面向装配序列规划的进化算法研究
|
摘要
在产品生产过程中，产品装配所需工时占生产制造总工时的40%－60%，装配成本约占总生产成本的40%。合理的装配序列规划(Assembly Sequence Planning,ASP)对提高产品的质量、降低生产成本具有十分重要的意义。几何推理方法和以进化算法(Evolutionary Algorithm, EA)为代表的智能算法是求解ASP的典型方法。由于装配序列规划问题实际上是一个NP组合优化问题，传统的几何推理方法利用零部件间的几何配合信息计算推理出装配序列，将不可避免地遇到装配序列的组合爆炸问题。有序二叉决策图（Ordered Binary Decision Diagram, OBDD）是一种新型的图形数据结构，具有高紧凑性和易操作性特点，是减缓或者在部分程度上避免状态组合复杂性问题的有效技术。基于OBDD的装配序列生成的几何推理方法在一定程度上扩大了问题的求解规模，但仍无法克服ASP组合爆炸问题。进化算法是一种适合解大规模组合优化问题的有效近似算法，ASP用进化算法求解可有效克服组合爆炸问题，但当前求解ASP的进化算法仍难以有效利用装配经验知识。另外，这些进化算法运行时支撑的数据结构基本上还是数组类或集合类，导致算法需要较大存储空间和存有大量盲目搜索。针对以上问题，本文给出了多个求解ASP问题的多智能体进化算法和基于装配计算的进化算法，并把基于装配计算的进化算法推广到一类约束满足问题求解领域。具体研究内容如下:
1.为了克服传统进化算法（如模拟退火、遗传算法等）中个体难以有效利用装配经验知识的问题，给出了一种求解ASP的多智能体进化算法。设计了智能体的学习算子、协作算子、竞争算子和变异算子，构造了表示装配经验的公有知识和私有知识。该进化算法中的智能体可以有效利用装配体的几何，工艺等相关知识。智能体之间通过领域实现私有知识的分享。另外，还根据基于联接件ASP的特点，实现了基于联接件ASP求解的一种多智能体进化算法。仿真实验表明这两种进化算法与同类算法相比均具有较好性能。
2.多智能体进化算法对约束条件和装配经验的有效利用是通过知识来实现的，存在一定盲目性。在自装配计算的启发下，为了在进化算法中能有效抑制不满足基本约束条件的装配序列的生成，提出了基于装配计算求解ASP的进化算法，设计了新型进化算子-生长算子，生长算子直接利用装配计算的特征，使得个体在进化过程中遵守装配体的约束条件，从而减少进化算法搜索的盲目性。另外，结合多智能体学习和智能推理特征，本文还给出了基于装配计算求解ASP的多智能体进化算法。实验表明，这两种进化算法与同类算法相比有效提高了ASP的求解效率。
3.目前求解装配序列规划的进化算法的装配体模型主要是数组或矩阵型，而用OBDD等符号技术表达装配体模型则能节省存储空间，其隐式数据模型表示的特征还有利于搜索的隐含并行。为节省数据的存储空间，提高进化算法相关算子的搜索性能。本文设计了基于符号技术OBDD的进化算法，实现了随机重组、启发重组两种算子。其中启发重组具备了装配计算的特征，类似于生长算子。仿真实验表明，与类似的传统进化算法相比，该算法能有效节省数据的存储空间，同时发挥了符号技术的隐含并行搜索能力。
4.为了测试基于装配计算的进化算法可扩展性，深化装配计算进化算法的研究，在自装配计算刚体计算模型的启发下，设计和实现了求解TSP、n皇后等问题为代表的约束满足问题求解的基于装配计算的进化算法，设计了相应的自装配计算模型和进化规则，进化个体间的迭代不必以代同步，进化过程由装配计算模型的约束规则控制。仿真实验表明，基于装配计算的进化算法在求解此类问题时与同类算法相比具有较好的性能。
In traditional manufacturing，assembly tasks account for about50%of totalproduction time and more than20%of total manufacturing cost. It is an important factorthat determines the quality and cost of product assembly. Reasonable assemblysequence planning(ASP) is an important factor in improving product's quality andreducing production cost. Evolutionary algorithm (EA) as the representative of theintelligent algorithm and geometric reasoning method are two typical methods forsolving ASP problem. Because ASP is a NP problem, the geometric reasoning methodwill inevitably bring about combinatorial explosion problem. Ordered binary decisiondiagram (OBDD) which is a new type of graph data structure, has the advantages ofhigh compactness and ease of operation. It is an effective technical to slow or avoidstate complexity of combinatorial problem in part. Geometric reasoning method ofassembly sequence based on OBDD can solve larger ASP, but still unable to overcomethe combinatorial explosion problem. EA is a kind of efficient approximationalgorithms for solving large-scale combinatorial optimization problems, theevolutionary algorithms for solving ASP can overcome the combinatorial explosionproblem, but they are still difficult to effectively make use of the assembly experienceand knowledge. EA based on the data structure of an array or set needs much storagespace and has a lot of blind search. In order to overcome the shortages，several novelEAs are proposed for ASP in the dissertation. Similar EA is also designed for a kind ofconstraint satisfaction problem(CSP). The main research contents are as follows:
1. Typical evolution algorithm (such as simulated annealing，genetic algorithm andso on) is difficult to make use of individual assembly experience and assemblyknowledge. A multi-agent co-evolution algorithm is proposed for ASP, where learning，cooperative，competition，mutation operators of agent are designed and assemblyexperience of public knowledge and private knowledge are constructed. Agents caneffectively make use of the knowledge of assembly body and assembly experience.They share private knowledge through adjective field. In addition，according to thecharacteristics of ASP based on connector，a multi-agent evolutionary algorithm forASP based on connector is proposed. Simulation results show that two algorithms havebetter performance than other similar EAs.
2. Multi-agent evolutionary algorithm makes use of constraints and assemblyexperience through knowledge which is some blindness. Inspired by self-assemblycalculation, in order to generate assembly sequences which meet basic constraints, evolutionary algorithms are proposed based on the assembly calculation for ASP. Anew evolutionary operator-growth operator is designed in each individual. Growthbased on assembly calculation observes assembly constraints in the process of evolutionto reduce the blindness search. In addition，combined with multi-agent technology，thisdissertation proposed a multi-agent evolutionary algorithm for ASP based on assemblycalculation. Experiments show two algorithms have better performance than othersimilar EAs.
3. Most of the assembly model of ASP for EA is an array or matrix type. OBDDimplicit expression of assembly model can save storage space. Its data model alsofacilitates implicit parallel search. In order to save the data storage space and improvethe search performance of evolutionary algorithm, this dissertation proposed anevolutionary algorithm based on OBDD symbol technology. Two operators of randomrecombination and heuristic reorganization are realized and heuristic reorganization hasthe characteristics of assembly calculation. Compared with the traditional evolutionaryalgorithm，simulation results show the algorithm can effectively save the data storagespace and make use of a symbol technology hidden parallel search ability to improve itsperformance.
4. In order to test scalability of evolutionary algorithm based on assemblycalculation and make more research, inspired by the assembly calculation of titlecomputational model, a novel evolutionary algorithm is suggested for solving constraintsatisfaction problem by solving TSP and n-queen problems. Assembly calculationmodel and evolutionary rules are designed accordingly. The generation of individualsneed not synchronous. The process is controlled by the assembly model and controlrules. Simulation results show that the novel algorithm has better performance for CSPthan other similar EAs.
1.为了克服传统进化算法（如模拟退火、遗传算法等）中个体难以有效利用装配经验知识的问题，给出了一种求解ASP的多智能体进化算法。设计了智能体的学习算子、协作算子、竞争算子和变异算子，构造了表示装配经验的公有知识和私有知识。该进化算法中的智能体可以有效利用装配体的几何，工艺等相关知识。智能体之间通过领域实现私有知识的分享。另外，还根据基于联接件ASP的特点，实现了基于联接件ASP求解的一种多智能体进化算法。仿真实验表明这两种进化算法与同类算法相比均具有较好性能。
2.多智能体进化算法对约束条件和装配经验的有效利用是通过知识来实现的，存在一定盲目性。在自装配计算的启发下，为了在进化算法中能有效抑制不满足基本约束条件的装配序列的生成，提出了基于装配计算求解ASP的进化算法，设计了新型进化算子-生长算子，生长算子直接利用装配计算的特征，使得个体在进化过程中遵守装配体的约束条件，从而减少进化算法搜索的盲目性。另外，结合多智能体学习和智能推理特征，本文还给出了基于装配计算求解ASP的多智能体进化算法。实验表明，这两种进化算法与同类算法相比有效提高了ASP的求解效率。
3.目前求解装配序列规划的进化算法的装配体模型主要是数组或矩阵型，而用OBDD等符号技术表达装配体模型则能节省存储空间，其隐式数据模型表示的特征还有利于搜索的隐含并行。为节省数据的存储空间，提高进化算法相关算子的搜索性能。本文设计了基于符号技术OBDD的进化算法，实现了随机重组、启发重组两种算子。其中启发重组具备了装配计算的特征，类似于生长算子。仿真实验表明，与类似的传统进化算法相比，该算法能有效节省数据的存储空间，同时发挥了符号技术的隐含并行搜索能力。
4.为了测试基于装配计算的进化算法可扩展性，深化装配计算进化算法的研究，在自装配计算刚体计算模型的启发下，设计和实现了求解TSP、n皇后等问题为代表的约束满足问题求解的基于装配计算的进化算法，设计了相应的自装配计算模型和进化规则，进化个体间的迭代不必以代同步，进化过程由装配计算模型的约束规则控制。仿真实验表明，基于装配计算的进化算法在求解此类问题时与同类算法相比具有较好的性能。
In traditional manufacturing，assembly tasks account for about50%of totalproduction time and more than20%of total manufacturing cost. It is an important factorthat determines the quality and cost of product assembly. Reasonable assemblysequence planning(ASP) is an important factor in improving product's quality andreducing production cost. Evolutionary algorithm (EA) as the representative of theintelligent algorithm and geometric reasoning method are two typical methods forsolving ASP problem. Because ASP is a NP problem, the geometric reasoning methodwill inevitably bring about combinatorial explosion problem. Ordered binary decisiondiagram (OBDD) which is a new type of graph data structure, has the advantages ofhigh compactness and ease of operation. It is an effective technical to slow or avoidstate complexity of combinatorial problem in part. Geometric reasoning method ofassembly sequence based on OBDD can solve larger ASP, but still unable to overcomethe combinatorial explosion problem. EA is a kind of efficient approximationalgorithms for solving large-scale combinatorial optimization problems, theevolutionary algorithms for solving ASP can overcome the combinatorial explosionproblem, but they are still difficult to effectively make use of the assembly experienceand knowledge. EA based on the data structure of an array or set needs much storagespace and has a lot of blind search. In order to overcome the shortages，several novelEAs are proposed for ASP in the dissertation. Similar EA is also designed for a kind ofconstraint satisfaction problem(CSP). The main research contents are as follows:
1. Typical evolution algorithm (such as simulated annealing，genetic algorithm andso on) is difficult to make use of individual assembly experience and assemblyknowledge. A multi-agent co-evolution algorithm is proposed for ASP, where learning，cooperative，competition，mutation operators of agent are designed and assemblyexperience of public knowledge and private knowledge are constructed. Agents caneffectively make use of the knowledge of assembly body and assembly experience.They share private knowledge through adjective field. In addition，according to thecharacteristics of ASP based on connector，a multi-agent evolutionary algorithm forASP based on connector is proposed. Simulation results show that two algorithms havebetter performance than other similar EAs.
2. Multi-agent evolutionary algorithm makes use of constraints and assemblyexperience through knowledge which is some blindness. Inspired by self-assemblycalculation, in order to generate assembly sequences which meet basic constraints, evolutionary algorithms are proposed based on the assembly calculation for ASP. Anew evolutionary operator-growth operator is designed in each individual. Growthbased on assembly calculation observes assembly constraints in the process of evolutionto reduce the blindness search. In addition，combined with multi-agent technology，thisdissertation proposed a multi-agent evolutionary algorithm for ASP based on assemblycalculation. Experiments show two algorithms have better performance than othersimilar EAs.
3. Most of the assembly model of ASP for EA is an array or matrix type. OBDDimplicit expression of assembly model can save storage space. Its data model alsofacilitates implicit parallel search. In order to save the data storage space and improvethe search performance of evolutionary algorithm, this dissertation proposed anevolutionary algorithm based on OBDD symbol technology. Two operators of randomrecombination and heuristic reorganization are realized and heuristic reorganization hasthe characteristics of assembly calculation. Compared with the traditional evolutionaryalgorithm，simulation results show the algorithm can effectively save the data storagespace and make use of a symbol technology hidden parallel search ability to improve itsperformance.
4. In order to test scalability of evolutionary algorithm based on assemblycalculation and make more research, inspired by the assembly calculation of titlecomputational model, a novel evolutionary algorithm is suggested for solving constraintsatisfaction problem by solving TSP and n-queen problems. Assembly calculationmodel and evolutionary rules are designed accordingly. The generation of individualsneed not synchronous. The process is controlled by the assembly model and controlrules. Simulation results show that the novel algorithm has better performance for CSPthan other similar EAs.
引文
1. Q. Su. Computer aided geometric feasible assembly sequence planning andoptimizing. The International Journal of Advanced Manufacturing Technology,2007,33:48-57.
2.王峻峰,李世其,刘继红,钟毅芳.计算机辅助装配规划研究综述.工程图学学报,2005(2):1-7.
3.李荣.基于知识的装配序列规划关键技术研究.博士学位论文.哈尔滨工业大学,2009.
4. V. N. Rajan, S. Y. Nof. Minimal precedence constraints for integrated assembly andexecution planning. IEEE Transactions on Robot Automat,1996,12(2):175-186.
5.宋天虎.先进制造技术的发展与未来.中国机械工程,1998(4):9-13.
6.李宗斌.先进制造中多色集合理论的研究及应用.北京:中国水利水电出版社,2005.
7.徐周波,古天龙.装配序列规划问题的CSP模型及其符号OBDD求解技术.计算机辅助设计与图形学学报,2010,22(5):803-810.
8.王艳玮.计算机辅助装配序列规划关键技术研究.西北工业大学博士学位论文,1999.
9.范菁.虚拟环境下的交互式只能装配规划.浙江大学博士学位论文,2002．
10.宁黎华,古天龙.基于免疫算法的装配序列规划问题求解.计算机集成制造系统,2007,13(1):81-87.
11. H. E. Tseng. Guided genetic algorithms for solving larger constraint assemblyproblem. International Journal Production. Research,2006,44(3):601-625.
12. H. E. Tseng, W. P. Wang and H. Y. Shih. Using memetic algorithms with guidedlocal search to solve assembly sequence planning. Expert Systems withApplications,2007,33(2):451-467.
13. C. C. Chang, H. E. Tseng, L. P. Meng. Artificial immune systems for assemblysequence planning exploration. Engineering Applications of Artificial Intelligence,2009,22:1218-1232.
14.姚新,刘勇.进化算法研究进展.计算机学报,1995,18（9）:694-706.
15. W. C. Zhong, L. J., X. M. Zhi, L. C. Jiao. A multi-Agent genetic algorithm forglobal numerical optimization. IEEE Transactions on System, Man, andCybernetics-Part B,2004,34(2):1128-1141.
16.潘晓英,焦李成.项目优化调度的多智能体社会进化算法.计算机研究与发展,2008,45(6):998-1003.
17.公茂果，焦李成，杨咚咚，马文萍.进化多目标优化算法研究.计算机学报,2009,20(2):271-289.
18. Q. F. Zhang，A. M. Zhou，Y. R. Jin／Vl-MEDA：A regularicy model basedmultiobjective estimation of distribution algorithm. IEEE Trans．on EvolutionaryComputation，2007，12(1)：41-63.
19. Abdelbar, A. M. Wunsch II, Donald C. Improving the performance of MAX-MINant system on the TSP using stubborn ants. Proceedings of the14th InternationalConference on Genetic and Evolutionary Computation Companion,2012:1395-1396.
20. C. W. Zeng, T. L. Gu. A novel assembly evolutionary algorithm for n-queensproblem. International Conference on Computational Intelligence and SecurityWorkshops,2007:171-174.
21. R. J. Meuth, D. C. Wunsch.Divide and conquer evolutionary TSP solution forvehicle path planning, IEEE Congress on Evolutionary Computation (CEC),2008:676-81.
22.杨志飞,古天龙.基于OBDD的有向图的存储与操作研究.计算机科学,2007,34（8）:283-285.
23.孙自广,古天龙,张增芳.基于OBDD的灰度图像无损压缩算法.系统仿真学报,2007,28(23):5674-5676.
24.徐周波,古天龙,赵岭忠.网络最大流问题的一种新的符号ADD求解算法.通信学报,2005,26(2):1-8.
25.古天龙,李风英.一类Petri网调度问题的符号求解技术.系统仿真学报,2005（17）:148-154.
26. D. E. Homen, L. S. Mello, A. C. Sanderson. A correct and complete algorithm forthe genertation of mechanical assembly sequences. IEEE Transactions on Roboticsand Automation,1991,7:228-240.
27.李磊.数字化产品预装配序列生成、评价与优化研究.西北工业大学博士学位论文,2002.
28. k. Lee, D. C. Gossad. A hierar cllialsl data structure for representing assemblies.Computer-Aided Design,1985,17(1):8-12.
29. W. Zhang. Representation of assembly and automatic robot planning by petri net.IEEE Transactions on Systems, Man, and Cybcrmctics,1989(19):418-422.
30.刘平,李原,张开富.复杂产品装配的分层有向图建模方法.机械设计,2007,24(4):30-32.
31.季忠齐,童若锋,林兰芬,蔡铭,董金祥.基于图论和启发式搜索的装配序列规划算法.计算机工程,2003.8(29):115-117.
32.张博,张洪涛,赵珊珊,李宗斌.基于多色集合理论的产品装配规划建模与算法研究.西安交通大学学报,2005,39(11):1254-1258.
33.南风强,张友良,汪惠芬,王孝义.面向精度的数字化产品装配模型研究及应用.中国机械工程,2007.18(5):1071-1075.
34. F. Bonneville, C. Perrard. A Genetic algorithm to Generate and EvaluateAssemblyplans. IEEE Symposium on Emerging Technologies and FactoryAutomation,1995(2):231-239.
35.夏平均,姚英学,刘江省,李建广.基于虚拟现实和仿生算法的装配序列优化.机械工程学报,2007,43(4):44-52.
36.王辉,向东,段广洪.基于蚁群算法的产品拆卸序列规划研究.计算机集成制造系统,2006.9(9):1431-1437.
37.周开俊,李东波,黄希.基于遗传算法的装配序列规划研究.机械设计,2006,23(2):30-33.
38.苏强,赖盛杰.基于事例推理的装配顺序规划中的遗传算法改进设计.上海交通大学学报,2007,41(8):1233-1243.
39.王敏,叶碧莲.基于粒子群算法的装配规划研究.军民两用技术与产品,2008,1(1):44-45.
40.董天阳等.多细节层次装配模型及序列规划.计算机集成制造系统,2004.10(10):1212-1219.
41.顾寄南,张林筵,肖田元等.基于推理的有向图拓扑排序装配顺序规划及优化研究.机械工程学报,2002.12(38):142-145.
42.郑太雄,伍光风.基于模糊神经网络产品设计的可装配性评价.机械设计,2005,22(1):39-41.
43.朱海平,邵新宇,张国军.基于自动拆卸过程的装配序列生成方法研究.工程图学学报,2008(4):27-32.
44.付宜利,田立中,储林波.基于模糊评价的装配序列生成.哈尔滨工业大学学报,2002.34(12):739-742.
45. J. K. Koza. Genetic programming. MIT Press, Cambridge, MA,1992.
46. F. F. Easton, N. Mansour. A distributed genetic algorithm for deterministic andstochastic labor scheduling problems. European Journal of Operational Research,1999.118(3):505-523.
47.王帅强,马军,王海洋.基于遗传规划的行为模型精化方法.计算机研究与发展,2008,45(11):1911-1919.
48. W. B. Langdon, Riccardo Poli. Foundations of genetic programming. Springer,2010.
49. D. B. Fogel. An introduction to simulated evolutionary optimization. IEEETransactions on Neural Networks,1994,5(4):3-14.
50. Zbigniew, Z. Michalewicz. Genetic algorithms+data structure=evolution programs.Berlin Springer-Verlag.1992
51. S. B. Akers. Binary decision diagrams. IEEE Transaction on Computer,1978,27(6):509-516.
52. R. E. Bryant. Graph-based algorithms for boolean function manipulation. IEEETransactions on Computers,1986,35(8):677-691.
53. R. E. Bryant. Symbolic bolean manipulation with ordered binary decision diagrams.ACM Computing Surveys,1992.24(3):293-318.
54. K. L. McMillan. Symolic model checking, an approach to avoid state explosion.NewYork: Ph D Cameigie Mellon University,1992.
55. E. M. Clarke. O. Grumberg, D.E. Long. Model checking and abstraction. NewYork:In Proceedings of the19th ACM Symposium on Principles of ProgrammingLanguages,199230-33.
56. J. R. Burch, E. M. Clarke, D.E. Long, et al. Symbolic model checking forsequential circuit verification. IEEE Transactions on Computer-Aided Design ofIntegrated Circuits and Systerns,1994.13(4):401-424.
57. G. Q. Pan, U. Sattler, M.Y. Vardi. BDD-based decision procedures for the modallogic K. Journal of Applied NonClassical Logics,2006,16(1): l69-208.
58. S. Rudolph, M. Krotzsch, P. Hitzler. Terminological reasoning in shiq with orderedbinary decision diagrams: In: Proceedings of the23rd AAAI Conference onArtficial Intelligence: AAAI Press,2008:529-534.
59. A. Cimatti, M. Roveri, P. Bertoli. Conformant planning via symbolic modelchecking and heuristic search. Artificial Intelligence,2004(159):127-206.
60. L. M. Adleman. Molecular computation of solutions to combinatorial problem.Science,1994(266):1021-1024.
61. D. S. Hong, H. S. Cho. Generation of robotic assembly sequences using asimulated annealing. Proceedings of the IEEE/RSJ International conference onIntelligent Robotic and Systems. Berne: IEEE Press,1999:1247-1252.
62. F. Failli, G. Dini. Ant colony systems in assembly planning: a new approach tosequence detection and optimization. Proceedings of the2nd CIRP IntelligentComputation in Manufacturing Engineering. Capri: CIRP Press,2000:227-232.
63.丁建立,陈增强,袁著祉.遗传算法与蚂蚁算法的融合.计算机研究与发展,2003,40(9):1351-1356.
64.熊志辉,李思昆,陈吉华.遗传算法与蚂蚁算法动态融合的软硬件划分.软件学报,2005,14(4):503-512.
65.李智,许川佩,莫玮.基于蚂蚁算法和遗传算法的同步时序电路初始化.电子学报,2003,31(8):1276-1280.
66.匡兵,黄美发,钟艳如,孙永厚.一种基于遗传算法的装配序列优化方法.现代制造工艺,2005(12):69-71.
67.徐周波,古天龙,常亮.加权约束满足问题的符号ADD求解算法.模式识别与人工智能,2011(1):14-21.
68.吴耀.数字化装配信息建模及信息提取技术研究.南京理工大学硕士学位论文,
2006.
69.付宜利,田立中,谢龙,马玉林.基于有向割集分解的装配序列生成方法.机械工程学报,2003(6):58-62.
70.蔡铭,童若锋,董天阳,董金祥.基于规则和爆炸图的装配序列规划.计算机辅助设计与图形学学报,2004（8）:1106-1113.
71.杨志飞,古天龙,钟艳如.基于OBDD的装配序列自动推理技术研究.桂林电子工业学院学报,2005,25(1):43-47.
72.李凤英,古天龙,侯杰.基于赋时Petri网的装配序列规划符号求解.桂林电子工业大学学报,2010,30(2).
73.张景霞,王润孝.种基于子装配体识别的装配顺序生成.机械设计与制造,2004(6):88-89.
74. J. H. Holland. Adaptation in natural and artificial systems. Boston: MIT Press,
1992.
75. G. Rudolph. Convergence properties of canonical genetic algorithms. IEEETransactions on Neural Networks,1994,5(1):96-101.
76. D. E. Goldberg. Segrest P. Finite markov chain analysis of genetic algorithm. inProc of the Second Int. Conf. on Genetic A1gorithms,1987:1-8.
77. D. E. Goldberg. Genetic Algorithms: In search optimization&machine learning.MA: Addison Wesley,1989.
78.陈国良,王煦法,庄镇泉.遗传算法及其应用.北京:人民邮电出版社,1996.
79.徐周波.约束满足问题的符号算法及其在装配规划中的应用研究.西安电子科技大学博士学位论文,2011,12
80. T. L. Gu, Z. B. Xu, Z. F. Yang. Symbolic obdd representations for mechanicalassembly sequences. Computer Aided Design,2008,40(4):411-421
81. R. B. Gottipolu, K. Ghosh, An integrated approach to the generation of assemblysequences. International Journal of Computer Application in Technology,1995,8(3-4):125-138
82. R. B. Gottipolu, K. Ghosh, A simplified and efficient representation for evaluationand selection of assembly sequences. Computer in Industry,2003,50(3):251-264
83. M. A. Wesley, T. Lozano Perez, L. I. Lieberman, et al. Geometric modeling systemfor automated mechanical assembly. IBM Journal of Research Development,1980,24(1):64
84. K. Mori, M. Tsukiyama, T. Fukuda. Application of an immune algorithm tomulti-optimization problems. Electrical Engineering in Japan,1998,122(2):30-37.
85. X. Y. Pan, L.C. Jiao. A multi-agent social evolutionary algorithm for projectoptimization scheduling. Journal of Computer Research and Development,2008,45(6):998-1003.
86.钟伟才.多智能体进化模型和算法研究.西安电子科技大学博士学位论文,2004,9
87.曾聪文,古天龙.求解装配序列规划的一种多智能体进化算法.计算机集成制造系统.2009(09):1803-1804.
88. C. W. Zeng, T. L. Gu, Y. R. Zhong, G.Y. Cai. A multi-agent evolutionary algorithmfor connector-based assembly sequence planning. Procedia Engineering.201115(7):3689-3693
89. Z. P. Yin, H. Ding, H. X. Li, YouLun Xiong. A connector-based hierarchicalapproach to assembly sequence planning for mechanicalassemblies.Computer-Aided Design,2003,35:37-56.
90. T. Head. Formal language theory and DNA: an analysis of the generative capacityof recombinant behaviors. Bull MathBiol,1987(49):737-759.
91. R.W. Gatterdam. DNA and twist free splicing systems, in words, languages andcombinatorics. Singapore: World Scientific Publishers,1994,170-178.
92. D. Boneh, R. Lipton. On the computational power of DNA.Discrete Appl Math,1996(71):79-94.
93. D. K. Gifferd. On the path to computation with DNA. Science,1994(266):993-994.
94. R. Lipton. DNA solution of hard computational problems.Science,1995(68):49-66.
95. T. Leete, M. Schwartz, R. Williams, et al. Massively parallel DNA computation:expansion of symbolic determinants. In: Proceedings of the2nd DIMACSWorkshop on DNA-Based Computers,1996:49-66.
96. N. Jonoska, S. Karl. A molecular computation of the road coloring problem.InProceedings of the2nd DIMACS Workshop on DNA-Based Computers,1996:148-
158.
97. J. Oliver. Computation with DNA: matrix multiplication. In Proceedings of the2ndDIMACS Workshop on DNA-Based Computers.1996.236-248.
98. F. Guarnieri, C. Bancroft. Use of horizontal chain reaction for DNA-based addition.In Proceedings of the2nd DIMACS Workshop on DNA-Based Computers,1996:249-259.
99. R. Williams, H. Wood. Exascale computer algebra problems interconnect withmolecular reactions and complexity theory. In Proceedings of the2nd DIMACSWorkshop on DNA-Based Computers,1996:260-268.
100.A. Maranthe, A. E. Condon, Corn RM. On correction in DNA computing. In theProceedings of the6th DIMACS meeting on DNA Based Computers, Univ ofLeiden,2000:13-17.
101.K. L. Deninnghoff, R. W. Gat terdam. On the undecidability of splicing systems.Inthe Computer Math,1989,27:133-145.
102.G. Paun, G. Rozenberg, A. Salomaa. DNA Computing.Springer,1998.
103.L. E. Tony. Linear DNA self-assembly with hairpins linear context-free grammars.Dima Cs Series In Discrete Mathematics And Theoretical Computer Science,1999,48:289-296.
104.J. Reif. Parallel biomolecular computation: models and simutations. In7th AnnualACM Symposium on Parallel Algorithms and Architectures,1995:213-223.
105.J. Reif. Local parallel biomolecular computation. In the Proceedings of the3rdDIMACS meeting on DNA Based Computers, Univ of Penns,1997:23-26.
106.M. Amos. Theoretical and experimental DNA computation. In the Bulletin ofEATCS67,1999:125-138.
107.K. Lila. DNA computing: Arrival of Biological Mathematics.The MathematicalIntelligencer,1997,19:9-22.
108.M. Amos, P.E. Dunne, A. Gibbons. DNA simulation of boolean circuits.Proceedings of the Third Annual Conference,1998.679-683.
109.E. Baum. DNA sequences useful for computation. in Proceedings of the2ndDIMACS Workshop on DNA-based Computers,1996:122-127.
110.M. Amos, A. Gibbons, D. Hodgson. Error-resistant implementation of DNAcomputation.in Proceedings of the2nd DIMACS Workshop on DNA-basedComputers,1996,87-101.
111.Q. H. Liu, W. Liman, A. Frutos, et al. DNA Computing on Surfaces.Nature,2000,403:175-179.
112.D. Faulhammer, D. Cukras, R.J. Lipton, et al. Molecular computation: DNAsolutions to chess problems. Proc Natl A cad Sci US,2000,97:1385-1389.
113.W. Zhou, J. R. Zheng, J. J. Yan, J. F. Wang. A novel hybrid algorithm for assemblysequence planning combining bacterial chemotaxis with genetic algorithm. TheInternational Journal of Advanced Manufacturing Technology,2011,52:715-724.
114.Z. X. Yin, B. Song. DNA self-assembly model for matrix additionproblem. Journalof Computers,2011,6(4):698-704.
115.E. Winfree. Algorithmic self-assembly of DNA. PhD thesis. California Institute ofTechnology, Computation and Neural Systems Option,1998.
116.J. F. Wang, J. H. Liu. A novel ant colony algorithm for assembly sequence planning.International Journal of Advanced Manufacturing Technology,2005,25(11):1137-1143.
117.J. M. Liu, J. Han, Y. Y. Tang. Multi-agent oriented constraint satisfaction. ArtificialIntelligence,2002,136(1):101-144.
118.J. Liu, W. C. Zhong, and L. C. Jiao.A multiagent evolutionary algorithm forconstraint satisfaction problems. IEEE Transactions On Systems, Man, AndCybernetics-Part B: Cybernetics,2006,36(1):54-73.
119.K. Deb，H. G. Beyer．Self-Adaptive genetic algorithms with simulated binarycrossover．Evolutionary Computation，2001，9(2): l97-221.
120.古天龙,徐周波.有序二叉决策图及应用.科学出版社,2009.
121.T. L. Gu, H. d. Liu. The symbolic OBDD scheme for generating mechanicalassembly sequences Form Methods, Syst Des,2008（33）:29-44.
122.A. Eiben, P. E. Raue, Z. Ruttkay. Solving constraint satisfaction problems usinggenetic algorithms. In Proceedings of IEEE World Confernece on ComputationalIntelligence,1st IEEE Conference on Evolutionary Computation,1994:543-547.
123.R. Sosic, J. Gu. Efficient local search with conflict minimization: A case study ofthe N-queen problem. IEEE Transactions on Knowledge and Data Engineering,1994,6(5):661-668.
124.M. Boiikovic, M. Golub, L. Budin. Solving n-Queen problem using global parallelgenetic algorithm. Eurocon Ljubljana, Slovenia,2003:104-107.
125.孟大志,曹海萍. DNA计算与生物数学.生物物理学报,2002,18（6）:163-173.
126.D. A. Huffman. Impossible objects as nonsense sentences. Edinbrugh UniversityPress, Machine Intelligence6, Edinburgh: Meltzer B, Michie D,1971:295-323.
127.M. B. Clowes. On seeing things. Artificial Intelligence,1971,2(1):79-116.
128.D. L. Waltz. Generating semantic descriptions from drawings of scenes withshadows. Technical Report AI-TR-271, Massachusetts Institute of Technology,Cambridge, MA,1972.
129.I. Miguel, Q. Shen. Solution techniques for constraint satisfaction problems:foundations. Artificial Intelligence Review,2001,15(4):243-267.
130.R. Dechter, J. Pearl. Tree clustering for constraint networks. Artificial Intelligence,1989,38(3):353-366.
131.N. Sadeh, K. Sycara, Y. Xiong. Bachtracking techniques for the job shop schedulingconstraint satisfaction problem. Artificial Intelligence,1995,76(1-2):455-480.
132.D. N. Pham, J. Thornton, A. Sattar. Modelling and solving temporal reasoning aspropositional satisfiability. Artificial Intelligence,2008,172(15):1725-1782.
133.伍丽华,陈蔼洋,姜云飞.规划问题编码为约束可满足问题的研究.计算机科学,2006,33(8):187-292.
134.B. Cabon, D. Givry, L. Lobjois, et al. Radio link frequency assignment. Constraints,1999,4(1):79-89.
135.TSP Library. http://www.iwr.uni-heidelberg.de/iwr.compot/software,2005.
136.L. V. Kale. An almost perfect heuristic for the n nonattacking queen problem.Inform. Process. Lett.,1990,34:173-178.
137.M. Boiikovic, M. Golub, L. Budin. Solving n-Queen problem using global parallelgenetic algorithm. EUROCON Ljubljana, Slovenia,2003:104-107.
138.C. W. Zeng, Tianlong Gu. A novel assembly evolutionary algorithm for n-queensproblem. International Conference on Computational Intelligence And SecurityWorkshops,2007:171-174.
139.E. David, Goldberg. Generic algorithms in search, optimization and machinelearning. Addison-Werlcy Publishing Comwny Inc., Reading.MA,1989.
2.王峻峰,李世其,刘继红,钟毅芳.计算机辅助装配规划研究综述.工程图学学报,2005(2):1-7.
3.李荣.基于知识的装配序列规划关键技术研究.博士学位论文.哈尔滨工业大学,2009.
4. V. N. Rajan, S. Y. Nof. Minimal precedence constraints for integrated assembly andexecution planning. IEEE Transactions on Robot Automat,1996,12(2):175-186.
5.宋天虎.先进制造技术的发展与未来.中国机械工程,1998(4):9-13.
6.李宗斌.先进制造中多色集合理论的研究及应用.北京:中国水利水电出版社,2005.
7.徐周波,古天龙.装配序列规划问题的CSP模型及其符号OBDD求解技术.计算机辅助设计与图形学学报,2010,22(5):803-810.
8.王艳玮.计算机辅助装配序列规划关键技术研究.西北工业大学博士学位论文,1999.
9.范菁.虚拟环境下的交互式只能装配规划.浙江大学博士学位论文,2002．
10.宁黎华,古天龙.基于免疫算法的装配序列规划问题求解.计算机集成制造系统,2007,13(1):81-87.
11. H. E. Tseng. Guided genetic algorithms for solving larger constraint assemblyproblem. International Journal Production. Research,2006,44(3):601-625.
12. H. E. Tseng, W. P. Wang and H. Y. Shih. Using memetic algorithms with guidedlocal search to solve assembly sequence planning. Expert Systems withApplications,2007,33(2):451-467.
13. C. C. Chang, H. E. Tseng, L. P. Meng. Artificial immune systems for assemblysequence planning exploration. Engineering Applications of Artificial Intelligence,2009,22:1218-1232.
14.姚新,刘勇.进化算法研究进展.计算机学报,1995,18（9）:694-706.
15. W. C. Zhong, L. J., X. M. Zhi, L. C. Jiao. A multi-Agent genetic algorithm forglobal numerical optimization. IEEE Transactions on System, Man, andCybernetics-Part B,2004,34(2):1128-1141.
16.潘晓英,焦李成.项目优化调度的多智能体社会进化算法.计算机研究与发展,2008,45(6):998-1003.
17.公茂果，焦李成，杨咚咚，马文萍.进化多目标优化算法研究.计算机学报,2009,20(2):271-289.
18. Q. F. Zhang，A. M. Zhou，Y. R. Jin／Vl-MEDA：A regularicy model basedmultiobjective estimation of distribution algorithm. IEEE Trans．on EvolutionaryComputation，2007，12(1)：41-63.
19. Abdelbar, A. M. Wunsch II, Donald C. Improving the performance of MAX-MINant system on the TSP using stubborn ants. Proceedings of the14th InternationalConference on Genetic and Evolutionary Computation Companion,2012:1395-1396.
20. C. W. Zeng, T. L. Gu. A novel assembly evolutionary algorithm for n-queensproblem. International Conference on Computational Intelligence and SecurityWorkshops,2007:171-174.
21. R. J. Meuth, D. C. Wunsch.Divide and conquer evolutionary TSP solution forvehicle path planning, IEEE Congress on Evolutionary Computation (CEC),2008:676-81.
22.杨志飞,古天龙.基于OBDD的有向图的存储与操作研究.计算机科学,2007,34（8）:283-285.
23.孙自广,古天龙,张增芳.基于OBDD的灰度图像无损压缩算法.系统仿真学报,2007,28(23):5674-5676.
24.徐周波,古天龙,赵岭忠.网络最大流问题的一种新的符号ADD求解算法.通信学报,2005,26(2):1-8.
25.古天龙,李风英.一类Petri网调度问题的符号求解技术.系统仿真学报,2005（17）:148-154.
26. D. E. Homen, L. S. Mello, A. C. Sanderson. A correct and complete algorithm forthe genertation of mechanical assembly sequences. IEEE Transactions on Roboticsand Automation,1991,7:228-240.
27.李磊.数字化产品预装配序列生成、评价与优化研究.西北工业大学博士学位论文,2002.
28. k. Lee, D. C. Gossad. A hierar cllialsl data structure for representing assemblies.Computer-Aided Design,1985,17(1):8-12.
29. W. Zhang. Representation of assembly and automatic robot planning by petri net.IEEE Transactions on Systems, Man, and Cybcrmctics,1989(19):418-422.
30.刘平,李原,张开富.复杂产品装配的分层有向图建模方法.机械设计,2007,24(4):30-32.
31.季忠齐,童若锋,林兰芬,蔡铭,董金祥.基于图论和启发式搜索的装配序列规划算法.计算机工程,2003.8(29):115-117.
32.张博,张洪涛,赵珊珊,李宗斌.基于多色集合理论的产品装配规划建模与算法研究.西安交通大学学报,2005,39(11):1254-1258.
33.南风强,张友良,汪惠芬,王孝义.面向精度的数字化产品装配模型研究及应用.中国机械工程,2007.18(5):1071-1075.
34. F. Bonneville, C. Perrard. A Genetic algorithm to Generate and EvaluateAssemblyplans. IEEE Symposium on Emerging Technologies and FactoryAutomation,1995(2):231-239.
35.夏平均,姚英学,刘江省,李建广.基于虚拟现实和仿生算法的装配序列优化.机械工程学报,2007,43(4):44-52.
36.王辉,向东,段广洪.基于蚁群算法的产品拆卸序列规划研究.计算机集成制造系统,2006.9(9):1431-1437.
37.周开俊,李东波,黄希.基于遗传算法的装配序列规划研究.机械设计,2006,23(2):30-33.
38.苏强,赖盛杰.基于事例推理的装配顺序规划中的遗传算法改进设计.上海交通大学学报,2007,41(8):1233-1243.
39.王敏,叶碧莲.基于粒子群算法的装配规划研究.军民两用技术与产品,2008,1(1):44-45.
40.董天阳等.多细节层次装配模型及序列规划.计算机集成制造系统,2004.10(10):1212-1219.
41.顾寄南,张林筵,肖田元等.基于推理的有向图拓扑排序装配顺序规划及优化研究.机械工程学报,2002.12(38):142-145.
42.郑太雄,伍光风.基于模糊神经网络产品设计的可装配性评价.机械设计,2005,22(1):39-41.
43.朱海平,邵新宇,张国军.基于自动拆卸过程的装配序列生成方法研究.工程图学学报,2008(4):27-32.
44.付宜利,田立中,储林波.基于模糊评价的装配序列生成.哈尔滨工业大学学报,2002.34(12):739-742.
45. J. K. Koza. Genetic programming. MIT Press, Cambridge, MA,1992.
46. F. F. Easton, N. Mansour. A distributed genetic algorithm for deterministic andstochastic labor scheduling problems. European Journal of Operational Research,1999.118(3):505-523.
47.王帅强,马军,王海洋.基于遗传规划的行为模型精化方法.计算机研究与发展,2008,45(11):1911-1919.
48. W. B. Langdon, Riccardo Poli. Foundations of genetic programming. Springer,2010.
49. D. B. Fogel. An introduction to simulated evolutionary optimization. IEEETransactions on Neural Networks,1994,5(4):3-14.
50. Zbigniew, Z. Michalewicz. Genetic algorithms+data structure=evolution programs.Berlin Springer-Verlag.1992
51. S. B. Akers. Binary decision diagrams. IEEE Transaction on Computer,1978,27(6):509-516.
52. R. E. Bryant. Graph-based algorithms for boolean function manipulation. IEEETransactions on Computers,1986,35(8):677-691.
53. R. E. Bryant. Symbolic bolean manipulation with ordered binary decision diagrams.ACM Computing Surveys,1992.24(3):293-318.
54. K. L. McMillan. Symolic model checking, an approach to avoid state explosion.NewYork: Ph D Cameigie Mellon University,1992.
55. E. M. Clarke. O. Grumberg, D.E. Long. Model checking and abstraction. NewYork:In Proceedings of the19th ACM Symposium on Principles of ProgrammingLanguages,199230-33.
56. J. R. Burch, E. M. Clarke, D.E. Long, et al. Symbolic model checking forsequential circuit verification. IEEE Transactions on Computer-Aided Design ofIntegrated Circuits and Systerns,1994.13(4):401-424.
57. G. Q. Pan, U. Sattler, M.Y. Vardi. BDD-based decision procedures for the modallogic K. Journal of Applied NonClassical Logics,2006,16(1): l69-208.
58. S. Rudolph, M. Krotzsch, P. Hitzler. Terminological reasoning in shiq with orderedbinary decision diagrams: In: Proceedings of the23rd AAAI Conference onArtficial Intelligence: AAAI Press,2008:529-534.
59. A. Cimatti, M. Roveri, P. Bertoli. Conformant planning via symbolic modelchecking and heuristic search. Artificial Intelligence,2004(159):127-206.
60. L. M. Adleman. Molecular computation of solutions to combinatorial problem.Science,1994(266):1021-1024.
61. D. S. Hong, H. S. Cho. Generation of robotic assembly sequences using asimulated annealing. Proceedings of the IEEE/RSJ International conference onIntelligent Robotic and Systems. Berne: IEEE Press,1999:1247-1252.
62. F. Failli, G. Dini. Ant colony systems in assembly planning: a new approach tosequence detection and optimization. Proceedings of the2nd CIRP IntelligentComputation in Manufacturing Engineering. Capri: CIRP Press,2000:227-232.
63.丁建立,陈增强,袁著祉.遗传算法与蚂蚁算法的融合.计算机研究与发展,2003,40(9):1351-1356.
64.熊志辉,李思昆,陈吉华.遗传算法与蚂蚁算法动态融合的软硬件划分.软件学报,2005,14(4):503-512.
65.李智,许川佩,莫玮.基于蚂蚁算法和遗传算法的同步时序电路初始化.电子学报,2003,31(8):1276-1280.
66.匡兵,黄美发,钟艳如,孙永厚.一种基于遗传算法的装配序列优化方法.现代制造工艺,2005(12):69-71.
67.徐周波,古天龙,常亮.加权约束满足问题的符号ADD求解算法.模式识别与人工智能,2011(1):14-21.
68.吴耀.数字化装配信息建模及信息提取技术研究.南京理工大学硕士学位论文,
2006.
69.付宜利,田立中,谢龙,马玉林.基于有向割集分解的装配序列生成方法.机械工程学报,2003(6):58-62.
70.蔡铭,童若锋,董天阳,董金祥.基于规则和爆炸图的装配序列规划.计算机辅助设计与图形学学报,2004（8）:1106-1113.
71.杨志飞,古天龙,钟艳如.基于OBDD的装配序列自动推理技术研究.桂林电子工业学院学报,2005,25(1):43-47.
72.李凤英,古天龙,侯杰.基于赋时Petri网的装配序列规划符号求解.桂林电子工业大学学报,2010,30(2).
73.张景霞,王润孝.种基于子装配体识别的装配顺序生成.机械设计与制造,2004(6):88-89.
74. J. H. Holland. Adaptation in natural and artificial systems. Boston: MIT Press,
1992.
75. G. Rudolph. Convergence properties of canonical genetic algorithms. IEEETransactions on Neural Networks,1994,5(1):96-101.
76. D. E. Goldberg. Segrest P. Finite markov chain analysis of genetic algorithm. inProc of the Second Int. Conf. on Genetic A1gorithms,1987:1-8.
77. D. E. Goldberg. Genetic Algorithms: In search optimization&machine learning.MA: Addison Wesley,1989.
78.陈国良,王煦法,庄镇泉.遗传算法及其应用.北京:人民邮电出版社,1996.
79.徐周波.约束满足问题的符号算法及其在装配规划中的应用研究.西安电子科技大学博士学位论文,2011,12
80. T. L. Gu, Z. B. Xu, Z. F. Yang. Symbolic obdd representations for mechanicalassembly sequences. Computer Aided Design,2008,40(4):411-421
81. R. B. Gottipolu, K. Ghosh, An integrated approach to the generation of assemblysequences. International Journal of Computer Application in Technology,1995,8(3-4):125-138
82. R. B. Gottipolu, K. Ghosh, A simplified and efficient representation for evaluationand selection of assembly sequences. Computer in Industry,2003,50(3):251-264
83. M. A. Wesley, T. Lozano Perez, L. I. Lieberman, et al. Geometric modeling systemfor automated mechanical assembly. IBM Journal of Research Development,1980,24(1):64
84. K. Mori, M. Tsukiyama, T. Fukuda. Application of an immune algorithm tomulti-optimization problems. Electrical Engineering in Japan,1998,122(2):30-37.
85. X. Y. Pan, L.C. Jiao. A multi-agent social evolutionary algorithm for projectoptimization scheduling. Journal of Computer Research and Development,2008,45(6):998-1003.
86.钟伟才.多智能体进化模型和算法研究.西安电子科技大学博士学位论文,2004,9
87.曾聪文,古天龙.求解装配序列规划的一种多智能体进化算法.计算机集成制造系统.2009(09):1803-1804.
88. C. W. Zeng, T. L. Gu, Y. R. Zhong, G.Y. Cai. A multi-agent evolutionary algorithmfor connector-based assembly sequence planning. Procedia Engineering.201115(7):3689-3693
89. Z. P. Yin, H. Ding, H. X. Li, YouLun Xiong. A connector-based hierarchicalapproach to assembly sequence planning for mechanicalassemblies.Computer-Aided Design,2003,35:37-56.
90. T. Head. Formal language theory and DNA: an analysis of the generative capacityof recombinant behaviors. Bull MathBiol,1987(49):737-759.
91. R.W. Gatterdam. DNA and twist free splicing systems, in words, languages andcombinatorics. Singapore: World Scientific Publishers,1994,170-178.
92. D. Boneh, R. Lipton. On the computational power of DNA.Discrete Appl Math,1996(71):79-94.
93. D. K. Gifferd. On the path to computation with DNA. Science,1994(266):993-994.
94. R. Lipton. DNA solution of hard computational problems.Science,1995(68):49-66.
95. T. Leete, M. Schwartz, R. Williams, et al. Massively parallel DNA computation:expansion of symbolic determinants. In: Proceedings of the2nd DIMACSWorkshop on DNA-Based Computers,1996:49-66.
96. N. Jonoska, S. Karl. A molecular computation of the road coloring problem.InProceedings of the2nd DIMACS Workshop on DNA-Based Computers,1996:148-
158.
97. J. Oliver. Computation with DNA: matrix multiplication. In Proceedings of the2ndDIMACS Workshop on DNA-Based Computers.1996.236-248.
98. F. Guarnieri, C. Bancroft. Use of horizontal chain reaction for DNA-based addition.In Proceedings of the2nd DIMACS Workshop on DNA-Based Computers,1996:249-259.
99. R. Williams, H. Wood. Exascale computer algebra problems interconnect withmolecular reactions and complexity theory. In Proceedings of the2nd DIMACSWorkshop on DNA-Based Computers,1996:260-268.
100.A. Maranthe, A. E. Condon, Corn RM. On correction in DNA computing. In theProceedings of the6th DIMACS meeting on DNA Based Computers, Univ ofLeiden,2000:13-17.
101.K. L. Deninnghoff, R. W. Gat terdam. On the undecidability of splicing systems.Inthe Computer Math,1989,27:133-145.
102.G. Paun, G. Rozenberg, A. Salomaa. DNA Computing.Springer,1998.
103.L. E. Tony. Linear DNA self-assembly with hairpins linear context-free grammars.Dima Cs Series In Discrete Mathematics And Theoretical Computer Science,1999,48:289-296.
104.J. Reif. Parallel biomolecular computation: models and simutations. In7th AnnualACM Symposium on Parallel Algorithms and Architectures,1995:213-223.
105.J. Reif. Local parallel biomolecular computation. In the Proceedings of the3rdDIMACS meeting on DNA Based Computers, Univ of Penns,1997:23-26.
106.M. Amos. Theoretical and experimental DNA computation. In the Bulletin ofEATCS67,1999:125-138.
107.K. Lila. DNA computing: Arrival of Biological Mathematics.The MathematicalIntelligencer,1997,19:9-22.
108.M. Amos, P.E. Dunne, A. Gibbons. DNA simulation of boolean circuits.Proceedings of the Third Annual Conference,1998.679-683.
109.E. Baum. DNA sequences useful for computation. in Proceedings of the2ndDIMACS Workshop on DNA-based Computers,1996:122-127.
110.M. Amos, A. Gibbons, D. Hodgson. Error-resistant implementation of DNAcomputation.in Proceedings of the2nd DIMACS Workshop on DNA-basedComputers,1996,87-101.
111.Q. H. Liu, W. Liman, A. Frutos, et al. DNA Computing on Surfaces.Nature,2000,403:175-179.
112.D. Faulhammer, D. Cukras, R.J. Lipton, et al. Molecular computation: DNAsolutions to chess problems. Proc Natl A cad Sci US,2000,97:1385-1389.
113.W. Zhou, J. R. Zheng, J. J. Yan, J. F. Wang. A novel hybrid algorithm for assemblysequence planning combining bacterial chemotaxis with genetic algorithm. TheInternational Journal of Advanced Manufacturing Technology,2011,52:715-724.
114.Z. X. Yin, B. Song. DNA self-assembly model for matrix additionproblem. Journalof Computers,2011,6(4):698-704.
115.E. Winfree. Algorithmic self-assembly of DNA. PhD thesis. California Institute ofTechnology, Computation and Neural Systems Option,1998.
116.J. F. Wang, J. H. Liu. A novel ant colony algorithm for assembly sequence planning.International Journal of Advanced Manufacturing Technology,2005,25(11):1137-1143.
117.J. M. Liu, J. Han, Y. Y. Tang. Multi-agent oriented constraint satisfaction. ArtificialIntelligence,2002,136(1):101-144.
118.J. Liu, W. C. Zhong, and L. C. Jiao.A multiagent evolutionary algorithm forconstraint satisfaction problems. IEEE Transactions On Systems, Man, AndCybernetics-Part B: Cybernetics,2006,36(1):54-73.
119.K. Deb，H. G. Beyer．Self-Adaptive genetic algorithms with simulated binarycrossover．Evolutionary Computation，2001，9(2): l97-221.
120.古天龙,徐周波.有序二叉决策图及应用.科学出版社,2009.
121.T. L. Gu, H. d. Liu. The symbolic OBDD scheme for generating mechanicalassembly sequences Form Methods, Syst Des,2008（33）:29-44.
122.A. Eiben, P. E. Raue, Z. Ruttkay. Solving constraint satisfaction problems usinggenetic algorithms. In Proceedings of IEEE World Confernece on ComputationalIntelligence,1st IEEE Conference on Evolutionary Computation,1994:543-547.
123.R. Sosic, J. Gu. Efficient local search with conflict minimization: A case study ofthe N-queen problem. IEEE Transactions on Knowledge and Data Engineering,1994,6(5):661-668.
124.M. Boiikovic, M. Golub, L. Budin. Solving n-Queen problem using global parallelgenetic algorithm. Eurocon Ljubljana, Slovenia,2003:104-107.
125.孟大志,曹海萍. DNA计算与生物数学.生物物理学报,2002,18（6）:163-173.
126.D. A. Huffman. Impossible objects as nonsense sentences. Edinbrugh UniversityPress, Machine Intelligence6, Edinburgh: Meltzer B, Michie D,1971:295-323.
127.M. B. Clowes. On seeing things. Artificial Intelligence,1971,2(1):79-116.
128.D. L. Waltz. Generating semantic descriptions from drawings of scenes withshadows. Technical Report AI-TR-271, Massachusetts Institute of Technology,Cambridge, MA,1972.
129.I. Miguel, Q. Shen. Solution techniques for constraint satisfaction problems:foundations. Artificial Intelligence Review,2001,15(4):243-267.
130.R. Dechter, J. Pearl. Tree clustering for constraint networks. Artificial Intelligence,1989,38(3):353-366.
131.N. Sadeh, K. Sycara, Y. Xiong. Bachtracking techniques for the job shop schedulingconstraint satisfaction problem. Artificial Intelligence,1995,76(1-2):455-480.
132.D. N. Pham, J. Thornton, A. Sattar. Modelling and solving temporal reasoning aspropositional satisfiability. Artificial Intelligence,2008,172(15):1725-1782.
133.伍丽华,陈蔼洋,姜云飞.规划问题编码为约束可满足问题的研究.计算机科学,2006,33(8):187-292.
134.B. Cabon, D. Givry, L. Lobjois, et al. Radio link frequency assignment. Constraints,1999,4(1):79-89.
135.TSP Library. http://www.iwr.uni-heidelberg.de/iwr.compot/software,2005.
136.L. V. Kale. An almost perfect heuristic for the n nonattacking queen problem.Inform. Process. Lett.,1990,34:173-178.
137.M. Boiikovic, M. Golub, L. Budin. Solving n-Queen problem using global parallelgenetic algorithm. EUROCON Ljubljana, Slovenia,2003:104-107.
138.C. W. Zeng, Tianlong Gu. A novel assembly evolutionary algorithm for n-queensproblem. International Conference on Computational Intelligence And SecurityWorkshops,2007:171-174.
139.E. David, Goldberg. Generic algorithms in search, optimization and machinelearning. Addison-Werlcy Publishing Comwny Inc., Reading.MA,1989.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |