改进免疫遗传算法及其在优化调度问题中的应用研究
|
摘要
随着生产社会化的不断深入,生产规模及物资流通量越来越大,复杂性也越来越高,优化调度问题已经渗透到科研及工程应用的各个领域。近代人工智能技术的飞速发展对于解决优化调度问题提供了有力的理论基础保障。因此,在该领域的研究具有重要的理论意义和实用价值。传统优化调度方法存在着种种不足,已经不能很好地适用于大规模复杂问题。近年来,多学科交叉研究为解决此类问题提供了新的思路。其中以模仿生物免疫机理为理论基础的人工免疫优化算法在各领域的研究与应用中表现出优异的性能,已成为人工智能领域一个新的研究热点。
免疫遗传算法隶属于人工免疫优化算法范畴,该算法将免疫思想融入遗传进化流程中,使其有选择、有目的地利用特征信息来促进种群向优化趋势发展,同时抑制优化过程中的退化现象。本文在归纳了基本免疫遗传算法的原理与特点的基础上,总结其不足之处,综合运用多种免疫学和遗传学思想,从多种角度对算法进行改进,并将改进算法应用于几种典型的优化调度问题。通过实例仿真,验证改进算法的有效性和实用价值。本文的主要研究内容和成果如下：(1)深入研究人工免疫系统及其算法,系统地介绍了人工免疫系统的生物学原理及其仿生机理,详细阐述了人工免疫系统的具体研究内容和范围。在剖析基本免疫遗传算法原理、框架及特点基础上,着重分析了基本免疫遗传算法解决大规模复杂问题时,在稳定性、收敛性及适应性等方面的不足,提出了相应的改进思路。(2)针对基本免疫遗传算法存在的局部搜索能力差、早熟收敛等问题,借鉴生物免疫系统的克隆选择思想及记忆理论,提出了一种免疫克隆算法。该算法通过引入克隆算子来改善基本免疫遗传算法局部搜索能力差的缺点。通过克隆增殖和超变异算子加大种群的搜索范围,保持了种群的多样性。并把该算法用于求解物流配送调度问题,通过计算不同规模、不同类型的Benchmark问题,验证了算法的稳定性和有效性。(3)针对基本免疫遗传算法存在易陷入平衡态和丢失优势基因等不良现象,提出了一种多种群、双倍体免疫遗传算法。该算法一方面采用多种群同时进化,交换种群之间优势个体所携带的遗传信息,以打破种群内平衡态达到更高的平衡态；另一方面通过双倍体编码方式延长了有用基因块的寿命,显著提高了算法的局部搜索效率,保持了种群的多样性,有利于算法跳出局部最优解。并将该算法应用于求解单级多资源限制生产批量计划问题。通过仿真实例证明,多种群双倍体免疫遗传算法不但具有良好的全局和局部搜索能力,并且具有很好的逼近精度和搜索速度。
(4)针对基本免疫遗传算法由于静态地指定交叉、变异概率和疫苗而带来的搜索过程缓慢甚至停滞不前等缺点,提出了一种自适应免疫遗传算法。该算法通过自适应调整交叉、变异概率,动态生成疫苗等方式,改善了基本免疫遗传算法收敛速度缓慢、疫苗失效等缺点。并将该算法应用于求解柔性作业车间调度问题,应用不同算法对实例进行仿真,将自适应免疫遗传算法的仿真结果同遗传算法、免疫遗传算法进行对比分析,证明改进算法有良好的收敛性和鲁棒性。
本文通过以上的研究工作和仿真结果分析,对改进的免疫遗传算法进行综合性地概括、归纳和总结。在处理复杂优化调度问题方面提出了一些改进的思想,并进行了算法实现和仿真。对有待进一步深入研究的问题进行了设想,对免疫遗传算法解决相关优化调度问题进行了展望。
With the development of socialization of production and the increase of production scale and circulation of materials as well as the improvement of the complexity, optimization scheduling problem is already impregnated into every domain of scientific research and engineering applications. In modern times, the rapid development of artificial intelligence technology has provided a strong theoretical foundation on solving optimization scheduling problem. Therefore the research in this field has important theoretical meaning and practical value. The traditional optimization scheduling method has many shortcomings which would be unable to effectively apply to large scale complex problems. In recent years, interdisciplinary acrossing research has offered a new train of thought to solve such kind of problem, in which artificial immune optimization algorithm based on the theoretical foundation of imitating biological immune mechanism has demonstrated outstanding performance in various researching and application fields and has become a new hotspot in the fartificial intelligence area.
Immune genetic algorithm is subordinate to artificial immune optimization algorithm category. The algorithm put immune thinking into genetic evolution process, which make it have destination to promote population evolving towards optimization trend selectively by using characteristic information. At the same time, it restrains the degraded phenomenon in optimization course. This paper summarizes the theory and characteristics of simple immune genetic algorithm, sums up its shortcomings, improves the algorithm by using various immunology and genetics thinking, and applies the improved algorithm to several typical optimization scheduling problems. Through simulating of examples, the effectiveness and practical value of the algorithm is verified. The main contents and results are as follows:
(1) Researching on the artificial immune system and its algorithm deeply, the paper introduces systematacially the biology principle and simulate mechanism of artificial immune system and elaborates the specific content and of artificial immune system. Having analysed the principle and characteristic of simple immune genetic algorithm, the paper emphasizes the lack of simple immune genetic algorithm at stability, convergence and adaptability on solving complex and large scale issues, and puts forward corresponding improving ideas.
(2) According to the defects of simple immune genetic algorithm that has the poor local search capacity and premature convergence, the paper draws on the thinking of clonal selection and memory theory in biological immune system, and puts forward a sort of immune clonal algorithm. The proposed algorithm reforms the defects of of simple immune genetic algorithm for its poor local research ability by using clonal operator, increases the searching scope and maintains the diversity of the population through clonal proliferation and super mutation operators,. The paper applies immune clonal algorithms to solving the capacitated vehicle routing problems and verifies the validity and stability of improved algorithm through calculating the Benchmark problems by different sizes and types.
(3) According to the ill phenomenons of simple immune genetic algorithm that is easy to fall into equilibrium state and lose advantage genes, the paper puts forward a kind of multi population diploid immune genetic algorithm. On the one hand, the algorithm makes various groups evolving synchronously and exchanges advantaged individuals carrying hereditary information in population, which breaks current equilibrium state in population to reach a higher equilibrium state; On the other hand, it uses the diploid coding mode to prolong the life of useful genic blocks, improve the local search efficiency of algorithm obviously and maintains the diversity of population, which makes the algorithm jump out of local optimal solution. The paper applies the improved algorithm to solving single item capacitated lot-sizing schedule problem. The simulation of examples shows that multi population diploid immune genetic algorithm not only has well overall and local search ability, but also has a well approaching precision and searching speed.
(4) According to the defects of simple immune genetic algorithm that designates crossover probability, mutation probability and vaccine statically which makes the search course to perform slowly and even come to a standstill, the paper puts forward a sort of adaptive immune genetic algorithm. Through adjusting crossover and mutation probability adaptively and generating vaccines dynamically, the algorithm improve the shortcomings of simple immune genetic algorithm for slow convergence and vaccine failure. The paper applies the algorithm to solving flexible job-shop scheduling problem, carries out simulation using different algorithm, comparaes the simulation results of adaptive immune genetic algorithm with genetic algorithm and immune genetic algorithm and has a analysis. The contrastive analysis proves that the improved algorithm has a well convergence and robustness.
Through studies and analysis of simulation results above, the paper performances comprehensive summary, conclusion and summarize to the improved immune genetic algorithms. In the aspect of handling complex optimization scheduling problem, the paper has put forward some thoughts of improvement, carried out realization and simulation of the algorithm. The paper has a envisagement to the problem for future research and has a prospect on solving optimization scheduling problem using immune genetic algorithm.
免疫遗传算法隶属于人工免疫优化算法范畴,该算法将免疫思想融入遗传进化流程中,使其有选择、有目的地利用特征信息来促进种群向优化趋势发展,同时抑制优化过程中的退化现象。本文在归纳了基本免疫遗传算法的原理与特点的基础上,总结其不足之处,综合运用多种免疫学和遗传学思想,从多种角度对算法进行改进,并将改进算法应用于几种典型的优化调度问题。通过实例仿真,验证改进算法的有效性和实用价值。本文的主要研究内容和成果如下：(1)深入研究人工免疫系统及其算法,系统地介绍了人工免疫系统的生物学原理及其仿生机理,详细阐述了人工免疫系统的具体研究内容和范围。在剖析基本免疫遗传算法原理、框架及特点基础上,着重分析了基本免疫遗传算法解决大规模复杂问题时,在稳定性、收敛性及适应性等方面的不足,提出了相应的改进思路。(2)针对基本免疫遗传算法存在的局部搜索能力差、早熟收敛等问题,借鉴生物免疫系统的克隆选择思想及记忆理论,提出了一种免疫克隆算法。该算法通过引入克隆算子来改善基本免疫遗传算法局部搜索能力差的缺点。通过克隆增殖和超变异算子加大种群的搜索范围,保持了种群的多样性。并把该算法用于求解物流配送调度问题,通过计算不同规模、不同类型的Benchmark问题,验证了算法的稳定性和有效性。(3)针对基本免疫遗传算法存在易陷入平衡态和丢失优势基因等不良现象,提出了一种多种群、双倍体免疫遗传算法。该算法一方面采用多种群同时进化,交换种群之间优势个体所携带的遗传信息,以打破种群内平衡态达到更高的平衡态；另一方面通过双倍体编码方式延长了有用基因块的寿命,显著提高了算法的局部搜索效率,保持了种群的多样性,有利于算法跳出局部最优解。并将该算法应用于求解单级多资源限制生产批量计划问题。通过仿真实例证明,多种群双倍体免疫遗传算法不但具有良好的全局和局部搜索能力,并且具有很好的逼近精度和搜索速度。
(4)针对基本免疫遗传算法由于静态地指定交叉、变异概率和疫苗而带来的搜索过程缓慢甚至停滞不前等缺点,提出了一种自适应免疫遗传算法。该算法通过自适应调整交叉、变异概率,动态生成疫苗等方式,改善了基本免疫遗传算法收敛速度缓慢、疫苗失效等缺点。并将该算法应用于求解柔性作业车间调度问题,应用不同算法对实例进行仿真,将自适应免疫遗传算法的仿真结果同遗传算法、免疫遗传算法进行对比分析,证明改进算法有良好的收敛性和鲁棒性。
本文通过以上的研究工作和仿真结果分析,对改进的免疫遗传算法进行综合性地概括、归纳和总结。在处理复杂优化调度问题方面提出了一些改进的思想,并进行了算法实现和仿真。对有待进一步深入研究的问题进行了设想,对免疫遗传算法解决相关优化调度问题进行了展望。
With the development of socialization of production and the increase of production scale and circulation of materials as well as the improvement of the complexity, optimization scheduling problem is already impregnated into every domain of scientific research and engineering applications. In modern times, the rapid development of artificial intelligence technology has provided a strong theoretical foundation on solving optimization scheduling problem. Therefore the research in this field has important theoretical meaning and practical value. The traditional optimization scheduling method has many shortcomings which would be unable to effectively apply to large scale complex problems. In recent years, interdisciplinary acrossing research has offered a new train of thought to solve such kind of problem, in which artificial immune optimization algorithm based on the theoretical foundation of imitating biological immune mechanism has demonstrated outstanding performance in various researching and application fields and has become a new hotspot in the fartificial intelligence area.
Immune genetic algorithm is subordinate to artificial immune optimization algorithm category. The algorithm put immune thinking into genetic evolution process, which make it have destination to promote population evolving towards optimization trend selectively by using characteristic information. At the same time, it restrains the degraded phenomenon in optimization course. This paper summarizes the theory and characteristics of simple immune genetic algorithm, sums up its shortcomings, improves the algorithm by using various immunology and genetics thinking, and applies the improved algorithm to several typical optimization scheduling problems. Through simulating of examples, the effectiveness and practical value of the algorithm is verified. The main contents and results are as follows:
(1) Researching on the artificial immune system and its algorithm deeply, the paper introduces systematacially the biology principle and simulate mechanism of artificial immune system and elaborates the specific content and of artificial immune system. Having analysed the principle and characteristic of simple immune genetic algorithm, the paper emphasizes the lack of simple immune genetic algorithm at stability, convergence and adaptability on solving complex and large scale issues, and puts forward corresponding improving ideas.
(2) According to the defects of simple immune genetic algorithm that has the poor local search capacity and premature convergence, the paper draws on the thinking of clonal selection and memory theory in biological immune system, and puts forward a sort of immune clonal algorithm. The proposed algorithm reforms the defects of of simple immune genetic algorithm for its poor local research ability by using clonal operator, increases the searching scope and maintains the diversity of the population through clonal proliferation and super mutation operators,. The paper applies immune clonal algorithms to solving the capacitated vehicle routing problems and verifies the validity and stability of improved algorithm through calculating the Benchmark problems by different sizes and types.
(3) According to the ill phenomenons of simple immune genetic algorithm that is easy to fall into equilibrium state and lose advantage genes, the paper puts forward a kind of multi population diploid immune genetic algorithm. On the one hand, the algorithm makes various groups evolving synchronously and exchanges advantaged individuals carrying hereditary information in population, which breaks current equilibrium state in population to reach a higher equilibrium state; On the other hand, it uses the diploid coding mode to prolong the life of useful genic blocks, improve the local search efficiency of algorithm obviously and maintains the diversity of population, which makes the algorithm jump out of local optimal solution. The paper applies the improved algorithm to solving single item capacitated lot-sizing schedule problem. The simulation of examples shows that multi population diploid immune genetic algorithm not only has well overall and local search ability, but also has a well approaching precision and searching speed.
(4) According to the defects of simple immune genetic algorithm that designates crossover probability, mutation probability and vaccine statically which makes the search course to perform slowly and even come to a standstill, the paper puts forward a sort of adaptive immune genetic algorithm. Through adjusting crossover and mutation probability adaptively and generating vaccines dynamically, the algorithm improve the shortcomings of simple immune genetic algorithm for slow convergence and vaccine failure. The paper applies the algorithm to solving flexible job-shop scheduling problem, carries out simulation using different algorithm, comparaes the simulation results of adaptive immune genetic algorithm with genetic algorithm and immune genetic algorithm and has a analysis. The contrastive analysis proves that the improved algorithm has a well convergence and robustness.
Through studies and analysis of simulation results above, the paper performances comprehensive summary, conclusion and summarize to the improved immune genetic algorithms. In the aspect of handling complex optimization scheduling problem, the paper has put forward some thoughts of improvement, carried out realization and simulation of the algorithm. The paper has a envisagement to the problem for future research and has a prospect on solving optimization scheduling problem using immune genetic algorithm.
引文
1. 王凌.智能优化算法及其应用[M],北京：清华大学出版社,2001,1-24.
2. 袁亚湘,孙文瑜.最优化理论与方法[M],北京：科学出版社,1997,13-40.
3. 莫宏伟.基于人工免疫系统的数据挖掘技术原理与应用[J],计算机工程与应用,2004,14(3)：28-33.
4. 林锉云,董加礼.多目标优化的方法与理论[M],吉林：吉林教育出版社,1992,3-27.
5. 解可新,韩立兴,林友联.最优化方法[M],天津：天津大学出版社,1997,46-58
6. Christian Prins.A simple and effective evolutionary algorithm for the vehicle routing problem [J],Computers & Operations Research,2004,31(12):1985-2002.
7. 周在青.物流业务管理[M],上海：上海财经大学出版,2003,4-35.
8. 姚新,陈国良,徐慧敏等.进化计算研究进展[J],计算机学报,1995,18(9)：694-706.
9. 徐宗本,李国.解全局优化问题的仿生类算法(Ⅰ)—模拟进化算法[J],运筹学,1995,14(2)：1-13.
10. Barrie M.Baker,M.A.Ayechew.Agenetic algorithm for the vehicle routing problem [J],Computers & Operations Research,2003,30(5):787-800.
11.(美)鲍尔索克斯等著林国龙等译.物流管理：供应链过程的一体化[M],北京：机械工业出版社,1999,23-57.
12.郎茂样.物流配送车辆调度问题的模型和算法研究[D],北京：北方交通大学,2001.
13.李军、郭耀煌.物流配送车辆优化调度理论与方法[M],北京：中国物资出版社,2001.6-34.
14.唐立新.CIMS下生产批量计划理论及其应用[M],北京：科学出版社,1999.15-42.
15. Bahl H C, Ritzaml L P, Gupta J N D. Determining lot sizes and resources requirement [J],A reviews. Operations Research,1987,3:329-345.
16. Manne A S. Programming of economic lot sizes [J], Management Science,1958,14(2):1115-1135.
17. Epped G D, Martin R K. Solving multi-item capacitated lot-sizing problems using variable redefinition[J],Operation Research,1987,35:832-848.
18. Cattrysse D, Mase J, Van Wassenhove L N. Set partitioning and column generation heuristic for capacitated dynamic lot-sizing problem[J],European Journal of Operation Research,1990,46: 38-47.
19. Moustapha D, Harish C B, et al. A lagrangean relaxation approach for very-large-scale capacitated lot-sizing problem [J], Management Science,1992,38(9):1329-1339.
20. Kuik K, Salotnon M.The multi-level lot-sizing program evaluation of a simulated-annealing [J], European Journal of Operational Research,1990,45(6):25-37.
21. Elpida S.T. Agentifying the Process:Task-Based or Robot-Based Decomposition[C],IEEE Conference on System, Manufacturing and Synbenetics,1997:582-587.
22. Balasubramania S, Douglas H N, Morikawa K et al. Evolution of CIM with Genetic Algorithm [J], IEEE 1997:746-749.
23. Garey M.R., Graham R.L. and Johnson D.S.Performance Guarantees for Scheduling Algorithms [J], Operations Research,1978,1(26):3-21.
24. Lenstra J.K., Rinnooy. Kan A.H.G., Brucker P. Complexity of machine scheduling problems [J], Ann.Discr. Math,1977, 1(2):343-362.
25. Garey M.R., Johnson D.S. Computers and Intractability:A guide to the theory of NP-completeness [M], Freeman, San Francisco,1979,135-147.
26. Baker, K. Introduction to Sequencing and Scheduling [M], Wiley, New York,1974,236-254.
27. Johnson S.M. Optimal Two-and Three-Stage Production Scheduling with Set-up Times Included [J], Naval Research Logistics Quarterly,1954, 1(12):61-68.
28. Story, A. E., Wagner, H. M. Computational Experience with Integer Programming for Job-shop Scheduling[J],Industrial Scheduling, Chap.14, Prentice-Nall,New York,1963.
29. Morton, T. and D. Pentico. Heuristic Scheduling Systems-With Applications to a Production Systems and Project Management [M], John Wiley & Sons, New York,1993,324-354.
30. Gere, W S. Heuristics in Job Shop Scheduling [J], Mgmt. Sci.,1966, 1(13):167-190.
31. Mellor. A Review of Job Shop Scheduling [J], Operational Research Quarterly, Vo 1.17, NO.2,1966: 166-171.
32. Lenstra, J. K., Rinnooy Kan, A.H.G,and Brucker,P Complexity of Machine Scheduling Problems[J], Ann. Discrete.Math.,NO.1,1977:343-362.
33. Teofilo Gonzalez and Sartaj Sahni. Flowshop and Jobshop Schedules:Complexity and Approximation [J], Ops.es, Vol26,No.1,1978:36-52.
34. S. Panwalker, Wafik Iskander. A Survey of Scheduling Rules [J], Ops. Res., Uol.25, NO.1, January-Febmay,1977:45-61.
35. Stephen, C.Graves. A Review of Production Scheduling [J], Ops. Res., Uol29, No4, July-August. 1981:646-675.
36. Stanley B. Gershwin, Richard R.Hildebrant, Rajan Suri and Sanjoy K.Mitter. A Control Perspective on Recent Trends in Marnifacturing Systems [J], IEEE Control System Magzine,6(2),1986;3-15.
37. J.Browne. Production Activity Control-a Key Aspect of production Control [J], Int.J. Prod. Res., Vol 26,No.3,1988:415-427.
38. Clyde W. Holsapple and Vatghese S. Jacob, A Genetics Based Hybrid Scheduler for Generating Static Schedules in Flexible Manufacturing Contexts [J], IEEE Transactions on System,Man,and Cybernetics. Vol.23, No.4,1993:953-972.
39. Colin R. Reeves. A Genetic Algorithm for Flowshop Sequencing [J], Computers Ceps Res. Vol.22 No.1,1995:5-13.
40. Federico Della Croce, IZoberto tadei and Giuseppe Volta. A Genetic Algorithm for the Job Shop Problem [J], Computers Ops Res., Vol.22, No.1,1995:15-24.
41.李敏强,寇纪松.遗传算法的基本理论与应用[M],北京：北京化学工业出版社,2004.52-75.
42.席裕庚,柴天佑,恽为民.遗传算法综述[J],控制理论与应用,1996,5(12)：697-708.
43.曹立斌,周建兰.一种改进的禁忌搜索法在函数优化问题中的应用[J],微机发展,2003,10(6)：39-42.
44.刘清海,赵文清,丁予展.禁忌搜索离散优化技术[J],起重运输机械,2001,3(5)：11-13.
45.黄明,闫淑娟,梁旭.遗传算法和禁忌搜索算法在车间调度中的研究进展[J],工业控制计算机,2004,9(2)：4-5.
46. Van Larrhoven P. J. M. Simulated Annealing. Theory and application [M], Netherland:kluwer Academic Pnblisher, Dordrechet,1987.
47. Hopfield J J. Neural networks and physical systems with emergent computational abilities [J],Proceedings Nat. Acad. Sci., USA,1982,79:2554-2558.
48. Hopfield J J, Thank D W. Neural computation of decisions in optimization problems [J], Biol. Cybernetic,1985,52L:141-152.
49. Thank D W, Hopfield J J. Simple 'neural' optimization networks:an A/D converter, signaldecision circuit, and a linear programming circuit [J],IEEE Trans. Circ. Syst.,1986, CAS-33:533-541.
50. Kennedy M P, Chua L O. Neural networks for nonlinear programming [J], IEEE Trans. Circ. Syst., 1986,35:554-562.
51. Dorigom, Bonabeaue, Theraulazg. Ant algorithms and stigmergy [J], Future Generation Computer System,2000,16(6):851-871.
52.李士勇.蚁群优化算法及其应用研究进展[J],计算机测量与控制,2003,11(12)：911-917.
53.陈永强.人工蚁群算法及其在组合优化中的应用[D],哈尔滨：哈尔滨工业大学,2003.
54. Dorigom, Bonabeaue, Theraulazg. Ant algorithms for discrete optimization [J], Artificial Life, 1999,5(3):137-172.
55. Colornia, Dorigom, et al. Ant system for job-shop scheduling[J].Belgian Journal of Operations Research, Statistics and Computer Science (JORBEL),1994,34(1):39-53.
56. Bullnheimerb, et al. Applying the ant system to the vehicle routing problem [A].In OSMANIH, et al. Meta-Heuristics:Advances and Trends in Local Search Paradigms for Optimization[C]. Kluwer Academics,1998,109-120.
57.张素兵,刘泽民.基于蚂蚁算法的分级QoS路由调度方法[J],北京邮电大学学报,2000,23(4)：11-15.
58. Kennedy J, Eberhart R.C.Particle swarm optimization[C], IEEE International Conference on NN,1995,5:1942-1948.
59. Eberhart R.C, Kennedy J.A new optimizer using particle swarm theory[C],6th International Symposium on Micro Machine and Human Science, Nagoya,1995,39-43.
60. Eberhart R.C, Shi Yuhui.Particle swarm optimization:Developments, Applications and Resources [J].Proceedings of the 2001 Congress on Evolutionary Computation,2001,1:81-86.
61. Shi Yuhui, Eberhart, R. C.Parameter selection in particle swarm optimization[C],7th Annual Conferenceon Evolutionary Programming,1998,591-600.
62. Shi Yuhui, Eberhart, R. C.Experimental Study of Particle Swarm Optimization[C], Proceedings of SCI2000 Conference, Orlando, USA.
63.周驰,高海兵,高亮,章万国.粒子群优化算法[J],计算机应用研究,2003,5(12)：7-11.
64. Perelson A S. Immune Network Theory. Immunology Review [M],1989,10:5-36.
65. Jerne N K. The Immune System. Scientific American [J],1973,229(1):52-60.
66.焦李成,杜海峰.人工免疫系统进展与展望[J],电子学报,2003,31(10)：1540-1548.
67. Farmer J D, Parckard N H and Perelson A S. The immune system:adaptation and machine learning [J], Physic D.1986,22:187-204.
68. Dasgupta D. Artificial neural networks and artificial immune systems:similarities and differences. 1997 IEEE International Conference on Computational Cybernetics and Simulation [C], Institute of Electrical and Electronics Engineers, Incorporated.1997:8773-8781.
69.丁永生,任立红.人工免疫系统：理论与应用[J],模式识别与人工智能,2000,13(1)：52-59.
70. Ishida Y. Fully Distributed Diagnosis by PDP Learning Algorithm:Towards Immune Network PDP Model [J], In:Proceedings of International Joint Conference on Neural Networks,1990:777-782.
71.徐雪松.基于人工免疫系统的函数优化及其在复杂系统中的应用研究[D],浙江：浙江大学,2004.
72. D. Castro, V. Zuben. The Clonal Selection Algorithm with Engineering Applications [J], Proc. of GECC'00, Las Vegas, USA.2000,7:36-37.
73. D. Castro, V. Zuben. Learning and Optimization Using the Clonal Selection Principle [J], IEEE Transaction on Evolutionary Computation.2002,6(3):239-251.
74. S. T. Wierzchon. Function Optimization by the Immune Metaphor [J], TASK QUARTERLY.2002, 6(3):1-16.
75. A. M. Costa, P. A. Vargas. Makespan Minimization on Parallel Processors:An Immune-Based Approach[C], CEC'02, Proceedings of the 2002 Congress.2002,1:920-925.
76. D. Castro, J. Timmis. An Artificial Immune Network for Multimodal Function Optimization[C], Cec'02, Proc. of the 2002 Congress.2002,1:699-704.
77. A. C. C. Carlos, C. C. Nareli. An Approach to Solve Multiobjective Optimization Problems Based on Artificial Immune System [J],1 st International Conference on Artificial Immune Systems.2002: 212-221.
78. A. C. C. Carlos, C. C. Nareli. Solving Multiobjective Optimization Problems using an Artificial Immune System [M], Netherlands:Kluwer Academic Publishers.2002.
79. H. Bersini. The Immune and the Chemical Crossover [J], IEEE Transactions on Evolutionary Computation.2002,6(3):306-313.
80. H. F. Du, L. C. Jiao, S. A. Wang. Clonal operator and Antibody Clone Algorithms[C],1st International Conference on Machine Learning and Cybernetics,Beijing, China.2002,1:506-510.
81.葛红,毛宗源.免疫算法几个参数的研究[J],华南理工大学学报,2002,30(12)：15-18.
82.葛红,毛宗源.免疫算法的改进[J],计算机工程与应用,2002,14：47-50.
83. Adnan Acan. Clonal Selection Algorithm with Operator Multiplicity [J], Congress on Evolution Computation.2004,2:1909-1915.
84. Chunhua Li, Yanfei Zhu, Zongyuan Mao. A Novel Artificial Immune Algorithm Applied to Solve Optimization Problems [C],8th International Conference on Control, Automation, Robotics and Vision.2004(1):232-237.
85. YING YU, CHAO-ZHEN HOU. A CLONAL SELECTION ALGORITHM BY USING LEARNING OPERATOR[C],3rd International Conference on Machine Learning and Cybernetics. 2004(5):2924-2929.
86. Yinsheng Luo, Renhou Li, Feng Tian. Application of Artificial Immune Algorithm to Multimodal Function Optimization[C],5th World Congress on Intelligent Control and Automation.2004(3): 2248-2252.
87.郑日荣,毛宗源,罗欣贤.改进人工免疫算法的分析研究[J],计算机工程与应用,2003.34：35-37.
88.刘若辰,杜海峰,焦李成.一种免疫单克隆策略算法[J],电子学报,2004,32(11)：1880-1884.
89.刘若辰,杜海峰,焦李成.免疫多克隆策略[J],计算机研究与发展,2004,41(4)：571-576.
90.凌军等.基于小生境技术的多样性抗体生成算法[J],电子学报,2003,31(8)：1130-1133.
91.宋丹,傅明.基于多种群的自适应免疫进化计算[J],控制与决策,2005,20(11)：1251-1255.
92.李广强,赵洪伦,赵凤强,滕弘飞.人机合作的免疫算法及其在布局设计中的应用[J],计算机工程,2005,31(21)：4-7.
93. L. C. Jiao, L. Wang. A Novel Genetic Algorithm Based on Immunity [J], IEEE Tran.on System, Man, and Cybernetics.2000,30(5):552-561.
94.王磊,潘进,焦李成.免疫算法[J],电子学报,2000,28(7)：74-78.
95.王磊,潘进,焦李成.基于免疫策略的进化算法.自然科学进展[J],2000,10(5)：451-455.
96. X. Y. Sun, D. W. Gong. Research on a Modified Immunity Genetic Algorithm[C], Proc. of the 2002 International Conference on Control and Automation. Xiamen, China.2002:777-780.
97.王熙法,张显俊,曹先彬等.一种基于免疫原理的遗传算法[J],小型微型计算机系统,1999,20(2)：17-20.
98.曹先彬,刘克胜,王熙法.基于免疫遗传算法的装箱问题求解[J],小型微型计算机系统,2000,21(4)：361-363.
99.胡纯德,祝延军,高随祥.基于人工免疫算法和蚁群算法求解旅行商问题[J],计算机工程与应用,2004,4：60-63.
100.陈荣元,唐贤瑛,谭旭.基于免疫-蚂蚁算法的多约束QoS路由选择[J],通信学报,2004,25(8)：89-95.
101.高鹰,谢胜利.免疫粒子群优化算法[J],计算机工程与应用,2004,6：4-7.
102.杜海峰,公茂果,刘若辰,焦李成.自适应混沌克隆进化规划算法[J],中国科学E刊信息科学,2005,35(8)：817-829.
103.曹先彬,段建国.基于免疫选择和自组织临界变异的进化算法[J],系统仿真学报,2004,16(8)：1785-1788.
104.罗小平.人工免疫遗传学习算法及其工程应用研究[D],浙江：浙江大学,2002.7.1-29.
105.龚非力,医学免疫学[M],北京：科学出版社,2003,1-27.
106. Forrest S, Javornik B, Smith R, Perelson AS. Using genetic algorithms to explore pattern recognition in the immune system[J],Evolutionary Computation,1993,1(3):191-211.
107.孙宁,人工免疫优化算法及其应用研究[D],哈尔滨：哈尔滨工业大学,2006.17.1-29.
108. Colaco C. Acquired Wisdom in Innate Immunity [J], Immunology Today.1998,19 (1):50-52.
109. Medzhitov R, Janeway Jr C A. Innate Immunity:Impact on the Adaptive Immune Response [J], Current Opinion in Immunology.1997, (9):4-9.
110. Zinkernagel R M. Kelly J. How Antigen Influences Immunity [J], The Immunologist.1997, (4):114-120.
111. Sprent J. T and B Memory Cells [M], Cell.1994, (76):315-322.
112. Bere C, Ziegner M. The Maturation of the Immune Response [J], Immunology Today.1993,14 (8):400-402P.
113. Tarlinton D. Germinal Centers:Form and Function. Current Operation in Immunology [J],1998, (10):245-251.
114. Jerne NK. The immune system [J], Scientific American,1973,229(1):51-60.
115. George A J T, Gray D. Receptor editing during affinity maturation [J],Immune Today,1999,20(4): 196-200.
116. De Castro L N, Von Zuben F J. Artificial immune system:Part Ⅰ:basic theory and applications [J], Technical Report-RT DCA,1999,4(1):89.
117. Burnet F M. The Clonal Selection Theory of Acquired Immunity. Cambridge [M], UK:Cambridge University Press,1959.
118. De Castro L h, Von Zuben F J. The Clonal Selection Algorithm with Engineering Applications [J], Proc. of Artificial Immune System Workshop, Genetic and Evolutionary Computation Conference. San Francisco:Morgan Kaufmann,2000:36-37.
119.莫宏伟.人工免疫系统原理与应用[M],哈尔滨：哈尔滨工业大学出版社,2003.1-390.
120. Perelson A. Immune network theory [J], Immunological Review,1989,110:5-36.
121. De Boer R J, Segel L A & Perelson A S. Pattern Formation in One-and Two-dimensional Shape-Space Models of the Immune System [J], J. theor. Biol.1992,155:295-333.
122.王重庆.分子免疫学基础[M],北京：北京大学出版社,1999.
123. Tang Z, Yamaguchi T, Tashima K, et al. Multiple-Valued immune network modeland its simulation [J]. In:Proc 27th Int Symposium on multiple-valued logic, Autigonish, Canada.1997,233.
124. Herzenberg L A, Black S J. Regulatory circuits and antibody response [J], European J of Immiune.1980,10:1-11.
125.肖运本.免疫学基础及病原生物学[M],北京：中国医药科技出版社,1999.
126. KrishnaKuma K, Neidhoefer J. Immunized adaptive critics for level 2 intelligent control [J],1997 IEEE International Conference on Computational Cybernetics and Simulation. Institute of Electrical and Electronics Engineers, Incorporated,1997:856-861.
127. Sasaki M, Kawafuku M. Takahashi K. An immune feedback mechanism based adaptive learning of neural network controller [J], ICONIP'99.6th International Conference on Neural Information Processing. IEEE Computer Society Press,1999:502-507.
128.丁永生,任立红.一种新颖的模糊自调整免疫反馈控制系统[J],控制与决策,2000,15(4)：443-446.
129.丁永生,唐明浩.一种智能调节的免疫反馈控制系统[J],自动化仪表,2001,22(10)：5-7.
130.李海峰,王海风,陈珩.免疫系统建模及其在电力系统电压调节中的应用[J],电力系统自动化,2001(12)：17-23.
131.丁永生,任立红.人工免疫系统：理论与应用.模式识别与人工智能[J],2000.3. Vol.13. No.1.52-57.
132.肖人彬,王磊.人工免疫系统：原理、模型、分析及展望[J],计算机学报,2002.12. Vol.25. No.12.1281-1291.
133. Ishida Y, Adachi N. Active Noise Control by an Immune Algorithm [J], Adaptation in Immune System as an Evolution.Proc. ICEC96,1996,150-153.
134. Ishida Y. An Immune Network Model and Its Applications to Process Diagnosis[J], Systemsand Computers in Japan,1993,24(6):646-651.
135. Ishida Y, Mizzessyn F. Learning Algorithms on Immune Network Model[C], Application to Sensor Diagnosis. Proc, IJCNN92, Beijing,1992,33-38.
136. Chun J, Kim M, Jun H. Ahape Optimization of Electromagnetic Devices Using Immune Algorithm [J], IEEE Transactions on Magnetics,1997,33(2):232-240.
137. Chun J, Lim J, Jung H, Yoon J. Optimal Design of Synchronous with Parameter Correction Using Immune Algorithm [J], IEEE Transctions on Energy Conversion,1999(14),3:610-615.
138. Huang S. An Immune-based Optimization Method to Capacitor Placement in a Radial Distribution System [J], IEEE Transaction on Power,2000,3 (15):744-748.
139. Hong J, Lee W, Lee S, Lee B, Lee Y. An Efficient Production Algorithm for MultiHead Surface Mounting Machines Using the Biological Immune Algorithm [J], International Journal of Fuzzy Systems,2000,1 (2):45-53.
140.杨艳云.免疫算法在交流电机矢量控制中的应用研究[D],太原：太原理工大学,2006,4.
141. Hong J, Lee W, Lee S, Lee B, Lee Y. An Efficient Production Algorithm for MultiHead Surface Mounting Machines Using the Biological Immune Algorithm [J], International Journal of Fuzzy Systems,2000,1 (2):45-53.
142.黄席樾等.现代智能算法理论及应用[M],北京：科学出版社,2005.
143. Chun J, Lim J, Jung H, Yoon J. Optimal Design of Synchronous with Parameter Correction Using Immune Algorithm[J], IEEE Transctions on Energy Conversion,1999(14),3:610-615.
144. Huang S. An Immune-based Optimization Method to Capacitor Placement in a Radial Distribution System [J], IEEE Transaction on Power,2000,3 (15):744-748.
145. Forrest. S., Perelson. A. S., et.al. Self-nonself Discrimination in a Computer [J], In Proceedings of the IEEE Symposium on Research in Security and Privacy, Okaland, CA,1994 202-212.
146.缪红萍.免疫遗传算法及应用研究[D],北京：北京化工大学,2005,6.
147. Goldberg D E. Genetic Algorithms in Search, Optimization and Machine Learning, Reading [M], MA:Addison-Wesley,1989:110-123.
148. Farmer J. D. Packard N. H. and Perelson A. S. The immune system, adaptation and machine Learning [J], Physica D,1987,22:187-204.
149. Huang S. An Immune-based Optimization Method to Capacitor Placement in a Radial Distribution System [J], IEEE Transaction on Power,2000,3(15):744-748.
150.焦李成,杜海峰,刘芳等.免疫优化计算、学习与识别[M],北京：科学出版社,2006,6.
151. Leandro N De Castro, Fernando J Von Zuben. Learning and Optimization Using the Clonal Selection Principle [J],.IEEE Transactions on Evolutionary Computation, Special Issue on Aritifical Immune System,2002,6(3):239-251.
152. Bersini H. and Varela F. J. Hints for adaptive problem solving gleaned from immune networks [M], Proceedings of the first workshop on parallel problem solving from nature. Springer& Verlag,1990.
153.左兴权,李士勇.一类自适应免疫进化算法[J],控制与决策,2004,19(3)：252～256.
154.王小平,曹立明.遗传算法一理论、应用与软件实现[M],西安：西安交通大学出版社,2002.1.
155. Ishida Yoshiteru,Adachi Norihiko.Active noisecontrol by an immune algorithm:Adaptation in immune system as an evolution[A].Proc of the IEEE Conf on Evolutionary Computation [C],Piscataway:IEEE,1996,150-153.
156. Satoshi Endoh,Naruaki Toma,Koji Yamada.Immune algorithm for n-TSP[A].Proc of the IEEE Int Confon Systems,Man and Cybernetics[C],Pisca-taway:IEEE,1998,3844-3849.
157. Dasgupta D.An overview of artificial immune systems and their applications[A].Artificial Immune Systems and Their Applications,Ed[M],Dipankar asgupta,Pub.Springer-Verlag,1998,3-21.
158.莫宏伟,金鸿章.免疫算法原理与应用[J],航空计算技术,2002,32(4)：49～51.
159. N De Castro, Jonathan Timmis. Artificial immune system:a new computational intelligence approach [M], British:Springer Press,2002:77.
160. Cooper K D, Hall M W, Kennedy K. Procedure cloning [J], Proceedings of the International Conference on Computer Languages 1992:96-105.
161. Balazinska M, Merlo E, Dagenais M, etl. Advanced clone-analysis to support object-oriented system refactoring. Proceedings [J], Seventh Working Conference on Reverse Engineering,2000:98-107.
162.林学颜,张玲.现代细胞与分子免疫学[M],北京：科学出版社,1999.
163.陆德源,马宝骊.现代免疫学[M],上海：上海科技教育出版社,1998.
164. T. B. Kepler, A. S. Perelson. Somatic Hypermutation in B Cells:an Optimal Control Treatment [J], J.theror. Biol.,1993,164:37-64.
165. Calada, F. and Seiden, P. E. Affinity Maturation and Hypermutation in a Simulation of the Humoral Immune Response [J],Eur. J. Immunol.1996,26:1350-1358.
166.周明,孙树栋.遗传算法原理及应用[M],北京：国防工业出版社,2002.
167.宋毅.基于遗传算法的生产调度方法及其软件实现[D],杭州：浙江工业大学,2003.
168.王万良,吴启迪.生产调度智能算法及其应用[M],北京：科学出版社,2007.
169.周远晖,陆玉昌,石纯一.基于克服过早收敛的自适应并行遗传算法[J],清华大学学报(自然科学版),1998,38(3)：93-95.
170.邹琳,夏巨谌,胡国安.基于实数编码的多种群并行遗传算法研究[J],小型微型计算机系统,2004,25(6)：982-986.
171.Bessaou M, Siarry P. A new tool in electrostatics using a really-coded multi-population geneticalgorithm tuned through analytical test problems [J], Advances in Engineering Software, 2001,32(5):363-374.
172.司书宾,孙树栋,徐娅萍.求解Job-shop调度问题的多种群双倍体免疫算法研究[J],西北工业大学学报,2007,25(1)：27-30.
173.张建,马光文,杨东方,等.双倍体遗传算法求解龙溪河级电站长期优化调度问题[J],四川水利,2002,12(5)：33-35.
174.余建军,孙树栋,牛刚刚,等.面向Job-shop调度的改进免疫算法研究[J],工业工程,2007,10(2)： 93-96.
175. Xiaoyao Zhou, Haozhong Cheng. The induction motor parameter estimation through an adaptive genetic algorithm [J], Universities Power Engineering Conference.2004,1:494-498.
176. HazemM.Abbas, Mohamed M.Bayoumi. Volterra system identification using adaptive real-code genetic algorithms [J], Applied Soft Computing.2004,5:75-86.
177.欧阳森,王建华,耿英三等.一种新的改进遗传算法[J],计算机工程与应用,2003,39(11)：13-15.
178. Jackie Rees, Gary J.Koehler.Learning genetic algorithm parameters using hidden Markov models [J], European Journal of Operational Research.2006,175::806-820.
179.金晶,苏勇.一种改进的自适应遗传算法.计算机工程与应用[J],2005,41(18)：64-69.
180. SRINIVAS M, PATNAILK L M. Adaptive probabilities of crossover and mutation in genetic algorithms [J], IEEE Transaction on System, Man and Cybernetics.1994,24(4):656-667.
181. M Srinivas, L M Patnaik. Adaptive probabilities of crossover and mutation in genetic algorithms [J], IEEE Transactions on Systems, Man and Cybernetics,1994; 24(4):656-665.
182. Dasgupta D. Artificial Immune Systems and Their Applications [M], Berlin Heidelberg: Springer-Verlang,1999.
183. Ho Nhu Binh and Tay Joe Cing. GENACE:An efficient cultural algorithm for solving the flexible job-shop problem[C], In Proceedings of the IEEE Congress on Evolutionary Computation 2004, 1759-1766.
184. Jiao Licheng, WangLei. A novel genetic algorithm based on immunity [J],IEEE Transactions on Systems, Man, and Cybernetics,2000,30 (5):552-561.
185. Huang S. Enhancement of thermal unit commitment using immune algorithms based optimization approaches [J],Electrical Power and Energy System,1999,21:245-252.
186. Hunt J E. Cooke D E. Learning using an artificial immune system [J], Journal of Network and Computer Application,1996,9(4):189-212.
187. D. Dasgupta. Artificial neural networks and artificial immune systems:Similarities and differences. Systems [J], Man, and Cybernetics.1997,1:873-878.
188. E.D. Talliard. Parallel Iterative Search Methods for Vehicle Routing Problems [J], Networks 1993, 23,661-673.
189.郎茂样.物流配送车辆调度问题的模型和算法研究[D],北京：北方交通大学,2001.
190.陈火根,丁红钢,程耀东.物流配送中心车辆调度模型与遗传算法设计[J],浙江大学学报,2003,37(5)：512-516.
191.吴璟莉,刘仁辉.分批配送的有时间窗车辆路径问题的遗传算法[J],计算机工程,2006,32(8)：213-215.
192.谢秉磊,郭耀煌,郭强.动态车辆路径问题现状与展望[J],系统工程理论方法应用,2002,11(2)：116-120.
193.郎茂祥.基于遗传算法的物流配送路径优化问题研究[J],中国公路学报,2002,15(3)：76-79.
194. Bahl H C, Ritzman L P, Gupta J N D. Detemining lot-sizing and resource requirements review [J],Operational Research,1987,35 (3):329-345.
195. Tang O, Manne A S. Simulated annealing in lot sizing problems[J],Int J. Production Economics,2004(88):173-181.
196. M. Fatih Tasgetiren, Liang Y C. A binary particle swarm optimization algorithm for lot Sizing problem[J] Journal of Economic and Social Research, Vol.5, No.2, Jun.2003, pp.1-20.
197.唐立新,杨自厚.CIMS中带多资源的CLSP问题的遗传算法[J],系统工程理论与实践,1997,4(4)39-44.
198. Nembauser, GL.andWolse, L.A., Integer and Combinatorial Optimization, Wiley-Interscience Publication [M], Joh n Wzley&Sony Inc.
199.许志兴,丁运亮,熊仲宇,陆金桂.基于退火惩罚混合遗传算法求解生产批量计划问题[J],南京航空航天大学学报,2001,33(2)：91-95.
200. Maes J, McClain J O, Thomas L J. Multilevel capacitated lot-sizing complexity and LP-based heuristics[J],European Journal of Operation Research,1991,53:131-148.
201. Stadtier H. Mixed integer programming model formulations for dynamic multi-level capacitated lot-sizing [J], European Journal of Operation Research,1996,94:561-581.
202. Wolsey L A. Progress with single-item lot sizing [J], European Journal of Operational Research, 1995,86:395-401.
203. J. Carlier, E. Pinson. An algorithm for solving the job-shop problem [J], Management Science,1989, 35(2):164-176.
204. Hurink E., Jurisch B., Thole M. Tabu search for the job shop scheduling problem with multi-purpose machines [J], Operations Research Spektrum,1994,15:205-215.
205. Kacem I., Hammadi S., Borne P. Pareto-optimality approach for flexible job-shop scheduling problems:Hybridization of evolutionary algorithms and fuzzy logic [J], Mathematics and Computers in Simulation,2002,60:245-276.
206.张超勇,饶运清,李培根等.柔性作业车间调度问题的两级遗传算法[J],机械工程学报,2007,43(4)：119-124.
207. Holsapple W, Jacob V S, Pakath R, et al. A genetics-based hybrid scheduler for generating static schedules in flexible manufacturing contexts [J], IEEE Transaction on Systems. Man and Cybernetics,1993,23 (4):953-972.
208.熊锐,吴澄.车间生产调度问题的技术现状与发展趋势[J],清华大学学报.1998,38(10)：55-60.
209. Bruker P, Schlie R, Job-shop scheduling with multi-purpose machines [J],Computing,1990 45 (4): 369-375.
210. Brandimarte P. Routing and scheduling in a flexible job shop by taboo search [J], Annals of Operations Research.1993,22(2):157-183.
211. Mati Y, Rezg N, Xie X L. An integrated greedy heuristic for a flexible job shop scheduling problem [J]. The Proceedings of IEEE International Conference on Systems, Man, and Cybernetics. Piscataway, NJ, USA,2001.2534-2539.
212. Hapke M.Pareto simulated annealing for fuzzy multi-objective combinatorial optimization [J], Journal of Heuristics,2000,6(3):329-345.
213. Rigao C. Tardiness minimization in a flexible job shop:a tabu search approach [J], Journal of Intelligent Manufacturing,2004,15(1):103-115.
214. Mastrolilli M, Gambardella L M. Effective neighborhood functions for the flexible job shop problem [J], Journal of scheduling,2002,3(1):3-20.
215.翁妙凤,李晓敏.基于模拟退火多种群并行进化规划的柔性调度方法[J],华东船舶工业学院学报,2002,16(2)：27-31.
216.张维存,郑王谬,吴晓丹.基于主一从遗传算法求解柔性调度问题[J],计算机集成制造系统,2006,12(8)：1241-1245.
217.张超勇,饶运情,李培根,等.求解作业车间调度问题的一种改进遗传算法[J],计算机集成制造系统,2004,10(8)：966-970.
218.顾绍元,王小平,曹立明.基于遗传算法的柔性调度系统模型及其应用[J],同济大学学报,2002,30(2)：226-231.
219.王凌.车间调度及其遗传算法[M],北京：清华大学出版社,2003,5.
220. Panwalkar S.A., survey of scheduling rules. Operation Research [J],1977,25(1):45-61.
221.方剑.进化算法及其在Job Shop调度中的应用[D],上海：上海交通大学,1996.
222.陈萍,郭金锋.对Hopfield神经网络求解TSP的研究[J],北京邮电大学学报,1999,22(2)：58-61.
223.谢胜利,董金祥,黄强.基于遗传算法的车间作业调度问题求解[J],计算机工程与应用,2002,10：79-82.
224.耿兆强,邹益仁.基于遗传算法的作业车间模糊调度问题的研究[J],计算机集成制造系统,2002,8(8)-616-620.
225.何霆,梁办平.一种新的FMS混合调度算法[J],控制理论与应用,2000,17(6)：894-898.
226.张超勇,饶运情,李培根,等.柔性作业车间调度问题的两极遗传算法[J],机械工程学报,2007,43(4)：119-124.
227.杨晓梅,曾建潮.遗传算法求解柔性job shop调度问题[J],控制于决策,2004,19(10)：1197-1200.
228.刘乐,王艳秋,陈秋艳.人工免疫系统的研究与应用[J],辽宁工学院学报,2006,5(26)：286-290.
229. Najid N. M., Stephane D. P., Zaidat A. Modified simulated annealing method for flexible job shop scheduling problem[C], Proceedings of the IEEE International Conference on Systems Man and Cybernetics, NJ, USA:IEEE,2002.89-94.
230. P. Brandimarte. Routing and scheduling in a flexible job shop by tabu search [J], Annals of Operations Research,1993,22,158-183.
231. Ho Nhu Binh and Tay Joc Cing. GENACE:An efficient cultural algorithm for solving the flexible job-shop problem[C], In Proceedings of the IEEE Congress on Evolutionary Computation 2004, 1759-1766.
2. 袁亚湘,孙文瑜.最优化理论与方法[M],北京：科学出版社,1997,13-40.
3. 莫宏伟.基于人工免疫系统的数据挖掘技术原理与应用[J],计算机工程与应用,2004,14(3)：28-33.
4. 林锉云,董加礼.多目标优化的方法与理论[M],吉林：吉林教育出版社,1992,3-27.
5. 解可新,韩立兴,林友联.最优化方法[M],天津：天津大学出版社,1997,46-58
6. Christian Prins.A simple and effective evolutionary algorithm for the vehicle routing problem [J],Computers & Operations Research,2004,31(12):1985-2002.
7. 周在青.物流业务管理[M],上海：上海财经大学出版,2003,4-35.
8. 姚新,陈国良,徐慧敏等.进化计算研究进展[J],计算机学报,1995,18(9)：694-706.
9. 徐宗本,李国.解全局优化问题的仿生类算法(Ⅰ)—模拟进化算法[J],运筹学,1995,14(2)：1-13.
10. Barrie M.Baker,M.A.Ayechew.Agenetic algorithm for the vehicle routing problem [J],Computers & Operations Research,2003,30(5):787-800.
11.(美)鲍尔索克斯等著林国龙等译.物流管理：供应链过程的一体化[M],北京：机械工业出版社,1999,23-57.
12.郎茂样.物流配送车辆调度问题的模型和算法研究[D],北京：北方交通大学,2001.
13.李军、郭耀煌.物流配送车辆优化调度理论与方法[M],北京：中国物资出版社,2001.6-34.
14.唐立新.CIMS下生产批量计划理论及其应用[M],北京：科学出版社,1999.15-42.
15. Bahl H C, Ritzaml L P, Gupta J N D. Determining lot sizes and resources requirement [J],A reviews. Operations Research,1987,3:329-345.
16. Manne A S. Programming of economic lot sizes [J], Management Science,1958,14(2):1115-1135.
17. Epped G D, Martin R K. Solving multi-item capacitated lot-sizing problems using variable redefinition[J],Operation Research,1987,35:832-848.
18. Cattrysse D, Mase J, Van Wassenhove L N. Set partitioning and column generation heuristic for capacitated dynamic lot-sizing problem[J],European Journal of Operation Research,1990,46: 38-47.
19. Moustapha D, Harish C B, et al. A lagrangean relaxation approach for very-large-scale capacitated lot-sizing problem [J], Management Science,1992,38(9):1329-1339.
20. Kuik K, Salotnon M.The multi-level lot-sizing program evaluation of a simulated-annealing [J], European Journal of Operational Research,1990,45(6):25-37.
21. Elpida S.T. Agentifying the Process:Task-Based or Robot-Based Decomposition[C],IEEE Conference on System, Manufacturing and Synbenetics,1997:582-587.
22. Balasubramania S, Douglas H N, Morikawa K et al. Evolution of CIM with Genetic Algorithm [J], IEEE 1997:746-749.
23. Garey M.R., Graham R.L. and Johnson D.S.Performance Guarantees for Scheduling Algorithms [J], Operations Research,1978,1(26):3-21.
24. Lenstra J.K., Rinnooy. Kan A.H.G., Brucker P. Complexity of machine scheduling problems [J], Ann.Discr. Math,1977, 1(2):343-362.
25. Garey M.R., Johnson D.S. Computers and Intractability:A guide to the theory of NP-completeness [M], Freeman, San Francisco,1979,135-147.
26. Baker, K. Introduction to Sequencing and Scheduling [M], Wiley, New York,1974,236-254.
27. Johnson S.M. Optimal Two-and Three-Stage Production Scheduling with Set-up Times Included [J], Naval Research Logistics Quarterly,1954, 1(12):61-68.
28. Story, A. E., Wagner, H. M. Computational Experience with Integer Programming for Job-shop Scheduling[J],Industrial Scheduling, Chap.14, Prentice-Nall,New York,1963.
29. Morton, T. and D. Pentico. Heuristic Scheduling Systems-With Applications to a Production Systems and Project Management [M], John Wiley & Sons, New York,1993,324-354.
30. Gere, W S. Heuristics in Job Shop Scheduling [J], Mgmt. Sci.,1966, 1(13):167-190.
31. Mellor. A Review of Job Shop Scheduling [J], Operational Research Quarterly, Vo 1.17, NO.2,1966: 166-171.
32. Lenstra, J. K., Rinnooy Kan, A.H.G,and Brucker,P Complexity of Machine Scheduling Problems[J], Ann. Discrete.Math.,NO.1,1977:343-362.
33. Teofilo Gonzalez and Sartaj Sahni. Flowshop and Jobshop Schedules:Complexity and Approximation [J], Ops.es, Vol26,No.1,1978:36-52.
34. S. Panwalker, Wafik Iskander. A Survey of Scheduling Rules [J], Ops. Res., Uol.25, NO.1, January-Febmay,1977:45-61.
35. Stephen, C.Graves. A Review of Production Scheduling [J], Ops. Res., Uol29, No4, July-August. 1981:646-675.
36. Stanley B. Gershwin, Richard R.Hildebrant, Rajan Suri and Sanjoy K.Mitter. A Control Perspective on Recent Trends in Marnifacturing Systems [J], IEEE Control System Magzine,6(2),1986;3-15.
37. J.Browne. Production Activity Control-a Key Aspect of production Control [J], Int.J. Prod. Res., Vol 26,No.3,1988:415-427.
38. Clyde W. Holsapple and Vatghese S. Jacob, A Genetics Based Hybrid Scheduler for Generating Static Schedules in Flexible Manufacturing Contexts [J], IEEE Transactions on System,Man,and Cybernetics. Vol.23, No.4,1993:953-972.
39. Colin R. Reeves. A Genetic Algorithm for Flowshop Sequencing [J], Computers Ceps Res. Vol.22 No.1,1995:5-13.
40. Federico Della Croce, IZoberto tadei and Giuseppe Volta. A Genetic Algorithm for the Job Shop Problem [J], Computers Ops Res., Vol.22, No.1,1995:15-24.
41.李敏强,寇纪松.遗传算法的基本理论与应用[M],北京：北京化学工业出版社,2004.52-75.
42.席裕庚,柴天佑,恽为民.遗传算法综述[J],控制理论与应用,1996,5(12)：697-708.
43.曹立斌,周建兰.一种改进的禁忌搜索法在函数优化问题中的应用[J],微机发展,2003,10(6)：39-42.
44.刘清海,赵文清,丁予展.禁忌搜索离散优化技术[J],起重运输机械,2001,3(5)：11-13.
45.黄明,闫淑娟,梁旭.遗传算法和禁忌搜索算法在车间调度中的研究进展[J],工业控制计算机,2004,9(2)：4-5.
46. Van Larrhoven P. J. M. Simulated Annealing. Theory and application [M], Netherland:kluwer Academic Pnblisher, Dordrechet,1987.
47. Hopfield J J. Neural networks and physical systems with emergent computational abilities [J],Proceedings Nat. Acad. Sci., USA,1982,79:2554-2558.
48. Hopfield J J, Thank D W. Neural computation of decisions in optimization problems [J], Biol. Cybernetic,1985,52L:141-152.
49. Thank D W, Hopfield J J. Simple 'neural' optimization networks:an A/D converter, signaldecision circuit, and a linear programming circuit [J],IEEE Trans. Circ. Syst.,1986, CAS-33:533-541.
50. Kennedy M P, Chua L O. Neural networks for nonlinear programming [J], IEEE Trans. Circ. Syst., 1986,35:554-562.
51. Dorigom, Bonabeaue, Theraulazg. Ant algorithms and stigmergy [J], Future Generation Computer System,2000,16(6):851-871.
52.李士勇.蚁群优化算法及其应用研究进展[J],计算机测量与控制,2003,11(12)：911-917.
53.陈永强.人工蚁群算法及其在组合优化中的应用[D],哈尔滨：哈尔滨工业大学,2003.
54. Dorigom, Bonabeaue, Theraulazg. Ant algorithms for discrete optimization [J], Artificial Life, 1999,5(3):137-172.
55. Colornia, Dorigom, et al. Ant system for job-shop scheduling[J].Belgian Journal of Operations Research, Statistics and Computer Science (JORBEL),1994,34(1):39-53.
56. Bullnheimerb, et al. Applying the ant system to the vehicle routing problem [A].In OSMANIH, et al. Meta-Heuristics:Advances and Trends in Local Search Paradigms for Optimization[C]. Kluwer Academics,1998,109-120.
57.张素兵,刘泽民.基于蚂蚁算法的分级QoS路由调度方法[J],北京邮电大学学报,2000,23(4)：11-15.
58. Kennedy J, Eberhart R.C.Particle swarm optimization[C], IEEE International Conference on NN,1995,5:1942-1948.
59. Eberhart R.C, Kennedy J.A new optimizer using particle swarm theory[C],6th International Symposium on Micro Machine and Human Science, Nagoya,1995,39-43.
60. Eberhart R.C, Shi Yuhui.Particle swarm optimization:Developments, Applications and Resources [J].Proceedings of the 2001 Congress on Evolutionary Computation,2001,1:81-86.
61. Shi Yuhui, Eberhart, R. C.Parameter selection in particle swarm optimization[C],7th Annual Conferenceon Evolutionary Programming,1998,591-600.
62. Shi Yuhui, Eberhart, R. C.Experimental Study of Particle Swarm Optimization[C], Proceedings of SCI2000 Conference, Orlando, USA.
63.周驰,高海兵,高亮,章万国.粒子群优化算法[J],计算机应用研究,2003,5(12)：7-11.
64. Perelson A S. Immune Network Theory. Immunology Review [M],1989,10:5-36.
65. Jerne N K. The Immune System. Scientific American [J],1973,229(1):52-60.
66.焦李成,杜海峰.人工免疫系统进展与展望[J],电子学报,2003,31(10)：1540-1548.
67. Farmer J D, Parckard N H and Perelson A S. The immune system:adaptation and machine learning [J], Physic D.1986,22:187-204.
68. Dasgupta D. Artificial neural networks and artificial immune systems:similarities and differences. 1997 IEEE International Conference on Computational Cybernetics and Simulation [C], Institute of Electrical and Electronics Engineers, Incorporated.1997:8773-8781.
69.丁永生,任立红.人工免疫系统：理论与应用[J],模式识别与人工智能,2000,13(1)：52-59.
70. Ishida Y. Fully Distributed Diagnosis by PDP Learning Algorithm:Towards Immune Network PDP Model [J], In:Proceedings of International Joint Conference on Neural Networks,1990:777-782.
71.徐雪松.基于人工免疫系统的函数优化及其在复杂系统中的应用研究[D],浙江：浙江大学,2004.
72. D. Castro, V. Zuben. The Clonal Selection Algorithm with Engineering Applications [J], Proc. of GECC'00, Las Vegas, USA.2000,7:36-37.
73. D. Castro, V. Zuben. Learning and Optimization Using the Clonal Selection Principle [J], IEEE Transaction on Evolutionary Computation.2002,6(3):239-251.
74. S. T. Wierzchon. Function Optimization by the Immune Metaphor [J], TASK QUARTERLY.2002, 6(3):1-16.
75. A. M. Costa, P. A. Vargas. Makespan Minimization on Parallel Processors:An Immune-Based Approach[C], CEC'02, Proceedings of the 2002 Congress.2002,1:920-925.
76. D. Castro, J. Timmis. An Artificial Immune Network for Multimodal Function Optimization[C], Cec'02, Proc. of the 2002 Congress.2002,1:699-704.
77. A. C. C. Carlos, C. C. Nareli. An Approach to Solve Multiobjective Optimization Problems Based on Artificial Immune System [J],1 st International Conference on Artificial Immune Systems.2002: 212-221.
78. A. C. C. Carlos, C. C. Nareli. Solving Multiobjective Optimization Problems using an Artificial Immune System [M], Netherlands:Kluwer Academic Publishers.2002.
79. H. Bersini. The Immune and the Chemical Crossover [J], IEEE Transactions on Evolutionary Computation.2002,6(3):306-313.
80. H. F. Du, L. C. Jiao, S. A. Wang. Clonal operator and Antibody Clone Algorithms[C],1st International Conference on Machine Learning and Cybernetics,Beijing, China.2002,1:506-510.
81.葛红,毛宗源.免疫算法几个参数的研究[J],华南理工大学学报,2002,30(12)：15-18.
82.葛红,毛宗源.免疫算法的改进[J],计算机工程与应用,2002,14：47-50.
83. Adnan Acan. Clonal Selection Algorithm with Operator Multiplicity [J], Congress on Evolution Computation.2004,2:1909-1915.
84. Chunhua Li, Yanfei Zhu, Zongyuan Mao. A Novel Artificial Immune Algorithm Applied to Solve Optimization Problems [C],8th International Conference on Control, Automation, Robotics and Vision.2004(1):232-237.
85. YING YU, CHAO-ZHEN HOU. A CLONAL SELECTION ALGORITHM BY USING LEARNING OPERATOR[C],3rd International Conference on Machine Learning and Cybernetics. 2004(5):2924-2929.
86. Yinsheng Luo, Renhou Li, Feng Tian. Application of Artificial Immune Algorithm to Multimodal Function Optimization[C],5th World Congress on Intelligent Control and Automation.2004(3): 2248-2252.
87.郑日荣,毛宗源,罗欣贤.改进人工免疫算法的分析研究[J],计算机工程与应用,2003.34：35-37.
88.刘若辰,杜海峰,焦李成.一种免疫单克隆策略算法[J],电子学报,2004,32(11)：1880-1884.
89.刘若辰,杜海峰,焦李成.免疫多克隆策略[J],计算机研究与发展,2004,41(4)：571-576.
90.凌军等.基于小生境技术的多样性抗体生成算法[J],电子学报,2003,31(8)：1130-1133.
91.宋丹,傅明.基于多种群的自适应免疫进化计算[J],控制与决策,2005,20(11)：1251-1255.
92.李广强,赵洪伦,赵凤强,滕弘飞.人机合作的免疫算法及其在布局设计中的应用[J],计算机工程,2005,31(21)：4-7.
93. L. C. Jiao, L. Wang. A Novel Genetic Algorithm Based on Immunity [J], IEEE Tran.on System, Man, and Cybernetics.2000,30(5):552-561.
94.王磊,潘进,焦李成.免疫算法[J],电子学报,2000,28(7)：74-78.
95.王磊,潘进,焦李成.基于免疫策略的进化算法.自然科学进展[J],2000,10(5)：451-455.
96. X. Y. Sun, D. W. Gong. Research on a Modified Immunity Genetic Algorithm[C], Proc. of the 2002 International Conference on Control and Automation. Xiamen, China.2002:777-780.
97.王熙法,张显俊,曹先彬等.一种基于免疫原理的遗传算法[J],小型微型计算机系统,1999,20(2)：17-20.
98.曹先彬,刘克胜,王熙法.基于免疫遗传算法的装箱问题求解[J],小型微型计算机系统,2000,21(4)：361-363.
99.胡纯德,祝延军,高随祥.基于人工免疫算法和蚁群算法求解旅行商问题[J],计算机工程与应用,2004,4：60-63.
100.陈荣元,唐贤瑛,谭旭.基于免疫-蚂蚁算法的多约束QoS路由选择[J],通信学报,2004,25(8)：89-95.
101.高鹰,谢胜利.免疫粒子群优化算法[J],计算机工程与应用,2004,6：4-7.
102.杜海峰,公茂果,刘若辰,焦李成.自适应混沌克隆进化规划算法[J],中国科学E刊信息科学,2005,35(8)：817-829.
103.曹先彬,段建国.基于免疫选择和自组织临界变异的进化算法[J],系统仿真学报,2004,16(8)：1785-1788.
104.罗小平.人工免疫遗传学习算法及其工程应用研究[D],浙江：浙江大学,2002.7.1-29.
105.龚非力,医学免疫学[M],北京：科学出版社,2003,1-27.
106. Forrest S, Javornik B, Smith R, Perelson AS. Using genetic algorithms to explore pattern recognition in the immune system[J],Evolutionary Computation,1993,1(3):191-211.
107.孙宁,人工免疫优化算法及其应用研究[D],哈尔滨：哈尔滨工业大学,2006.17.1-29.
108. Colaco C. Acquired Wisdom in Innate Immunity [J], Immunology Today.1998,19 (1):50-52.
109. Medzhitov R, Janeway Jr C A. Innate Immunity:Impact on the Adaptive Immune Response [J], Current Opinion in Immunology.1997, (9):4-9.
110. Zinkernagel R M. Kelly J. How Antigen Influences Immunity [J], The Immunologist.1997, (4):114-120.
111. Sprent J. T and B Memory Cells [M], Cell.1994, (76):315-322.
112. Bere C, Ziegner M. The Maturation of the Immune Response [J], Immunology Today.1993,14 (8):400-402P.
113. Tarlinton D. Germinal Centers:Form and Function. Current Operation in Immunology [J],1998, (10):245-251.
114. Jerne NK. The immune system [J], Scientific American,1973,229(1):51-60.
115. George A J T, Gray D. Receptor editing during affinity maturation [J],Immune Today,1999,20(4): 196-200.
116. De Castro L N, Von Zuben F J. Artificial immune system:Part Ⅰ:basic theory and applications [J], Technical Report-RT DCA,1999,4(1):89.
117. Burnet F M. The Clonal Selection Theory of Acquired Immunity. Cambridge [M], UK:Cambridge University Press,1959.
118. De Castro L h, Von Zuben F J. The Clonal Selection Algorithm with Engineering Applications [J], Proc. of Artificial Immune System Workshop, Genetic and Evolutionary Computation Conference. San Francisco:Morgan Kaufmann,2000:36-37.
119.莫宏伟.人工免疫系统原理与应用[M],哈尔滨：哈尔滨工业大学出版社,2003.1-390.
120. Perelson A. Immune network theory [J], Immunological Review,1989,110:5-36.
121. De Boer R J, Segel L A & Perelson A S. Pattern Formation in One-and Two-dimensional Shape-Space Models of the Immune System [J], J. theor. Biol.1992,155:295-333.
122.王重庆.分子免疫学基础[M],北京：北京大学出版社,1999.
123. Tang Z, Yamaguchi T, Tashima K, et al. Multiple-Valued immune network modeland its simulation [J]. In:Proc 27th Int Symposium on multiple-valued logic, Autigonish, Canada.1997,233.
124. Herzenberg L A, Black S J. Regulatory circuits and antibody response [J], European J of Immiune.1980,10:1-11.
125.肖运本.免疫学基础及病原生物学[M],北京：中国医药科技出版社,1999.
126. KrishnaKuma K, Neidhoefer J. Immunized adaptive critics for level 2 intelligent control [J],1997 IEEE International Conference on Computational Cybernetics and Simulation. Institute of Electrical and Electronics Engineers, Incorporated,1997:856-861.
127. Sasaki M, Kawafuku M. Takahashi K. An immune feedback mechanism based adaptive learning of neural network controller [J], ICONIP'99.6th International Conference on Neural Information Processing. IEEE Computer Society Press,1999:502-507.
128.丁永生,任立红.一种新颖的模糊自调整免疫反馈控制系统[J],控制与决策,2000,15(4)：443-446.
129.丁永生,唐明浩.一种智能调节的免疫反馈控制系统[J],自动化仪表,2001,22(10)：5-7.
130.李海峰,王海风,陈珩.免疫系统建模及其在电力系统电压调节中的应用[J],电力系统自动化,2001(12)：17-23.
131.丁永生,任立红.人工免疫系统：理论与应用.模式识别与人工智能[J],2000.3. Vol.13. No.1.52-57.
132.肖人彬,王磊.人工免疫系统：原理、模型、分析及展望[J],计算机学报,2002.12. Vol.25. No.12.1281-1291.
133. Ishida Y, Adachi N. Active Noise Control by an Immune Algorithm [J], Adaptation in Immune System as an Evolution.Proc. ICEC96,1996,150-153.
134. Ishida Y. An Immune Network Model and Its Applications to Process Diagnosis[J], Systemsand Computers in Japan,1993,24(6):646-651.
135. Ishida Y, Mizzessyn F. Learning Algorithms on Immune Network Model[C], Application to Sensor Diagnosis. Proc, IJCNN92, Beijing,1992,33-38.
136. Chun J, Kim M, Jun H. Ahape Optimization of Electromagnetic Devices Using Immune Algorithm [J], IEEE Transactions on Magnetics,1997,33(2):232-240.
137. Chun J, Lim J, Jung H, Yoon J. Optimal Design of Synchronous with Parameter Correction Using Immune Algorithm [J], IEEE Transctions on Energy Conversion,1999(14),3:610-615.
138. Huang S. An Immune-based Optimization Method to Capacitor Placement in a Radial Distribution System [J], IEEE Transaction on Power,2000,3 (15):744-748.
139. Hong J, Lee W, Lee S, Lee B, Lee Y. An Efficient Production Algorithm for MultiHead Surface Mounting Machines Using the Biological Immune Algorithm [J], International Journal of Fuzzy Systems,2000,1 (2):45-53.
140.杨艳云.免疫算法在交流电机矢量控制中的应用研究[D],太原：太原理工大学,2006,4.
141. Hong J, Lee W, Lee S, Lee B, Lee Y. An Efficient Production Algorithm for MultiHead Surface Mounting Machines Using the Biological Immune Algorithm [J], International Journal of Fuzzy Systems,2000,1 (2):45-53.
142.黄席樾等.现代智能算法理论及应用[M],北京：科学出版社,2005.
143. Chun J, Lim J, Jung H, Yoon J. Optimal Design of Synchronous with Parameter Correction Using Immune Algorithm[J], IEEE Transctions on Energy Conversion,1999(14),3:610-615.
144. Huang S. An Immune-based Optimization Method to Capacitor Placement in a Radial Distribution System [J], IEEE Transaction on Power,2000,3 (15):744-748.
145. Forrest. S., Perelson. A. S., et.al. Self-nonself Discrimination in a Computer [J], In Proceedings of the IEEE Symposium on Research in Security and Privacy, Okaland, CA,1994 202-212.
146.缪红萍.免疫遗传算法及应用研究[D],北京：北京化工大学,2005,6.
147. Goldberg D E. Genetic Algorithms in Search, Optimization and Machine Learning, Reading [M], MA:Addison-Wesley,1989:110-123.
148. Farmer J. D. Packard N. H. and Perelson A. S. The immune system, adaptation and machine Learning [J], Physica D,1987,22:187-204.
149. Huang S. An Immune-based Optimization Method to Capacitor Placement in a Radial Distribution System [J], IEEE Transaction on Power,2000,3(15):744-748.
150.焦李成,杜海峰,刘芳等.免疫优化计算、学习与识别[M],北京：科学出版社,2006,6.
151. Leandro N De Castro, Fernando J Von Zuben. Learning and Optimization Using the Clonal Selection Principle [J],.IEEE Transactions on Evolutionary Computation, Special Issue on Aritifical Immune System,2002,6(3):239-251.
152. Bersini H. and Varela F. J. Hints for adaptive problem solving gleaned from immune networks [M], Proceedings of the first workshop on parallel problem solving from nature. Springer& Verlag,1990.
153.左兴权,李士勇.一类自适应免疫进化算法[J],控制与决策,2004,19(3)：252～256.
154.王小平,曹立明.遗传算法一理论、应用与软件实现[M],西安：西安交通大学出版社,2002.1.
155. Ishida Yoshiteru,Adachi Norihiko.Active noisecontrol by an immune algorithm:Adaptation in immune system as an evolution[A].Proc of the IEEE Conf on Evolutionary Computation [C],Piscataway:IEEE,1996,150-153.
156. Satoshi Endoh,Naruaki Toma,Koji Yamada.Immune algorithm for n-TSP[A].Proc of the IEEE Int Confon Systems,Man and Cybernetics[C],Pisca-taway:IEEE,1998,3844-3849.
157. Dasgupta D.An overview of artificial immune systems and their applications[A].Artificial Immune Systems and Their Applications,Ed[M],Dipankar asgupta,Pub.Springer-Verlag,1998,3-21.
158.莫宏伟,金鸿章.免疫算法原理与应用[J],航空计算技术,2002,32(4)：49～51.
159. N De Castro, Jonathan Timmis. Artificial immune system:a new computational intelligence approach [M], British:Springer Press,2002:77.
160. Cooper K D, Hall M W, Kennedy K. Procedure cloning [J], Proceedings of the International Conference on Computer Languages 1992:96-105.
161. Balazinska M, Merlo E, Dagenais M, etl. Advanced clone-analysis to support object-oriented system refactoring. Proceedings [J], Seventh Working Conference on Reverse Engineering,2000:98-107.
162.林学颜,张玲.现代细胞与分子免疫学[M],北京：科学出版社,1999.
163.陆德源,马宝骊.现代免疫学[M],上海：上海科技教育出版社,1998.
164. T. B. Kepler, A. S. Perelson. Somatic Hypermutation in B Cells:an Optimal Control Treatment [J], J.theror. Biol.,1993,164:37-64.
165. Calada, F. and Seiden, P. E. Affinity Maturation and Hypermutation in a Simulation of the Humoral Immune Response [J],Eur. J. Immunol.1996,26:1350-1358.
166.周明,孙树栋.遗传算法原理及应用[M],北京：国防工业出版社,2002.
167.宋毅.基于遗传算法的生产调度方法及其软件实现[D],杭州：浙江工业大学,2003.
168.王万良,吴启迪.生产调度智能算法及其应用[M],北京：科学出版社,2007.
169.周远晖,陆玉昌,石纯一.基于克服过早收敛的自适应并行遗传算法[J],清华大学学报(自然科学版),1998,38(3)：93-95.
170.邹琳,夏巨谌,胡国安.基于实数编码的多种群并行遗传算法研究[J],小型微型计算机系统,2004,25(6)：982-986.
171.Bessaou M, Siarry P. A new tool in electrostatics using a really-coded multi-population geneticalgorithm tuned through analytical test problems [J], Advances in Engineering Software, 2001,32(5):363-374.
172.司书宾,孙树栋,徐娅萍.求解Job-shop调度问题的多种群双倍体免疫算法研究[J],西北工业大学学报,2007,25(1)：27-30.
173.张建,马光文,杨东方,等.双倍体遗传算法求解龙溪河级电站长期优化调度问题[J],四川水利,2002,12(5)：33-35.
174.余建军,孙树栋,牛刚刚,等.面向Job-shop调度的改进免疫算法研究[J],工业工程,2007,10(2)： 93-96.
175. Xiaoyao Zhou, Haozhong Cheng. The induction motor parameter estimation through an adaptive genetic algorithm [J], Universities Power Engineering Conference.2004,1:494-498.
176. HazemM.Abbas, Mohamed M.Bayoumi. Volterra system identification using adaptive real-code genetic algorithms [J], Applied Soft Computing.2004,5:75-86.
177.欧阳森,王建华,耿英三等.一种新的改进遗传算法[J],计算机工程与应用,2003,39(11)：13-15.
178. Jackie Rees, Gary J.Koehler.Learning genetic algorithm parameters using hidden Markov models [J], European Journal of Operational Research.2006,175::806-820.
179.金晶,苏勇.一种改进的自适应遗传算法.计算机工程与应用[J],2005,41(18)：64-69.
180. SRINIVAS M, PATNAILK L M. Adaptive probabilities of crossover and mutation in genetic algorithms [J], IEEE Transaction on System, Man and Cybernetics.1994,24(4):656-667.
181. M Srinivas, L M Patnaik. Adaptive probabilities of crossover and mutation in genetic algorithms [J], IEEE Transactions on Systems, Man and Cybernetics,1994; 24(4):656-665.
182. Dasgupta D. Artificial Immune Systems and Their Applications [M], Berlin Heidelberg: Springer-Verlang,1999.
183. Ho Nhu Binh and Tay Joe Cing. GENACE:An efficient cultural algorithm for solving the flexible job-shop problem[C], In Proceedings of the IEEE Congress on Evolutionary Computation 2004, 1759-1766.
184. Jiao Licheng, WangLei. A novel genetic algorithm based on immunity [J],IEEE Transactions on Systems, Man, and Cybernetics,2000,30 (5):552-561.
185. Huang S. Enhancement of thermal unit commitment using immune algorithms based optimization approaches [J],Electrical Power and Energy System,1999,21:245-252.
186. Hunt J E. Cooke D E. Learning using an artificial immune system [J], Journal of Network and Computer Application,1996,9(4):189-212.
187. D. Dasgupta. Artificial neural networks and artificial immune systems:Similarities and differences. Systems [J], Man, and Cybernetics.1997,1:873-878.
188. E.D. Talliard. Parallel Iterative Search Methods for Vehicle Routing Problems [J], Networks 1993, 23,661-673.
189.郎茂样.物流配送车辆调度问题的模型和算法研究[D],北京：北方交通大学,2001.
190.陈火根,丁红钢,程耀东.物流配送中心车辆调度模型与遗传算法设计[J],浙江大学学报,2003,37(5)：512-516.
191.吴璟莉,刘仁辉.分批配送的有时间窗车辆路径问题的遗传算法[J],计算机工程,2006,32(8)：213-215.
192.谢秉磊,郭耀煌,郭强.动态车辆路径问题现状与展望[J],系统工程理论方法应用,2002,11(2)：116-120.
193.郎茂祥.基于遗传算法的物流配送路径优化问题研究[J],中国公路学报,2002,15(3)：76-79.
194. Bahl H C, Ritzman L P, Gupta J N D. Detemining lot-sizing and resource requirements review [J],Operational Research,1987,35 (3):329-345.
195. Tang O, Manne A S. Simulated annealing in lot sizing problems[J],Int J. Production Economics,2004(88):173-181.
196. M. Fatih Tasgetiren, Liang Y C. A binary particle swarm optimization algorithm for lot Sizing problem[J] Journal of Economic and Social Research, Vol.5, No.2, Jun.2003, pp.1-20.
197.唐立新,杨自厚.CIMS中带多资源的CLSP问题的遗传算法[J],系统工程理论与实践,1997,4(4)39-44.
198. Nembauser, GL.andWolse, L.A., Integer and Combinatorial Optimization, Wiley-Interscience Publication [M], Joh n Wzley&Sony Inc.
199.许志兴,丁运亮,熊仲宇,陆金桂.基于退火惩罚混合遗传算法求解生产批量计划问题[J],南京航空航天大学学报,2001,33(2)：91-95.
200. Maes J, McClain J O, Thomas L J. Multilevel capacitated lot-sizing complexity and LP-based heuristics[J],European Journal of Operation Research,1991,53:131-148.
201. Stadtier H. Mixed integer programming model formulations for dynamic multi-level capacitated lot-sizing [J], European Journal of Operation Research,1996,94:561-581.
202. Wolsey L A. Progress with single-item lot sizing [J], European Journal of Operational Research, 1995,86:395-401.
203. J. Carlier, E. Pinson. An algorithm for solving the job-shop problem [J], Management Science,1989, 35(2):164-176.
204. Hurink E., Jurisch B., Thole M. Tabu search for the job shop scheduling problem with multi-purpose machines [J], Operations Research Spektrum,1994,15:205-215.
205. Kacem I., Hammadi S., Borne P. Pareto-optimality approach for flexible job-shop scheduling problems:Hybridization of evolutionary algorithms and fuzzy logic [J], Mathematics and Computers in Simulation,2002,60:245-276.
206.张超勇,饶运清,李培根等.柔性作业车间调度问题的两级遗传算法[J],机械工程学报,2007,43(4)：119-124.
207. Holsapple W, Jacob V S, Pakath R, et al. A genetics-based hybrid scheduler for generating static schedules in flexible manufacturing contexts [J], IEEE Transaction on Systems. Man and Cybernetics,1993,23 (4):953-972.
208.熊锐,吴澄.车间生产调度问题的技术现状与发展趋势[J],清华大学学报.1998,38(10)：55-60.
209. Bruker P, Schlie R, Job-shop scheduling with multi-purpose machines [J],Computing,1990 45 (4): 369-375.
210. Brandimarte P. Routing and scheduling in a flexible job shop by taboo search [J], Annals of Operations Research.1993,22(2):157-183.
211. Mati Y, Rezg N, Xie X L. An integrated greedy heuristic for a flexible job shop scheduling problem [J]. The Proceedings of IEEE International Conference on Systems, Man, and Cybernetics. Piscataway, NJ, USA,2001.2534-2539.
212. Hapke M.Pareto simulated annealing for fuzzy multi-objective combinatorial optimization [J], Journal of Heuristics,2000,6(3):329-345.
213. Rigao C. Tardiness minimization in a flexible job shop:a tabu search approach [J], Journal of Intelligent Manufacturing,2004,15(1):103-115.
214. Mastrolilli M, Gambardella L M. Effective neighborhood functions for the flexible job shop problem [J], Journal of scheduling,2002,3(1):3-20.
215.翁妙凤,李晓敏.基于模拟退火多种群并行进化规划的柔性调度方法[J],华东船舶工业学院学报,2002,16(2)：27-31.
216.张维存,郑王谬,吴晓丹.基于主一从遗传算法求解柔性调度问题[J],计算机集成制造系统,2006,12(8)：1241-1245.
217.张超勇,饶运情,李培根,等.求解作业车间调度问题的一种改进遗传算法[J],计算机集成制造系统,2004,10(8)：966-970.
218.顾绍元,王小平,曹立明.基于遗传算法的柔性调度系统模型及其应用[J],同济大学学报,2002,30(2)：226-231.
219.王凌.车间调度及其遗传算法[M],北京：清华大学出版社,2003,5.
220. Panwalkar S.A., survey of scheduling rules. Operation Research [J],1977,25(1):45-61.
221.方剑.进化算法及其在Job Shop调度中的应用[D],上海：上海交通大学,1996.
222.陈萍,郭金锋.对Hopfield神经网络求解TSP的研究[J],北京邮电大学学报,1999,22(2)：58-61.
223.谢胜利,董金祥,黄强.基于遗传算法的车间作业调度问题求解[J],计算机工程与应用,2002,10：79-82.
224.耿兆强,邹益仁.基于遗传算法的作业车间模糊调度问题的研究[J],计算机集成制造系统,2002,8(8)-616-620.
225.何霆,梁办平.一种新的FMS混合调度算法[J],控制理论与应用,2000,17(6)：894-898.
226.张超勇,饶运情,李培根,等.柔性作业车间调度问题的两极遗传算法[J],机械工程学报,2007,43(4)：119-124.
227.杨晓梅,曾建潮.遗传算法求解柔性job shop调度问题[J],控制于决策,2004,19(10)：1197-1200.
228.刘乐,王艳秋,陈秋艳.人工免疫系统的研究与应用[J],辽宁工学院学报,2006,5(26)：286-290.
229. Najid N. M., Stephane D. P., Zaidat A. Modified simulated annealing method for flexible job shop scheduling problem[C], Proceedings of the IEEE International Conference on Systems Man and Cybernetics, NJ, USA:IEEE,2002.89-94.
230. P. Brandimarte. Routing and scheduling in a flexible job shop by tabu search [J], Annals of Operations Research,1993,22,158-183.
231. Ho Nhu Binh and Tay Joc Cing. GENACE:An efficient cultural algorithm for solving the flexible job-shop problem[C], In Proceedings of the IEEE Congress on Evolutionary Computation 2004, 1759-1766.
© 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 |