求解混杂生产调度问题的嵌套混合蚁群算法(英文)

A Nested Hybrid Ant Colony Algorithm for Hybrid Production Scheduling Problems

蚁群算法作为解决优化问题的有力工具 ,它的有效性已经得到了证明 .由于其生物学背景 ,基本蚁群算法被设计来求解复杂的排序类型组合优化问题 ,在连续空间优化问题的求解方面研究很少 .本文提出一种嵌套混合蚁群算法 ,用于解决具有混杂变量类型的复杂生产调度问题 ,在一种新的最佳路径信息素更新算法的基础上 ,提高了搜索效率 .计算机仿真结果表明 ,本文提出的方法在求解此类问题上性能优于另一种基于进化计算的有效方法———遗传算法 .蚁群算法作为解决优化问题的有力工具 ,它的有效性已经得到了证明 .由于其生物学背景 ,基本蚁群算法被设计来求解复杂的排序类型组合优化问题 ,在连续空间优化问题的求解方面研究很少 .本文提出一种嵌套混合蚁群算法 ,用于解决具有混杂变量类型的复杂生产调度问题 ,在一种新的最佳路径信息素更新算法的基础上 ,提高了搜索效率 .计算机仿真结果表明 ,本文提出的方法在求解此类问题上性能优于另一种基于进化计算的有效方法———遗传算法 .

The validity of the ant colony algorithm has been demonstrated as a powerful tool to solve the optimization problems. This technique is used to solve difficult combinatorial optimization problems but is seldom used for continuous space search due to its biological background. A nested hybrid ant colony algorithm is proposed in this paper to solve the complicated production scheduling problem with hybrid variable structures, and a novel optimal path pheromone update algorithm is suggested to promote search efficiency. Computer simulation results show that the proposed method is more effective than genetic algorithms as a kind of evolutionary algorithms in solving such kind of difficult problems.