four-wheel vehicle are reduced by 4.21, 4.14, 7.91, 2.03, and 25.53 percentage points, respectively, and the prediction time is reduced by 8.8-10 s. It can overcome the harsh environmental impact of coke oven, accurately predict the trajectory of the moving targets in the lane of the coke pushing vehicle, and provide an active and safe predictive control method for the unmanned operation of coke pushing truck.