基于改进的蚁群算法（ACO）的混合多目标AGV调度

Research On Hybrid Multi-objective AGV Scheduling Based on Improved Ant Colony Algorithm

摘要:

蚁群算法是一种通过模拟蚁群的寻路行为对现实问题进行优化的现代智能仿生算法。针对实现AGV任务作业调度时，行驶路径最短的实际应用需求，本文将AGV的路径优化模型转化为旅行商问题，分析了多目标AGV优化中出现的冲突问题。在本文中，尝试了一种直接通信机制来进行改进传统算法，改进后的方法能够更好地维持种群的持久性，最终对于AGV调度规划起到积极的作用，有效地提高了AGV调度系统的效率。

Abstract:

Ant colony algorithm is a modern intelligent bionic algorithm that optimizes real problems by simulating the ant colony's pathfinding behavior. In order to realize the shortest practical application requirements of AGV task scheduling, this paper transforms the path optimization model of AGV into a traveling salesman problem, and analyzes the conflicts in multi-target AGV optimization. In this paper, a direct communication mechanism is tried to improve the traditional algorithm. The improved method can better maintain the persistence of the population. Finally, it plays an active role in the AGV scheduling and effectively improves the AGV scheduling system.