Dynamic balance adaptive colony algorithm solving Job-Shop scheduling

被引：0

作者：

Wang, Yan-Hong ^{[1
]}

Wang, Wen-Xia ^{[1
]}

Yu, Hong-Xia ^{[1
]}

Chen, Li ^{[1
]}

机构：

[1] School of Information Science and Engineering, Shenyang University of Technology

来源：

Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2013年 / 19卷 / 10期

关键词：

Ant colony optimal algorithms; Dynamic balance; Job-Shop scheduling; Self-adaption;

D O I：

10.13196/j.cims.2013.10.WANGYanhong.20131017

中图分类号：

学科分类号：

摘要：

Aiming at the defect that ant colony optimization easily fell into local optimal solution and had long search time, a Dynamic Balance Adaptive Colony Algorithm (DB-ACA) was proposed to solve Job-Shop scheduling problem. An adaptive adjustment strategy of volatility coefficient was introduced to overcome premature convergence, which adjust the evaporation coefficient proactively according to the tendencies of the intermediate solution towards to a local optimum. A dynamic equilibrium mechanism was also put forward to improve the global search capability and the search speed of the algorithm, which changed the distribution of the solution dynamically according to the concentration of solutions distribution when the convergence coefficient was greater than the set threshold. 100 simulation tests on the classic benchmark scheduling problems were run separately, and compared with other four typical algorithms from literatures. The simulation results showed that the proposed algorithm had better performance than others in the convergence speed, the solution quality and the solution stability.

引用

页码：2521 / 2527

页数：6

共 16 条

[1]

Pinedo M.L., Scheduling: Theory, Algorithms, and Systems, (2008)

[2]

Haupt R., A survey of priority rule based scheduling, OR Spectrum, 11, 1, pp. 3-16, (1989)

[3]

Metaxiotis K.S., Askounis D., Psarras J., Expert systems in production planning and scheduling: A state of the art survey, Intelligent Manufacturing, 13, 4, pp. 253-260, (2002)

[4]

Liu M., A survey of data-based production scheduling methods, Acta Automatica Sinica, 35, 6, pp. 785-806, (2009)

[5]

Huang Z., Zhang D., Li Q., Ant colony algorithm and summary of its improved algorithms, Computing Technology and Automation, 25, 3, pp. 35-38, (2005)

[6]

Pu X., Sun K., An improved adaptive ant colony optimization algorithm with application, Journal of Chongqing University of Posts and Telecommunications, 23, 3, pp. 331-335, (2011)

[7]

Song X., Zhu Y., Yin C., Et al., Hybrid ant colony algorithm for fuzzy Job Shop scheduling, Computer Integrated Manufacturing Systems, 13, 1, (2007)

[8]

Liao Q., Zhou K., Solving Job Shop scheduling optimization problem with genetic algorithm, Computer Integrated Manufacturing Systems, 5, 5, pp. 62-64, (1999)

[9]

Mnch L., Schabacker R., Pabst D., Et al., Genetic algorithm based sub-problem solution procedures for a modified shifting bottleneck heuristic for complex Job Shops, European Journal of Operational Research, 177, 3, pp. 2100-2118, (2007)

[10]

Ren Z., Feng Z., Ke L., Et al., Enhanced ant colony optimization algorithm based on clustering analysis, Control and Decision, 25, 8, pp. 1201-1206, (2010)

← 1 2 →