Ant colony optimization algorithm for multiprocessor scheduling problem
求解多处理机调度问题的蚁群算法

The ant colony optimization (ACO) algorithm is an intelligent optimization algorithm which simulates the ants' behaviors in searching for food. ACO is especially suitable for the discrete combination optimization problems. In this paper, we presented an ACO algorithm for multiprocessor scheduling problem. In the algorithm, each ant represented one processor and selected its tasks. The algorithm measured the urgency of each task by analyzing its critical path, the earliest and latest starting time. With the help of the obtained information, the ant selected the proper tasks for the processor it represented. Experimental results show our algorithm can obtain more efficient scheduling results than classical algorithms.