计算机应用与软件2012，Vol.29Issue(8)：52-54,109,4.
两段式蚁群粒子群混合优化算法求解MFJSP
TWO-STAGE BLENDED-ALGORITHM OF ANT COLONY AND PARTICLE SWARM OPTIMISATIONS FOR SOLVING MFJSP
摘要
Abstract
To overcome the shortcomings existed in solving MFJSP with single optimisation algorithm, a two-stage blended-algorithm of ant colony and particle swarm optimisations (TSAPO) has been put forward in this paper. In TSAPO, the multi-objective optimisation is realised by decomposition method through two stages. In first stage, the subset of the algorithm is determined and the corresponding ant transition probability is designed, and the ant colony optimisation is employed to acquire the process routing. In second stage, by designing particle swarm decoding, the scheduling problem is solved by particle swarm optimisation capable of adaptive parameters regulation. The experiment of standard example is carried out using TSAPO algorithm and better optimised object is obtained than using other algorithms in comparison. It shows that the TSAPO algorithm has better optimisation effect in solving MFJSP.关键词
柔性作业车间调度问题/多目标优化/蚁群优化算法/粒子群优化算法/两段式蚁群粒子群优化算法Key words
Flexible job-shop scheduling problem/Multi-objective optimisation/Ant colony optimisation/Particle swarm optimisation Two-stage algorithm of ant colony and particle swarm optimisations分类
信息技术与安全科学引用本文复制引用
李莉,周春楠..两段式蚁群粒子群混合优化算法求解MFJSP[J].计算机应用与软件,2012,29(8):52-54,109,4.基金项目
国家自然科学基金项目(71003020) （71003020）
中央高校基本科研业务费专项基金项目(DL10AB02). （DL10AB02）
