With the rapid development of intelligent transportation, intelligent algorithms and path planning have become effective methods to relieve traffic pressure. Intelligent algorithm can realize the priority selection mode in realizing traffic optimization efficiency. However, there is local optimization in intelligence and it is difficult to realize global optimization. In this paper, the antilearning model is used to solve the problem that the gray wolf algorithm falls into local optimization. The positions of different wolves are updated. When falling into local optimization, the current position is optimized to realize global optimization. Extreme Learning Machine (ELM) algorithm model is introduced to accelerate Improved Gray Wolf Optimization (IGWO) optimization and improve convergence speed. Finally, the experiment proves that IGWO-ELM algorithm is compared in path planning, and the algorithm has an ideal effect and high efficiency.

中文翻译：

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路线规划中改进的灰狼优化-极限学习机算法的优化研究

随着智能交通的迅猛发展，智能算法和路径规划已成为缓解交通压力的有效方法。智能算法可以实现流量优化效率中的优先级选择模式。但是，智能中存在局部优化，难以实现全局优化。本文采用反学习模型来解决灰狼算法陷入局部优化的问题。不同狼的位置会更新。在进行局部优化时，将优化当前位置以实现全局优化。引入了极限学习机（ELM）算法模型，以加速改进的灰狼优化（IGWO）优化并提高收敛速度。最后，