[1]曹坤煜,陈永当,宋辛辛,等.改进免疫遗传算法求解柔性作业车间调度问题[J].计算机技术与发展,2020,30(11):174-179.[doi:10. 3969 / j. issn. 1673-629X. 2020. 11. 032]
　CAO Kun-yu,CHEN Yong-dang,SONG Xin-xin,et al.Flexible Job-shop Scheduling Problem Solved by Improved Immune Genetic Algorithm[J].,2020,30(11):174-179.[doi:10. 3969 / j. issn. 1673-629X. 2020. 11. 032]
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改进免疫遗传算法求解柔性作业车间调度问题()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
30
期数:
2020年11期
页码:
174-179
栏目:
应用开发研究
出版日期:
2020-11-10

文章信息/Info

Title:
Flexible Job-shop Scheduling Problem Solved by Improved Immune Genetic Algorithm
文章编号:
1673-629X(2020)11-0174-06
作者:
曹坤煜1; 2; 陈永当1; 2; 宋辛辛1; 强冰冰3
1. 西安工程大学 机电工程学院,陕西 西安 710600; 2. 西安市现代智能纺织装备重点实验室,陕西 西安 710600; 3. 昆明理工大学 机电工程学院,云南 昆明 650500
Author(s):
CAO Kun-yu1; 2; CHEN Yong-dang1; 2; SONG Xin-xin1; QIANG Bing-bing3
1. School of Mechanical and Electrical Engineering,Xi’an Polytechnic University,Xi’an 710600,China; 2. Xi’an Key Laboratory of Modern Intelligent Textile Equipment,Xi’an 710600,China; 3. School of Mechanical and Electrical Engineering,Kunming University of Science and Technology, Kunming 650500,China
关键词:
柔性作业车间调度; 免疫遗传算法; 混合策略; 自适应; 种群分割
Keywords:
flexible job-shop scheduling problem; immune genetic algorithm; mixed strategy; adaptive strategy; population decomposition
分类号:
TP18;TP301
DOI:
10. 3969 / j. issn. 1673-629X. 2020. 11. 032
摘要:
针对单目标柔性作业车间调度问题,以最小化最大完工时间为目标建立了生产调度模型,并在此模型的基础上设计了一种收敛速度和求解稳定性均较优的免疫遗传算法。 该算法采用三种方式相结合混合策略的种群初始化方式产生初始解,改善了初始种群的形成机制,提高了种群的初始质量和多样性。 在基于提高算法搜索能力的基础上提出了抗体浓度调节方式及根据抗体浓度正交自适应调节的交叉算子、变异算子的构造方法。 针对免疫遗传算法早熟收敛问题,利用种群分割的思想增加了其多样性,进一步提高了算法的收敛能力。 最后,使用 MATLAB 求解基准算例对算法的性能进行仿真测试,并给出了算例仿真的最优甘特图与收敛图,通过与其他算法的求解结果相比较,验证了该算法有效性和可行性。
Abstract:
Aiming at the single-objective flexible job-shop scheduling problem,the production scheduling model is established with the target of minimum completion time,on which an improved immune genetic algorithm with higher convergence and steadier equation solving ability is devised. This algorithm adopts population initialization with mixed strategy that integrated three methods generated the initial results,optimizing the forming mechanism of the initial population and improving its quality and diversity. The way of adjusting antibody concentration and the construction method of crossover operator and mutation operator that is adjusted by the adaptive orthogonality strategy of antibody concentration are put forward on the basis of improving the algorithm global search ability. For the problem of premature convergence of immune genetic algorithm, its diversity is increased by adopting the idea of population decomposition,so as to further improve the convergence ability of the algorithm. Finally,the performance of the algorithm is simulated by using MATLAB to solve the benchmark example,and the optimal Gantt chart and convergence chart of the simulation are given. Compared with other algorithms,it is verified that the proposed algorithm is effective and feasible.

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[1]李钦 余谅.基于免疫遗传算法的网格入侵检测模型[J].计算机技术与发展,2009,(05):162.
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[2]唐进岭 张著洪.多项目多任务选择动态规划及其智能决策[J].计算机技术与发展,2012,(09):75.
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[3]李雪花,高全力,赵 辉,等.求解柔性作业车间调度问题的混合遗传算法[J].计算机技术与发展,2022,32(08):185.[doi:10. 3969 / j. issn. 1673-629X. 2022. 08. 030]
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更新日期/Last Update: 2020-11-10