改进蚁群算法在制造车间调度中的应用研究

Application of Improved Ant Colony Algorithm in Manufacturing Shop Scheduling

提出一种改进蚁群算法来求解FJSP难题。开始阶段利用遗传算法为蚁群算法提供初始信息,采用自适应函数改变状态转移中的信息素先验值,结合新的启发规则增强蚂蚁搜索过程中信息素引导作用。设计一种增量函数,扩大蚂蚁在优势路径与劣势路径上的辨别能力,加快算法后期在最优路径的收敛速度。在设计的算例实验中,实验结果表明改进后的蚁群算法在求解FJSP问题上有较好的优势。

An improved ant colony algorithm is proposed to solve the FJSP problem.At the beginning,genetic algorithm is used to provide initial information for ant colony algorithm.An adaptive function is used to change the prior value of the pheromone in the state transition and a new heuristic rule is used to enhance the pheromone guidance in the search process.An incremental function is designed to expand the ability of ants to distinguish between the dominant path and the inferior path,and to accelerate the convergence speed of the optimal path in the later stage of the algorithm.The experimental results show that the improved ant colony algorithm has better advantages in solving FJSP problems.